Image-Text-to-Text
Safetensors
MLX
lance-mlx
qwen2_5_vl
lance
vqa
image-understanding
quantized
awq
apple-silicon
4-bit precision
Instructions to use mlx-community/Lance-3B-AWQ-INT4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/Lance-3B-AWQ-INT4 with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("mlx-community/Lance-3B-AWQ-INT4") config = load_config("mlx-community/Lance-3B-AWQ-INT4") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
| { | |
| "source_dir": "/Users/dustinnielson/DEV_INT/lance-mlx-research/lance-mlx-models/Lance-3B-bf16", | |
| "stats_dir": "/Users/dustinnielson/DEV_INT/lance-mlx-research/lance-mlx/notes/phase5n_diagnostics/phase5c3_awq_port/act_stats", | |
| "bits": 4, | |
| "group_size": 128, | |
| "mode": "affine+awq", | |
| "n_grid": 20, | |
| "bf16_bytes": 12371046496, | |
| "quantized_bytes": 3306811488, | |
| "compression_ratio": 0.2673024864201432, | |
| "fusion_groups_applied": 144, | |
| "fusion_groups_skipped": 0, | |
| "skip_patterns_always": [ | |
| "time_embedder.proj_in", | |
| "time_embedder.proj_out", | |
| "llm2vae" | |
| ], | |
| "awq_per_group": { | |
| "layers.0.input_layernorm": { | |
| "best_alpha": 0.35, | |
| "best_err": 0.00458593072835356, | |
| "consumers": [ | |
| "layers.0.self_attn.q_proj", | |
| "layers.0.self_attn.k_proj", | |
| "layers.0.self_attn.v_proj" | |
| ] | |
| }, | |
| "layers.0.input_layernorm_moe_gen": { | |
| "best_alpha": 0.4, | |
| "best_err": 0.0021403880091384053, | |
| "consumers": [ | |
| "layers.0.self_attn.q_proj_moe_gen", | |
| "layers.0.self_attn.k_proj_moe_gen", | |
| "layers.0.self_attn.v_proj_moe_gen" | |
| ] | |
| }, | |
| "layers.0.post_attention_layernorm": { | |
| "best_alpha": 0.35, | |
| "best_err": 0.007643559714779258, | |
| "consumers": [ | |
| "layers.0.mlp.gate_proj", | |
| "layers.0.mlp.up_proj" | |
| ] | |
| }, | |
| "layers.0.post_attention_layernorm_moe_gen": { | |
| "best_alpha": 0.4, | |
| "best_err": 0.003576420247554779, | |
| "consumers": [ | |
| "layers.0.mlp_moe_gen.gate_proj", | |
| "layers.0.mlp_moe_gen.up_proj" | |
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| }, | |
| "layers.1.input_layernorm": { | |
| "best_alpha": 0.3, | |
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| "layers.1.self_attn.q_proj", | |
| "layers.1.self_attn.k_proj", | |
| "layers.1.self_attn.v_proj" | |
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| "layers.1.input_layernorm_moe_gen": { | |
| "best_alpha": 0.3, | |
| "best_err": 0.05615841131657362, | |
| "consumers": [ | |
| "layers.1.self_attn.q_proj_moe_gen", | |
| "layers.1.self_attn.k_proj_moe_gen", | |
| "layers.1.self_attn.v_proj_moe_gen" | |
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| "layers.1.post_attention_layernorm": { | |
| "best_alpha": 0.35, | |
| "best_err": 0.01055069756694138, | |
| "consumers": [ | |
| "layers.1.mlp.gate_proj", | |
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| "layers.1.post_attention_layernorm_moe_gen": { | |
| "best_alpha": 0.4, | |
| "best_err": 0.0033836811780929565, | |
| "consumers": [ | |
| "layers.1.mlp_moe_gen.gate_proj", | |
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| "layers.2.input_layernorm_moe_gen": { | |
| "best_alpha": 0.3, | |
| "best_err": 0.029231476597487926, | |
| "consumers": [ | |
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| "layers.2.self_attn.v_proj_moe_gen" | |
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| "layers.2.post_attention_layernorm": { | |
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| "consumers": [ | |
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| "layers.3.input_layernorm_moe_gen": { | |
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| "consumers": [ | |
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| "layers.3.self_attn.v_proj_moe_gen" | |
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| "consumers": [ | |
| "layers.3.mlp.gate_proj", | |
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| "layers.3.post_attention_layernorm_moe_gen": { | |
| "best_alpha": 0.5, | |
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| "consumers": [ | |
| "layers.3.mlp_moe_gen.gate_proj", | |
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| "consumers": [ | |
| "layers.4.self_attn.q_proj_moe_gen", | |
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| "layers.4.self_attn.v_proj_moe_gen" | |
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| "layers.4.post_attention_layernorm": { | |
| "best_alpha": 0.45, | |
| "best_err": 0.009718624409288168, | |
