From 6210a06d43aac6874f9f476ff1275f9ca1633e15 Mon Sep 17 00:00:00 2001 From: Liujian <824010343@qq.com> Date: Thu, 13 Mar 2025 14:16:03 +0800 Subject: [PATCH] fix local model bug --- ai-provider/local/models.json | 416 +++++++++++++++++----------------- module/ai-local/iml.go | 4 +- module/ai/iml.go | 1 - service/ai-local/iml.go | 2 +- stores/ai/model.go | 4 +- 5 files changed, 213 insertions(+), 214 deletions(-) diff --git a/ai-provider/local/models.json b/ai-provider/local/models.json index 74fea8d1..42bc9927 100644 --- a/ai-provider/local/models.json +++ b/ai-provider/local/models.json @@ -10703,7 +10703,7 @@ { "id": "llava", "name": "llava", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "4.7GB", "digest": "8dd30f6b0cb1", "provider": "", @@ -10713,7 +10713,7 @@ { "id": "llava:7b", "name": "llava:7b", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "4.7GB", "digest": "8dd30f6b0cb1", "provider": "", @@ -10723,7 +10723,7 @@ { "id": "llava:13b", "name": "llava:13b", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "8.0GB", "digest": "0d0eb4d7f485", "provider": "", @@ -10733,7 +10733,7 @@ { "id": "llava:34b", "name": "llava:34b", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "20GB", "digest": "3d2d24f46674", "provider": "", @@ -10743,7 +10743,7 @@ { "id": "llava:13b-v1.5-fp16", "name": "llava:13b-v1.5-fp16", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "27GB", "digest": "ce3bde71eaa7", "provider": "", @@ -10753,7 +10753,7 @@ { "id": "llava:13b-v1.5-q2_K", "name": "llava:13b-v1.5-q2_K", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "6.1GB", "digest": "3cdd3869f154", "provider": "", @@ -10763,7 +10763,7 @@ { "id": "llava:13b-v1.5-q3_K_L", "name": "llava:13b-v1.5-q3_K_L", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "7.6GB", "digest": "07b1b74ec398", "provider": "", @@ -10773,7 +10773,7 @@ { "id": "llava:13b-v1.5-q3_K_M", "name": "llava:13b-v1.5-q3_K_M", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "7.0GB", "digest": "61441e82808c", "provider": "", @@ -10783,7 +10783,7 @@ { "id": "llava:13b-v1.5-q3_K_S", "name": "llava:13b-v1.5-q3_K_S", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "6.3GB", "digest": "c860f869008d", "provider": "", @@ -10793,7 +10793,7 @@ { "id": "llava:13b-v1.5-q4_0", "name": "llava:13b-v1.5-q4_0", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "8.0GB", "digest": "e3b7997801dc", "provider": "", @@ -10803,7 +10803,7 @@ { "id": "llava:13b-v1.5-q4_1", "name": "llava:13b-v1.5-q4_1", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "8.8GB", "digest": "0ca30ff66062", "provider": "", @@ -10813,7 +10813,7 @@ { "id": "llava:13b-v1.5-q4_K_M", "name": "llava:13b-v1.5-q4_K_M", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "8.5GB", "digest": "d0a6a3f0e6c4", "provider": "", @@ -10823,7 +10823,7 @@ { "id": "llava:13b-v1.5-q4_K_S", "name": "llava:13b-v1.5-q4_K_S", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "8.1GB", "digest": "16939c152bd9", "provider": "", @@ -10833,7 +10833,7 @@ { "id": "llava:13b-v1.5-q5_0", "name": "llava:13b-v1.5-q5_0", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "9.6GB", "digest": "78e17026912a", "provider": "", @@ -10843,7 +10843,7 @@ { "id": "llava:13b-v1.5-q5_1", "name": "llava:13b-v1.5-q5_1", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "10GB", "digest": "b11f33bc65fa", "provider": "", @@ -10853,7 +10853,7 @@ { "id": "llava:13b-v1.5-q5_K_M", "name": "llava:13b-v1.5-q5_K_M", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "9.9GB", "digest": "48418b116e18", "provider": "", @@ -10863,7 +10863,7 @@ { "id": "llava:13b-v1.5-q5_K_S", "name": "llava:13b-v1.5-q5_K_S", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "9.6GB", "digest": "c7f4b8076a0e", "provider": "", @@ -10873,7 +10873,7 @@ { "id": "llava:13b-v1.5-q6_K", "name": "llava:13b-v1.5-q6_K", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "11GB", "digest": "3c085b55f924", "provider": "", @@ -10883,7 +10883,7 @@ { "id": "llava:13b-v1.5-q8_0", "name": "llava:13b-v1.5-q8_0", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "14GB", "digest": "4e00c435bb25", "provider": "", @@ -10893,7 +10893,7 @@ { "id": "llava:13b-v1.6", "name": "llava:13b-v1.6", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "8.0GB", "digest": "0d0eb4d7f485", "provider": "", @@ -10903,7 +10903,7 @@ { "id": "llava:13b-v1.6-vicuna-fp16", "name": "llava:13b-v1.6-vicuna-fp16", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "27GB", "digest": "81e28406a4e9", "provider": "", @@ -10913,7 +10913,7 @@ { "id": "llava:13b-v1.6-vicuna-q2_K", "name": "llava:13b-v1.6-vicuna-q2_K", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "5.5GB", "digest": "87aecb135e2a", "provider": "", @@ -10923,7 +10923,7 @@ { "id": "llava:13b-v1.6-vicuna-q3_K_L", "name": "llava:13b-v1.6-vicuna-q3_K_L", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "7.6GB", "digest": "a2b56a2b8e79", "provider": "", @@ -10933,7 +10933,7 @@ { "id": "llava:13b-v1.6-vicuna-q3_K_M", "name": "llava:13b-v1.6-vicuna-q3_K_M", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "7.0GB", "digest": "2e48f094b51a", "provider": "", @@ -10943,7 +10943,7 @@ { "id": "llava:13b-v1.6-vicuna-q3_K_S", "name": "llava:13b-v1.6-vicuna-q3_K_S", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "6.3GB", "digest": "54ef03322645", "provider": "", @@ -10953,7 +10953,7 @@ { "id": "llava:13b-v1.6-vicuna-q4_0", "name": "llava:13b-v1.6-vicuna-q4_0", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "8.0GB", "digest": "0d0eb4d7f485", "provider": "", @@ -10963,7 +10963,7 @@ { "id": "llava:13b-v1.6-vicuna-q4_1", "name": "llava:13b-v1.6-vicuna-q4_1", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "8.8GB", "digest": "0b97528b26b0", "provider": "", @@ -10973,7 +10973,7 @@ { "id": "llava:13b-v1.6-vicuna-q4_K_M", "name": "llava:13b-v1.6-vicuna-q4_K_M", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "8.5GB", "digest": "0843119c3874", "provider": "", @@ -10983,7 +10983,7 @@ { "id": "llava:13b-v1.6-vicuna-q4_K_S", "name": "llava:13b-v1.6-vicuna-q4_K_S", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "8.1GB", "digest": "5e4cf96dfc4c", "provider": "", @@ -10993,7 +10993,7 @@ { "id": "llava:13b-v1.6-vicuna-q5_0", "name": "llava:13b-v1.6-vicuna-q5_0", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "9.6GB", "digest": "6e06e1393058", "provider": "", @@ -11003,7 +11003,7 @@ { "id": "llava:13b-v1.6-vicuna-q5_1", "name": "llava:13b-v1.6-vicuna-q5_1", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "10GB", "digest": "31f1d78cb272", "provider": "", @@ -11013,7 +11013,7 @@ { "id": "llava:13b-v1.6-vicuna-q5_K_M", "name": "llava:13b-v1.6-vicuna-q5_K_M", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "9.9GB", "digest": "446b10458a6a", "provider": "", @@ -11023,7 +11023,7 @@ { "id": "llava:13b-v1.6-vicuna-q5_K_S", "name": "llava:13b-v1.6-vicuna-q5_K_S", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "9.6GB", "digest": "53999e2c86c8", "provider": "", @@ -11033,7 +11033,7 @@ { "id": "llava:13b-v1.6-vicuna-q6_K", "name": "llava:13b-v1.6-vicuna-q6_K", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "11GB", "digest": "1c0a91e1e4d9", "provider": "", @@ -11043,7 +11043,7 @@ { "id": "llava:13b-v1.6-vicuna-q8_0", "name": "llava:13b-v1.6-vicuna-q8_0", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "14GB", "digest": "b25c4195f9e2", "provider": "", @@ -11053,7 +11053,7 @@ { "id": "llava:34b-v1.6", "name": "llava:34b-v1.6", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "20GB", "digest": "3d2d24f46674", "provider": "", @@ -11063,7 +11063,7 @@ { "id": "llava:34b-v1.6-fp16", "name": "llava:34b-v1.6-fp16", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "69GB", "digest": "89c50af82d5e", "provider": "", @@ -11073,7 +11073,7 @@ { "id": "llava:34b-v1.6-q2_K", "name": "llava:34b-v1.6-q2_K", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "14GB", "digest": "6326f59da4f1", "provider": "", @@ -11083,7 +11083,7 @@ { "id": "llava:34b-v1.6-q3_K_L", "name": "llava:34b-v1.6-q3_K_L", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "19GB", "digest": "7f9889648d1a", "provider": "", @@ -11093,7 +11093,7 @@ { "id": "llava:34b-v1.6-q3_K_M", "name": "llava:34b-v1.6-q3_K_M", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "17GB", "digest": "89e924fed7d4", "provider": "", @@ -11103,7 +11103,7 @@ { "id": "llava:34b-v1.6-q3_K_S", "name": "llava:34b-v1.6-q3_K_S", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "16GB", "digest": "a0376a205682", "provider": "", @@ -11113,7 +11113,7 @@ { "id": "llava:34b-v1.6-q4_0", "name": "llava:34b-v1.6-q4_0", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "20GB", "digest": "3d2d24f46674", "provider": "", @@ -11123,7 +11123,7 @@ { "id": "llava:34b-v1.6-q4_1", "name": "llava:34b-v1.6-q4_1", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "22GB", "digest": "96d20de28a1a", "provider": "", @@ -11133,7 +11133,7 @@ { "id": "llava:34b-v1.6-q4_K_M", "name": "llava:34b-v1.6-q4_K_M", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "21GB", "digest": "538ff4c5a8b8", "provider": "", @@ -11143,7 +11143,7 @@ { "id": "llava:34b-v1.6-q4_K_S", "name": "llava:34b-v1.6-q4_K_S", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "20GB", "digest": "787b2213f0db", "provider": "", @@ -11153,7 +11153,7 @@ { "id": "llava:34b-v1.6-q5_0", "name": "llava:34b-v1.6-q5_0", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "24GB", "digest": "b239e218bdf0", "provider": "", @@ -11163,7 +11163,7 @@ { "id": "llava:34b-v1.6-q5_1", "name": "llava:34b-v1.6-q5_1", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "27GB", "digest": "60926fd725ec", "provider": "", @@ -11173,7 +11173,7 @@ { "id": "llava:34b-v1.6-q5_K_M", "name": "llava:34b-v1.6-q5_K_M", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "25GB", "digest": "0eb2ab10d35c", "provider": "", @@ -11183,7 +11183,7 @@ { "id": "llava:34b-v1.6-q5_K_S", "name": "llava:34b-v1.6-q5_K_S", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "24GB", "digest": "cdd8d5db3870", "provider": "", @@ -11193,7 +11193,7 @@ { "id": "llava:34b-v1.6-q6_K", "name": "llava:34b-v1.6-q6_K", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "29GB", "digest": "8f572ea02185", "provider": "", @@ -11203,7 +11203,7 @@ { "id": "llava:34b-v1.6-q8_0", "name": "llava:34b-v1.6-q8_0", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "37GB", "digest": "959065f30849", "provider": "", @@ -11213,7 +11213,7 @@ { "id": "llava:7b-v1.5-fp16", "name": "llava:7b-v1.5-fp16", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "14GB", "digest": "337a5b25bada", "provider": "", @@ -11223,7 +11223,7 @@ { "id": "llava:7b-v1.5-q2_K", "name": "llava:7b-v1.5-q2_K", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "3.5GB", "digest": "33973d2589d1", "provider": "", @@ -11233,7 +11233,7 @@ { "id": "llava:7b-v1.5-q3_K_L", "name": "llava:7b-v1.5-q3_K_L", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "4.2GB", "digest": "057fefd59cdb", "provider": "", @@ -11243,7 +11243,7 @@ { "id": "llava:7b-v1.5-q3_K_M", "name": "llava:7b-v1.5-q3_K_M", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "3.9GB", "digest": "9d738df24288", "provider": "", @@ -11253,7 +11253,7 @@ { "id": "llava:7b-v1.5-q3_K_S", "name": "llava:7b-v1.5-q3_K_S", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "3.6GB", "digest": "605b68e0b568", "provider": "", @@ -11263,7 +11263,7 @@ { "id": "llava:7b-v1.5-q4_0", "name": "llava:7b-v1.5-q4_0", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "4.5GB", "digest": "cd3274b81a85", "provider": "", @@ -11273,7 +11273,7 @@ { "id": "llava:7b-v1.5-q4_1", "name": "llava:7b-v1.5-q4_1", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "4.9GB", "digest": "146a55d9df75", "provider": "", @@ -11283,7 +11283,7 @@ { "id": "llava:7b-v1.5-q4_K_M", "name": "llava:7b-v1.5-q4_K_M", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "4.7GB", "digest": "75a9333e75cd", "provider": "", @@ -11293,7 +11293,7 @@ { "id": "llava:7b-v1.5-q4_K_S", "name": "llava:7b-v1.5-q4_K_S", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "4.5GB", "digest": "d8d545afa5f0", "provider": "", @@ -11303,7 +11303,7 @@ { "id": "llava:7b-v1.5-q5_0", "name": "llava:7b-v1.5-q5_0", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "5.3GB", "digest": "339249626980", "provider": "", @@ -11313,7 +11313,7 @@ { "id": "llava:7b-v1.5-q5_1", "name": "llava:7b-v1.5-q5_1", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "5.7GB", "digest": "6d97e8715c53", "provider": "", @@ -11323,7 +11323,7 @@ { "id": "llava:7b-v1.5-q5_K_M", "name": "llava:7b-v1.5-q5_K_M", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "5.4GB", "digest": "4fb097b9cfa3", "provider": "", @@ -11333,7 +11333,7 @@ { "id": "llava:7b-v1.5-q5_K_S", "name": "llava:7b-v1.5-q5_K_S", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "5.3GB", "digest": "34f9b24f2315", "provider": "", @@ -11343,7 +11343,7 @@ { "id": "llava:7b-v1.5-q6_K", "name": "llava:7b-v1.5-q6_K", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "6.2GB", "digest": "df0203e92f79", "provider": "", @@ -11353,7 +11353,7 @@ { "id": "llava:7b-v1.5-q8_0", "name": "llava:7b-v1.5-q8_0", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "7.8GB", "digest": "c684b68b3f34", "provider": "", @@ -11363,7 +11363,7 @@ { "id": "llava:7b-v1.6", "name": "llava:7b-v1.6", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "4.7GB", "digest": "8dd30f6b0cb1", "provider": "", @@ -11373,7 +11373,7 @@ { "id": "llava:7b-v1.6-mistral-fp16", "name": "llava:7b-v1.6-mistral-fp16", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "15GB", "digest": "9fd1e5417c5f", "provider": "", @@ -11383,7 +11383,7 @@ { "id": "llava:7b-v1.6-mistral-q2_K", "name": "llava:7b-v1.6-mistral-q2_K", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "3.3GB", "digest": "52e0ce44a5f5", "provider": "", @@ -11393,7 +11393,7 @@ { "id": "llava:7b-v1.6-mistral-q3_K_L", "name": "llava:7b-v1.6-mistral-q3_K_L", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "4.4GB", "digest": "a48bbc9b567b", "provider": "", @@ -11403,7 +11403,7 @@ { "id": "llava:7b-v1.6-mistral-q3_K_M", "name": "llava:7b-v1.6-mistral-q3_K_M", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "4.1GB", "digest": "25a00600f8b4", "provider": "", @@ -11413,7 +11413,7 @@ { "id": "llava:7b-v1.6-mistral-q3_K_S", "name": "llava:7b-v1.6-mistral-q3_K_S", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "3.8GB", "digest": "2b9c055fe6a2", "provider": "", @@ -11423,7 +11423,7 @@ { "id": "llava:7b-v1.6-mistral-q4_0", "name": "llava:7b-v1.6-mistral-q4_0", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "4.7GB", "digest": "8dd30f6b0cb1", "provider": "", @@ -11433,7 +11433,7 @@ { "id": "llava:7b-v1.6-mistral-q4_1", "name": "llava:7b-v1.6-mistral-q4_1", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "5.2GB", "digest": "8da3213068e6", "provider": "", @@ -11443,7 +11443,7 @@ { "id": "llava:7b-v1.6-mistral-q4_K_M", "name": "llava:7b-v1.6-mistral-q4_K_M", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "5.0GB", "digest": "8d3fbd6ad3f4", "provider": "", @@ -11453,7 +11453,7 @@ { "id": "llava:7b-v1.6-mistral-q4_K_S", "name": "llava:7b-v1.6-mistral-q4_K_S", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "4.8GB", "digest": "2878e8c79f6e", "provider": "", @@ -11463,7 +11463,7 @@ { "id": "llava:7b-v1.6-mistral-q5_0", "name": "llava:7b-v1.6-mistral-q5_0", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "5.6GB", "digest": "b8f63553f521", "provider": "", @@ -11473,7 +11473,7 @@ { "id": "llava:7b-v1.6-mistral-q5_1", "name": "llava:7b-v1.6-mistral-q5_1", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "6.1GB", "digest": "eda0b3f3b09b", "provider": "", @@ -11483,7 +11483,7 @@ { "id": "llava:7b-v1.6-mistral-q5_K_M", "name": "llava:7b-v1.6-mistral-q5_K_M", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "5.8GB", "digest": "244b7e3d3d5a", "provider": "", @@ -11493,7 +11493,7 @@ { "id": "llava:7b-v1.6-mistral-q5_K_S", "name": "llava:7b-v1.6-mistral-q5_K_S", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "5.6GB", "digest": "62dc434a7ae8", "provider": "", @@ -11503,7 +11503,7 @@ { "id": "llava:7b-v1.6-mistral-q6_K", "name": "llava:7b-v1.6-mistral-q6_K", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "6.6GB", "digest": "8781169d7f8f", "provider": "", @@ -11513,7 +11513,7 @@ { "id": "llava:7b-v1.6-mistral-q8_0", "name": "llava:7b-v1.6-mistral-q8_0", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "8.3GB", "digest": "c2973e390e84", "provider": "", @@ -11523,7 +11523,7 @@ { "id": "llava:7b-v1.6-vicuna-fp16", "name": "llava:7b-v1.6-vicuna-fp16", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "14GB", "digest": "bb8da134bacb", "provider": "", @@ -11533,7 +11533,7 @@ { "id": "llava:7b-v1.6-vicuna-q2_K", "name": "llava:7b-v1.6-vicuna-q2_K", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "3.2GB", "digest": "6321163f3833", "provider": "", @@ -11543,7 +11543,7 @@ { "id": "llava:7b-v1.6-vicuna-q3_K_L", "name": "llava:7b-v1.6-vicuna-q3_K_L", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "4.2GB", "digest": "0127d9087e07", "provider": "", @@ -11553,7 +11553,7 @@ { "id": "llava:7b-v1.6-vicuna-q3_K_M", "name": "llava:7b-v1.6-vicuna-q3_K_M", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "3.9GB", "digest": "7004e1f24eb1", "provider": "", @@ -11563,7 +11563,7 @@ { "id": "llava:7b-v1.6-vicuna-q3_K_S", "name": "llava:7b-v1.6-vicuna-q3_K_S", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "3.6GB", "digest": "7701df672950", "provider": "", @@ -11573,7 +11573,7 @@ { "id": "llava:7b-v1.6-vicuna-q4_0", "name": "llava:7b-v1.6-vicuna-q4_0", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "4.5GB", "digest": "b6cbe07f1d5e", "provider": "", @@ -11583,7 +11583,7 @@ { "id": "llava:7b-v1.6-vicuna-q4_1", "name": "llava:7b-v1.6-vicuna-q4_1", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "4.9GB", "digest": "f8a27e237e97", "provider": "", @@ -11593,7 +11593,7 @@ { "id": "llava:7b-v1.6-vicuna-q4_K_M", "name": "llava:7b-v1.6-vicuna-q4_K_M", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "4.7GB", "digest": "15360a9e0fb9", "provider": "", @@ -11603,7 +11603,7 @@ { "id": "llava:7b-v1.6-vicuna-q4_K_S", "name": "llava:7b-v1.6-vicuna-q4_K_S", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "4.5GB", "digest": "5006a8a41d2b", "provider": "", @@ -11613,7 +11613,7 @@ { "id": "llava:7b-v1.6-vicuna-q5_0", "name": "llava:7b-v1.6-vicuna-q5_0", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "5.3GB", "digest": "6a2bb61a611a", "provider": "", @@ -11623,7 +11623,7 @@ { "id": "llava:7b-v1.6-vicuna-q5_1", "name": "llava:7b-v1.6-vicuna-q5_1", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "5.7GB", "digest": "1bd37032ec33", "provider": "", @@ -11633,7 +11633,7 @@ { "id": "llava:7b-v1.6-vicuna-q5_K_M", "name": "llava:7b-v1.6-vicuna-q5_K_M", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "5.4GB", "digest": "b22b0c041223", "provider": "", @@ -11643,7 +11643,7 @@ { "id": "llava:7b-v1.6-vicuna-q5_K_S", "name": "llava:7b-v1.6-vicuna-q5_K_S", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "5.3GB", "digest": "4aaa19502e34", "provider": "", @@ -11653,7 +11653,7 @@ { "id": "llava:7b-v1.6-vicuna-q6_K", "name": "llava:7b-v1.6-vicuna-q6_K", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "6.2GB", "digest": "11bd55683f9c", "provider": "", @@ -11663,7 +11663,7 @@ { "id": "llava:7b-v1.6-vicuna-q8_0", "name": "llava:7b-v1.6-vicuna-q8_0", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "7.8GB", "digest": "6da20a71d9bb", "provider": "", @@ -11673,7 +11673,7 @@ { "id": "llava:v1.6", "name": "llava:v1.6", - "description": "πŸŒ‹ LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", + "description": "LLaVA is a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. Updated to version 1.6.", "size": "4.7GB", "digest": "8dd30f6b0cb1", "provider": "", @@ -35753,7 +35753,7 @@ { "id": "smollm", "name": "smollm", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "991MB", "digest": "95f6557a0f0f", "provider": "", @@ -35763,7 +35763,7 @@ { "id": "smollm:135m", "name": "smollm:135m", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "92MB", "digest": "b0b2a4617438", "provider": "", @@ -35773,7 +35773,7 @@ { "id": "smollm:360m", "name": "smollm:360m", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "229MB", "digest": "b3ba1ccba2b8", "provider": "", @@ -35783,7 +35783,7 @@ { "id": "smollm:1.7b", "name": "smollm:1.7b", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "991MB", "digest": "95f6557a0f0f", "provider": "", @@ -35793,7 +35793,7 @@ { "id": "smollm:1.7b-base-v0.2-fp16", "name": "smollm:1.7b-base-v0.2-fp16", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "3.4GB", "digest": "64f31ee84d1c", "provider": "", @@ -35803,7 +35803,7 @@ { "id": "smollm:1.7b-base-v0.2-q2_K", "name": "smollm:1.7b-base-v0.2-q2_K", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "675MB", "digest": "b013da007877", "provider": "", @@ -35813,7 +35813,7 @@ { "id": "smollm:1.7b-base-v0.2-q3_K_L", "name": "smollm:1.7b-base-v0.2-q3_K_L", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "933MB", "digest": "9656463272fe", "provider": "", @@ -35823,7 +35823,7 @@ { "id": "smollm:1.7b-base-v0.2-q3_K_M", "name": "smollm:1.7b-base-v0.2-q3_K_M", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "860MB", "digest": "9acc25818892", "provider": "", @@ -35833,7 +35833,7 @@ { "id": "smollm:1.7b-base-v0.2-q3_K_S", "name": "smollm:1.7b-base-v0.2-q3_K_S", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "777MB", "digest": "596523cb1def", "provider": "", @@ -35843,7 +35843,7 @@ { "id": "smollm:1.7b-base-v0.2-q4_0", "name": "smollm:1.7b-base-v0.2-q4_0", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "991MB", "digest": "97f3f0aef2df", "provider": "", @@ -35853,7 +35853,7 @@ { "id": "smollm:1.7b-base-v0.2-q4_1", "name": "smollm:1.7b-base-v0.2-q4_1", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "1.1GB", "digest": "0c1bb3471f92", "provider": "", @@ -35863,7 +35863,7 @@ { "id": "smollm:1.7b-base-v0.2-q4_K_M", "name": "smollm:1.7b-base-v0.2-q4_K_M", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "1.1GB", "digest": "8dec12ffec4a", "provider": "", @@ -35873,7 +35873,7 @@ { "id": "smollm:1.7b-base-v0.2-q4_K_S", "name": "smollm:1.7b-base-v0.2-q4_K_S", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "999MB", "digest": "9b3d36b6e0c2", "provider": "", @@ -35883,7 +35883,7 @@ { "id": "smollm:1.7b-base-v0.2-q5_0", "name": "smollm:1.7b-base-v0.2-q5_0", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "1.2GB", "digest": "7eddfb294e53", "provider": "", @@ -35893,7 +35893,7 @@ { "id": "smollm:1.7b-base-v0.2-q5_1", "name": "smollm:1.7b-base-v0.2-q5_1", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "1.3GB", "digest": "fde4c91abcce", "provider": "", @@ -35903,7 +35903,7 @@ { "id": "smollm:1.7b-base-v0.2-q5_K_M", "name": "smollm:1.7b-base-v0.2-q5_K_M", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "1.2GB", "digest": "7daaf6f561d8", "provider": "", @@ -35913,7 +35913,7 @@ { "id": "smollm:1.7b-base-v0.2-q5_K_S", "name": "smollm:1.7b-base-v0.2-q5_K_S", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "1.2GB", "digest": "4f0e5ec42d5b", "provider": "", @@ -35923,7 +35923,7 @@ { "id": "smollm:1.7b-base-v0.2-q6_K", "name": "smollm:1.7b-base-v0.2-q6_K", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "1.4GB", "digest": "9cf3be2645b6", "provider": "", @@ -35933,7 +35933,7 @@ { "id": "smollm:1.7b-base-v0.2-q8_0", "name": "smollm:1.7b-base-v0.2-q8_0", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "1.8GB", "digest": "d1f91cd676ad", "provider": "", @@ -35943,7 +35943,7 @@ { "id": "smollm:1.7b-instruct-v0.2-fp16", "name": "smollm:1.7b-instruct-v0.2-fp16", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "3.4GB", "digest": "b07e591ca9ba", "provider": "", @@ -35953,7 +35953,7 @@ { "id": "smollm:1.7b-instruct-v0.2-q2_K", "name": "smollm:1.7b-instruct-v0.2-q2_K", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "675MB", "digest": "da8ba92e42b1", "provider": "", @@ -35963,7 +35963,7 @@ { "id": "smollm:1.7b-instruct-v0.2-q3_K_L", "name": "smollm:1.7b-instruct-v0.2-q3_K_L", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "933MB", "digest": "88ef3062260e", "provider": "", @@ -35973,7 +35973,7 @@ { "id": "smollm:1.7b-instruct-v0.2-q3_K_M", "name": "smollm:1.7b-instruct-v0.2-q3_K_M", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "860MB", "digest": "728c1f932b06", "provider": "", @@ -35983,7 +35983,7 @@ { "id": "smollm:1.7b-instruct-v0.2-q3_K_S", "name": "smollm:1.7b-instruct-v0.2-q3_K_S", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "777MB", "digest": "c35438da7180", "provider": "", @@ -35993,7 +35993,7 @@ { "id": "smollm:1.7b-instruct-v0.2-q4_0", "name": "smollm:1.7b-instruct-v0.2-q4_0", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "991MB", "digest": "95f6557a0f0f", "provider": "", @@ -36003,7 +36003,7 @@ { "id": "smollm:1.7b-instruct-v0.2-q4_1", "name": "smollm:1.7b-instruct-v0.2-q4_1", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "1.1GB", "digest": "77d7238b232e", "provider": "", @@ -36013,7 +36013,7 @@ { "id": "smollm:1.7b-instruct-v0.2-q4_K_M", "name": "smollm:1.7b-instruct-v0.2-q4_K_M", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "1.1GB", "digest": "dd7299829a14", "provider": "", @@ -36023,7 +36023,7 @@ { "id": "smollm:1.7b-instruct-v0.2-q4_K_S", "name": "smollm:1.7b-instruct-v0.2-q4_K_S", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "999MB", "digest": "35eec1a7014d", "provider": "", @@ -36033,7 +36033,7 @@ { "id": "smollm:1.7b-instruct-v0.2-q5_0", "name": "smollm:1.7b-instruct-v0.2-q5_0", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "1.2GB", "digest": "8391060f29db", "provider": "", @@ -36043,7 +36043,7 @@ { "id": "smollm:1.7b-instruct-v0.2-q5_1", "name": "smollm:1.7b-instruct-v0.2-q5_1", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "1.3GB", "digest": "ce91dd1d7380", "provider": "", @@ -36053,7 +36053,7 @@ { "id": "smollm:1.7b-instruct-v0.2-q5_K_M", "name": "smollm:1.7b-instruct-v0.2-q5_K_M", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "1.2GB", "digest": "91d16f72a7b3", "provider": "", @@ -36063,7 +36063,7 @@ { "id": "smollm:1.7b-instruct-v0.2-q5_K_S", "name": "smollm:1.7b-instruct-v0.2-q5_K_S", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "1.2GB", "digest": "489a893ac8a7", "provider": "", @@ -36073,7 +36073,7 @@ { "id": "smollm:1.7b-instruct-v0.2-q6_K", "name": "smollm:1.7b-instruct-v0.2-q6_K", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "1.4GB", "digest": "ecdf9864899c", "provider": "", @@ -36083,7 +36083,7 @@ { "id": "smollm:1.7b-instruct-v0.2-q8_0", "name": "smollm:1.7b-instruct-v0.2-q8_0", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "1.8GB", "digest": "4afeb8de9eec", "provider": "", @@ -36093,7 +36093,7 @@ { "id": "smollm:135m-base-v0.2-fp16", "name": "smollm:135m-base-v0.2-fp16", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "271MB", "digest": "1d0f00cec918", "provider": "", @@ -36103,7 +36103,7 @@ { "id": "smollm:135m-base-v0.2-q2_K", "name": "smollm:135m-base-v0.2-q2_K", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "88MB", "digest": "e2048e8ac78d", "provider": "", @@ -36113,7 +36113,7 @@ { "id": "smollm:135m-base-v0.2-q3_K_L", "name": "smollm:135m-base-v0.2-q3_K_L", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "98MB", "digest": "c2e22813067a", "provider": "", @@ -36123,7 +36123,7 @@ { "id": "smollm:135m-base-v0.2-q3_K_M", "name": "smollm:135m-base-v0.2-q3_K_M", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "94MB", "digest": "a5ab45ae2e2e", "provider": "", @@ -36133,7 +36133,7 @@ { "id": "smollm:135m-base-v0.2-q3_K_S", "name": "smollm:135m-base-v0.2-q3_K_S", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "88MB", "digest": "102433ffd9fb", "provider": "", @@ -36143,7 +36143,7 @@ { "id": "smollm:135m-base-v0.2-q4_0", "name": "smollm:135m-base-v0.2-q4_0", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "92MB", "digest": "528415e872e5", "provider": "", @@ -36153,7 +36153,7 @@ { "id": "smollm:135m-base-v0.2-q4_1", "name": "smollm:135m-base-v0.2-q4_1", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "98MB", "digest": "edecca7cff90", "provider": "", @@ -36163,7 +36163,7 @@ { "id": "smollm:135m-base-v0.2-q4_K_M", "name": "smollm:135m-base-v0.2-q4_K_M", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "105MB", "digest": "3177ecf5d3b5", "provider": "", @@ -36173,7 +36173,7 @@ { "id": "smollm:135m-base-v0.2-q4_K_S", "name": "smollm:135m-base-v0.2-q4_K_S", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "102MB", "digest": "2aa760d22716", "provider": "", @@ -36183,7 +36183,7 @@ { "id": "smollm:135m-base-v0.2-q5_0", "name": "smollm:135m-base-v0.2-q5_0", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "105MB", "digest": "825973a56945", "provider": "", @@ -36193,7 +36193,7 @@ { "id": "smollm:135m-base-v0.2-q5_1", "name": "smollm:135m-base-v0.2-q5_1", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "112MB", "digest": "065d585f86d9", "provider": "", @@ -36203,7 +36203,7 @@ { "id": "smollm:135m-base-v0.2-q5_K_M", "name": "smollm:135m-base-v0.2-q5_K_M", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "112MB", "digest": "34545da0aa92", "provider": "", @@ -36213,7 +36213,7 @@ { "id": "smollm:135m-base-v0.2-q5_K_S", "name": "smollm:135m-base-v0.2-q5_K_S", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "110MB", "digest": "4aef8aaa4469", "provider": "", @@ -36223,7 +36223,7 @@ { "id": "smollm:135m-base-v0.2-q6_K", "name": "smollm:135m-base-v0.2-q6_K", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "138MB", "digest": "b11338fff65a", "provider": "", @@ -36233,7 +36233,7 @@ { "id": "smollm:135m-base-v0.2-q8_0", "name": "smollm:135m-base-v0.2-q8_0", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "145MB", "digest": "fcdc33f9909d", "provider": "", @@ -36243,7 +36243,7 @@ { "id": "smollm:135m-instruct-v0.2-fp16", "name": "smollm:135m-instruct-v0.2-fp16", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "271MB", "digest": "95d01a081beb", "provider": "", @@ -36253,7 +36253,7 @@ { "id": "smollm:135m-instruct-v0.2-q2_K", "name": "smollm:135m-instruct-v0.2-q2_K", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "88MB", "digest": "e14a680b0b22", "provider": "", @@ -36263,7 +36263,7 @@ { "id": "smollm:135m-instruct-v0.2-q3_K_L", "name": "smollm:135m-instruct-v0.2-q3_K_L", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "98MB", "digest": "aede666c7e16", "provider": "", @@ -36273,7 +36273,7 @@ { "id": "smollm:135m-instruct-v0.2-q3_K_M", "name": "smollm:135m-instruct-v0.2-q3_K_M", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "94MB", "digest": "f5db1ab329b1", "provider": "", @@ -36283,7 +36283,7 @@ { "id": "smollm:135m-instruct-v0.2-q3_K_S", "name": "smollm:135m-instruct-v0.2-q3_K_S", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "88MB", "digest": "3ae895b5e9ea", "provider": "", @@ -36293,7 +36293,7 @@ { "id": "smollm:135m-instruct-v0.2-q4_0", "name": "smollm:135m-instruct-v0.2-q4_0", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "92MB", "digest": "b0b2a4617438", "provider": "", @@ -36303,7 +36303,7 @@ { "id": "smollm:135m-instruct-v0.2-q4_1", "name": "smollm:135m-instruct-v0.2-q4_1", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "98MB", "digest": "bbf7e14150a9", "provider": "", @@ -36313,7 +36313,7 @@ { "id": "smollm:135m-instruct-v0.2-q4_K_M", "name": "smollm:135m-instruct-v0.2-q4_K_M", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "105MB", "digest": "7eedbf45baa1", "provider": "", @@ -36323,7 +36323,7 @@ { "id": "smollm:135m-instruct-v0.2-q4_K_S", "name": "smollm:135m-instruct-v0.2-q4_K_S", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "102MB", "digest": "45013939c7c9", "provider": "", @@ -36333,7 +36333,7 @@ { "id": "smollm:135m-instruct-v0.2-q5_0", "name": "smollm:135m-instruct-v0.2-q5_0", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "105MB", "digest": "33187e134422", "provider": "", @@ -36343,7 +36343,7 @@ { "id": "smollm:135m-instruct-v0.2-q5_1", "name": "smollm:135m-instruct-v0.2-q5_1", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "112MB", "digest": "51f73e9aa3f5", "provider": "", @@ -36353,7 +36353,7 @@ { "id": "smollm:135m-instruct-v0.2-q5_K_M", "name": "smollm:135m-instruct-v0.2-q5_K_M", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "112MB", "digest": "a8975f4dc03b", "provider": "", @@ -36363,7 +36363,7 @@ { "id": "smollm:135m-instruct-v0.2-q5_K_S", "name": "smollm:135m-instruct-v0.2-q5_K_S", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "110MB", "digest": "52d3fabb63ee", "provider": "", @@ -36373,7 +36373,7 @@ { "id": "smollm:135m-instruct-v0.2-q6_K", "name": "smollm:135m-instruct-v0.2-q6_K", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "138MB", "digest": "dd1e60cf54df", "provider": "", @@ -36383,7 +36383,7 @@ { "id": "smollm:135m-instruct-v0.2-q8_0", "name": "smollm:135m-instruct-v0.2-q8_0", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "145MB", "digest": "6fbf8e918862", "provider": "", @@ -36393,7 +36393,7 @@ { "id": "smollm:360m-base-v0.2-fp16", "name": "smollm:360m-base-v0.2-fp16", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "726MB", "digest": "417c81308b94", "provider": "", @@ -36403,7 +36403,7 @@ { "id": "smollm:360m-base-v0.2-q2_K", "name": "smollm:360m-base-v0.2-q2_K", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "219MB", "digest": "e5e1a58c7d0c", "provider": "", @@ -36413,7 +36413,7 @@ { "id": "smollm:360m-base-v0.2-q3_K_L", "name": "smollm:360m-base-v0.2-q3_K_L", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "246MB", "digest": "d13db1d8af29", "provider": "", @@ -36423,7 +36423,7 @@ { "id": "smollm:360m-base-v0.2-q3_K_M", "name": "smollm:360m-base-v0.2-q3_K_M", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "235MB", "digest": "66875ceb33a7", "provider": "", @@ -36433,7 +36433,7 @@ { "id": "smollm:360m-base-v0.2-q3_K_S", "name": "smollm:360m-base-v0.2-q3_K_S", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "219MB", "digest": "8aba991b34bf", "provider": "", @@ -36443,7 +36443,7 @@ { "id": "smollm:360m-base-v0.2-q4_0", "name": "smollm:360m-base-v0.2-q4_0", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "229MB", "digest": "32ed02cfe24e", "provider": "", @@ -36453,7 +36453,7 @@ { "id": "smollm:360m-base-v0.2-q4_1", "name": "smollm:360m-base-v0.2-q4_1", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "249MB", "digest": "74c8ae01b04d", "provider": "", @@ -36463,7 +36463,7 @@ { "id": "smollm:360m-base-v0.2-q4_K_M", "name": "smollm:360m-base-v0.2-q4_K_M", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "271MB", "digest": "d3b4961678c3", "provider": "", @@ -36473,7 +36473,7 @@ { "id": "smollm:360m-base-v0.2-q4_K_S", "name": "smollm:360m-base-v0.2-q4_K_S", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "260MB", "digest": "81b950fac810", "provider": "", @@ -36483,7 +36483,7 @@ { "id": "smollm:360m-base-v0.2-q5_0", "name": "smollm:360m-base-v0.2-q5_0", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "268MB", "digest": "319a822c1baa", "provider": "", @@ -36493,7 +36493,7 @@ { "id": "smollm:360m-base-v0.2-q5_1", "name": "smollm:360m-base-v0.2-q5_1", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "288MB", "digest": "1c350d9f5c43", "provider": "", @@ -36503,7 +36503,7 @@ { "id": "smollm:360m-base-v0.2-q5_K_M", "name": "smollm:360m-base-v0.2-q5_K_M", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "290MB", "digest": "bf2e3336142d", "provider": "", @@ -36513,7 +36513,7 @@ { "id": "smollm:360m-base-v0.2-q5_K_S", "name": "smollm:360m-base-v0.2-q5_K_S", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "283MB", "digest": "05dad743d112", "provider": "", @@ -36523,7 +36523,7 @@ { "id": "smollm:360m-base-v0.2-q6_K", "name": "smollm:360m-base-v0.2-q6_K", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "367MB", "digest": "6006320b3839", "provider": "", @@ -36533,7 +36533,7 @@ { "id": "smollm:360m-base-v0.2-q8_0", "name": "smollm:360m-base-v0.2-q8_0", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "386MB", "digest": "dd417453fb49", "provider": "", @@ -36543,7 +36543,7 @@ { "id": "smollm:360m-instruct-v0.2-fp16", "name": "smollm:360m-instruct-v0.2-fp16", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "726MB", "digest": "d0475be06ee3", "provider": "", @@ -36553,7 +36553,7 @@ { "id": "smollm:360m-instruct-v0.2-q2_K", "name": "smollm:360m-instruct-v0.2-q2_K", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "219MB", "digest": "1dfe94149c4d", "provider": "", @@ -36563,7 +36563,7 @@ { "id": "smollm:360m-instruct-v0.2-q3_K_L", "name": "smollm:360m-instruct-v0.2-q3_K_L", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "246MB", "digest": "6f711219bd5f", "provider": "", @@ -36573,7 +36573,7 @@ { "id": "smollm:360m-instruct-v0.2-q3_K_M", "name": "smollm:360m-instruct-v0.2-q3_K_M", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "235MB", "digest": "4ecfcbc87975", "provider": "", @@ -36583,7 +36583,7 @@ { "id": "smollm:360m-instruct-v0.2-q3_K_S", "name": "smollm:360m-instruct-v0.2-q3_K_S", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "219MB", "digest": "359f743b44a1", "provider": "", @@ -36593,7 +36593,7 @@ { "id": "smollm:360m-instruct-v0.2-q4_0", "name": "smollm:360m-instruct-v0.2-q4_0", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "229MB", "digest": "b3ba1ccba2b8", "provider": "", @@ -36603,7 +36603,7 @@ { "id": "smollm:360m-instruct-v0.2-q4_1", "name": "smollm:360m-instruct-v0.2-q4_1", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "249MB", "digest": "4a28c336aedd", "provider": "", @@ -36613,7 +36613,7 @@ { "id": "smollm:360m-instruct-v0.2-q4_K_M", "name": "smollm:360m-instruct-v0.2-q4_K_M", - "description": "πŸͺ A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", + "description": "A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.", "size": "271MB", "digest": "535cb302b6b9", "provider": "", @@ -43183,7 +43183,7 @@ { "id": "dolphin3", "name": "dolphin3", - "description": "Dolphin 3.0 Llama 3.1 8B 🐬 is the next generation of the Dolphin series of instruct-tuned models designed to be the ultimate general purpose local model, enabling coding, math, agentic, function calling, and general use cases.", + "description": "Dolphin 3.0 Llama 3.1 8B is the next generation of the Dolphin series of instruct-tuned models designed to be the ultimate general purpose local model, enabling coding, math, agentic, function calling, and general use cases.", "size": "4.9GB", "digest": "d5ab9ae8e1f2", "provider": "", @@ -43193,7 +43193,7 @@ { "id": "dolphin3:8b", "name": "dolphin3:8b", - "description": "Dolphin 3.0 Llama 3.1 8B 🐬 is the next generation of the Dolphin series of instruct-tuned models designed to be the ultimate general purpose local model, enabling coding, math, agentic, function calling, and general use cases.", + "description": "Dolphin 3.0 Llama 3.1 8B is the next generation of the Dolphin series of instruct-tuned models designed to be the ultimate general purpose local model, enabling coding, math, agentic, function calling, and general use cases.", "size": "4.9GB", "digest": "d5ab9ae8e1f2", "provider": "", @@ -43203,7 +43203,7 @@ { "id": "dolphin3:8b-llama3.1-fp16", "name": "dolphin3:8b-llama3.1-fp16", - "description": "Dolphin 3.0 Llama 3.1 8B 🐬 is the next generation of the Dolphin series of instruct-tuned models designed to be the ultimate general purpose local model, enabling coding, math, agentic, function calling, and general use cases.", + "description": "Dolphin 3.0 Llama 3.1 8B is the next generation of the Dolphin series of instruct-tuned models designed to be the ultimate general purpose local model, enabling coding, math, agentic, function calling, and general use cases.", "size": "16GB", "digest": "b0941c6f3226", "provider": "", @@ -43213,7 +43213,7 @@ { "id": "dolphin3:8b-llama3.1-q4_K_M", "name": "dolphin3:8b-llama3.1-q4_K_M", - "description": "Dolphin 3.0 Llama 3.1 8B 🐬 is the next generation of the Dolphin series of instruct-tuned models designed to be the ultimate general purpose local model, enabling coding, math, agentic, function calling, and general use cases.", + "description": "Dolphin 3.0 Llama 3.1 8B is the next generation of the Dolphin series of instruct-tuned models designed to be the ultimate general purpose local model, enabling coding, math, agentic, function calling, and general use cases.", "size": "4.9GB", "digest": "d5ab9ae8e1f2", "provider": "", @@ -43223,7 +43223,7 @@ { "id": "dolphin3:8b-llama3.1-q8_0", "name": "dolphin3:8b-llama3.1-q8_0", - "description": "Dolphin 3.0 Llama 3.1 8B 🐬 is the next generation of the Dolphin series of instruct-tuned models designed to be the ultimate general purpose local model, enabling coding, math, agentic, function calling, and general use cases.", + "description": "Dolphin 3.0 Llama 3.1 8B is the next generation of the Dolphin series of instruct-tuned models designed to be the ultimate general purpose local model, enabling coding, math, agentic, function calling, and general use cases.", "size": "8.5GB", "digest": "e3310b61ffdb", "provider": "", @@ -59063,7 +59063,7 @@ { "id": "magicoder", "name": "magicoder", - "description": "🎩 Magicoder is a family of 7B parameter models trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets.", + "description": "Magicoder is a family of 7B parameter models trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets.", "size": "3.8GB", "digest": "8007de06f5d9", "provider": "", @@ -59073,7 +59073,7 @@ { "id": "magicoder:7b", "name": "magicoder:7b", - "description": "🎩 Magicoder is a family of 7B parameter models trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets.", + "description": "Magicoder is a family of 7B parameter models trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets.", "size": "3.8GB", "digest": "8007de06f5d9", "provider": "", @@ -59083,7 +59083,7 @@ { "id": "magicoder:7b-s-cl", "name": "magicoder:7b-s-cl", - "description": "🎩 Magicoder is a family of 7B parameter models trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets.", + "description": "Magicoder is a family of 7B parameter models trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets.", "size": "3.8GB", "digest": "8007de06f5d9", "provider": "", @@ -59093,7 +59093,7 @@ { "id": "magicoder:7b-s-cl-fp16", "name": "magicoder:7b-s-cl-fp16", - "description": "🎩 Magicoder is a family of 7B parameter models trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets.", + "description": "Magicoder is a family of 7B parameter models trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets.", "size": "13GB", "digest": "859e1ae4f566", "provider": "", @@ -59103,7 +59103,7 @@ { "id": "magicoder:7b-s-cl-q2_K", "name": "magicoder:7b-s-cl-q2_K", - "description": "🎩 Magicoder is a family of 7B parameter models trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets.", + "description": "Magicoder is a family of 7B parameter models trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets.", "size": "2.8GB", "digest": "86fd329c64b6", "provider": "", @@ -59113,7 +59113,7 @@ { "id": "magicoder:7b-s-cl-q3_K_L", "name": "magicoder:7b-s-cl-q3_K_L", - "description": "🎩 Magicoder is a family of 7B parameter models trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets.", + "description": "Magicoder is a family of 7B parameter models trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets.", "size": "3.6GB", "digest": "2bf1232fd9c1", "provider": "", @@ -59123,7 +59123,7 @@ { "id": "magicoder:7b-s-cl-q3_K_M", "name": "magicoder:7b-s-cl-q3_K_M", - "description": "🎩 Magicoder is a family of 7B parameter models trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets.", + "description": "Magicoder is a family of 7B parameter models trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets.", "size": "3.3GB", "digest": "b2a3d9987ff3", "provider": "", @@ -59133,7 +59133,7 @@ { "id": "magicoder:7b-s-cl-q3_K_S", "name": "magicoder:7b-s-cl-q3_K_S", - "description": "🎩 Magicoder is a family of 7B parameter models trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets.", + "description": "Magicoder is a family of 7B parameter models trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets.", "size": "2.9GB", "digest": "b7e2d7a303f8", "provider": "", @@ -59143,7 +59143,7 @@ { "id": "magicoder:7b-s-cl-q4_0", "name": "magicoder:7b-s-cl-q4_0", - "description": "🎩 Magicoder is a family of 7B parameter models trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets.", + "description": "Magicoder is a family of 7B parameter models trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets.", "size": "3.8GB", "digest": "8007de06f5d9", "provider": "", @@ -59153,7 +59153,7 @@ { "id": "magicoder:7b-s-cl-q4_1", "name": "magicoder:7b-s-cl-q4_1", - "description": "🎩 Magicoder is a family of 7B parameter models trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets.", + "description": "Magicoder is a family of 7B parameter models trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets.", "size": "4.2GB", "digest": "b29bf322a686", "provider": "", @@ -59163,7 +59163,7 @@ { "id": "magicoder:7b-s-cl-q4_K_M", "name": "magicoder:7b-s-cl-q4_K_M", - "description": "🎩 Magicoder is a family of 7B parameter models trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets.", + "description": "Magicoder is a family of 7B parameter models trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets.", "size": "4.1GB", "digest": "902b6b028113", "provider": "", @@ -59173,7 +59173,7 @@ { "id": "magicoder:7b-s-cl-q4_K_S", "name": "magicoder:7b-s-cl-q4_K_S", - "description": "🎩 Magicoder is a family of 7B parameter models trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets.", + "description": "Magicoder is a family of 7B parameter models trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets.", "size": "3.9GB", "digest": "42b56dd9b1da", "provider": "", @@ -59183,7 +59183,7 @@ { "id": "magicoder:7b-s-cl-q5_0", "name": "magicoder:7b-s-cl-q5_0", - "description": "🎩 Magicoder is a family of 7B parameter models trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets.", + "description": "Magicoder is a family of 7B parameter models trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets.", "size": "4.7GB", "digest": "10fa66c827ee", "provider": "", @@ -59193,7 +59193,7 @@ { "id": "magicoder:7b-s-cl-q5_1", "name": "magicoder:7b-s-cl-q5_1", - "description": "🎩 Magicoder is a family of 7B parameter models trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets.", + "description": "Magicoder is a family of 7B parameter models trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets.", "size": "5.1GB", "digest": "7da1fdad5251", "provider": "", @@ -59203,7 +59203,7 @@ { "id": "magicoder:7b-s-cl-q5_K_M", "name": "magicoder:7b-s-cl-q5_K_M", - "description": "🎩 Magicoder is a family of 7B parameter models trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets.", + "description": "Magicoder is a family of 7B parameter models trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets.", "size": "4.8GB", "digest": "057c4f587f41", "provider": "", @@ -59213,7 +59213,7 @@ { "id": "magicoder:7b-s-cl-q5_K_S", "name": "magicoder:7b-s-cl-q5_K_S", - "description": "🎩 Magicoder is a family of 7B parameter models trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets.", + "description": "Magicoder is a family of 7B parameter models trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets.", "size": "4.7GB", "digest": "ce0b81d40da6", "provider": "", @@ -59223,7 +59223,7 @@ { "id": "magicoder:7b-s-cl-q6_K", "name": "magicoder:7b-s-cl-q6_K", - "description": "🎩 Magicoder is a family of 7B parameter models trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets.", + "description": "Magicoder is a family of 7B parameter models trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets.", "size": "5.5GB", "digest": "cf853951ae4a", "provider": "", @@ -59233,7 +59233,7 @@ { "id": "magicoder:7b-s-cl-q8_0", "name": "magicoder:7b-s-cl-q8_0", - "description": "🎩 Magicoder is a family of 7B parameter models trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets.", + "description": "Magicoder is a family of 7B parameter models trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets.", "size": "7.2GB", "digest": "96b5e4193464", "provider": "", diff --git a/module/ai-local/iml.go b/module/ai-local/iml.go index cf886e44..a7da6a29 100644 --- a/module/ai-local/iml.go +++ b/module/ai-local/iml.go @@ -512,8 +512,8 @@ func (i *imlLocalModel) OnInit() { }) models, version := ai_provider_local.ModelsCanInstall() for _, model := range models { - if v, ok := oldModels[model.Id]; ok { + delete(oldModels, model.Id) if v.Version == version { continue } @@ -541,7 +541,7 @@ func (i *imlLocalModel) OnInit() { return } } - delete(oldModels, model.Id) + } for id := range oldModels { err = i.localModelPackageService.Delete(ctx, id) diff --git a/module/ai/iml.go b/module/ai/iml.go index f64e21b1..d92682d4 100644 --- a/module/ai/iml.go +++ b/module/ai/iml.go @@ -694,7 +694,6 @@ func (i *imlProviderModule) UpdateProviderConfig(ctx context.Context, id string, }, newKey(defaultKey), }, true) - return nil }) } diff --git a/service/ai-local/iml.go b/service/ai-local/iml.go index 7256c5f9..f0c79f8a 100644 --- a/service/ai-local/iml.go +++ b/service/ai-local/iml.go @@ -21,7 +21,7 @@ type imlLocalModelService struct { } func (i *imlLocalModelService) UpdateProvider(ctx context.Context, provider string, ids ...string) error { - _, err := i.store.UpdateWhere(ctx, map[string]interface{}{"provider": provider}, map[string]interface{}{"uuid": ids}) + _, err := i.store.UpdateField(ctx, "provider", provider, "uuid in (?)", ids) return err } diff --git a/stores/ai/model.go b/stores/ai/model.go index 0eb2788b..56fda088 100644 --- a/stores/ai/model.go +++ b/stores/ai/model.go @@ -138,7 +138,8 @@ type LocalModelPackage struct { Name string `gorm:"type:varchar(100);not null;column:name;comment:名称"` Size string `gorm:"type:varchar(100);not null;column:size;comment:ζ¨‘εž‹ε€§ε°"` Hash string `gorm:"type:varchar(100);not null;column:hash;comment:ζ¨‘εž‹hash"` - Description string `gorm:"type:varchar(255);not null;column:description;comment:描述"` + Description string `gorm:"type:varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci;not null;column:description;comment:描述"` + Text string `gorm:"type:varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci;not null;column:text;comment:描述"` Version string `gorm:"type:varchar(100);not null;column:version;comment:η‰ˆζœ¬"` IsPopular bool `gorm:"type:tinyint(1);not null;column:is_popular;comment:ζ˜―ε¦ηƒ­ι—¨"` } @@ -187,4 +188,3 @@ func (i *ProviderModel) TableName() string { func (i *ProviderModel) IdValue() int64 { return i.Id } -