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| "layers.4.post_attention_layernorm_moe_gen": { | |
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| "layers.5.input_layernorm_moe_gen": { | |
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| "layers.5.self_attn.v_proj_moe_gen" | |
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| "consumers": [ | |
| "layers.5.mlp_moe_gen.gate_proj", | |
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| "layers.8.mlp_moe_gen.gate_proj", | |
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| "layers.9.input_layernorm": { | |
| "best_alpha": 0.3, | |
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| "layers.9.self_attn.q_proj", | |
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| "layers.9.input_layernorm_moe_gen": { | |
| "best_alpha": 0.35, | |
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| "layers.9.self_attn.v_proj_moe_gen" | |
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| "layers.10.input_layernorm": { | |
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| "layers.10.self_attn.q_proj", | |
| "layers.10.self_attn.k_proj", | |
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| "layers.10.input_layernorm_moe_gen": { | |
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| "best_err": 0.008221070980653167, | |
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| "layers.10.self_attn.q_proj_moe_gen", | |
| "layers.10.self_attn.k_proj_moe_gen", | |
| "layers.10.self_attn.v_proj_moe_gen" | |
| ] | |
| }, | |
| "layers.10.post_attention_layernorm": { | |
| "best_alpha": 0.3, | |
| "best_err": 0.003546421998180449, | |
| "consumers": [ | |
| "layers.10.mlp.gate_proj", | |
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| "layers.10.post_attention_layernorm_moe_gen": { | |
| "best_alpha": 0.3, | |
| "best_err": 0.0033734560711309314, | |
| "consumers": [ | |
| "layers.10.mlp_moe_gen.gate_proj", | |
| "layers.10.mlp_moe_gen.up_proj" | |
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| }, | |
| "layers.11.input_layernorm": { | |
| "best_alpha": 0.3, | |
| "best_err": 0.006951858755201101, | |
| "consumers": [ | |
| "layers.11.self_attn.q_proj", | |
| "layers.11.self_attn.k_proj", | |
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| "layers.11.input_layernorm_moe_gen": { | |
| "best_alpha": 0.4, | |
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| "consumers": [ | |
| "layers.11.self_attn.q_proj_moe_gen", | |
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| "layers.11.post_attention_layernorm_moe_gen": { | |
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| "layers.11.mlp_moe_gen.gate_proj", | |
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| "layers.12.input_layernorm": { | |
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| "layers.12.self_attn.q_proj", | |
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| "layers.12.input_layernorm_moe_gen": { | |
| "best_alpha": 0.35, | |
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| "layers.12.self_attn.q_proj_moe_gen", | |
| "layers.12.self_attn.k_proj_moe_gen", | |
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| }, | |
| "layers.12.post_attention_layernorm": { | |
| "best_alpha": 0.35, | |
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| "consumers": [ | |
| "layers.12.mlp.gate_proj", | |
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| "layers.12.post_attention_layernorm_moe_gen": { | |
| "best_alpha": 0.3, | |
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| "consumers": [ | |
| "layers.12.mlp_moe_gen.gate_proj", | |
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| "layers.13.self_attn.q_proj", | |
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| "layers.13.input_layernorm_moe_gen": { | |
| "best_alpha": 0.35, | |
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| "consumers": [ | |
| "layers.13.self_attn.q_proj_moe_gen", | |
| "layers.13.self_attn.k_proj_moe_gen", | |
| "layers.13.self_attn.v_proj_moe_gen" | |
| ] | |
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| "layers.13.post_attention_layernorm": { | |
| "best_alpha": 0.35, | |
| "best_err": 0.003296053037047386, | |
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| "layers.13.mlp.gate_proj", | |
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| "layers.13.post_attention_layernorm_moe_gen": { | |
| "best_alpha": 0.3, | |
| "best_err": 0.003499676357023418, | |
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| "layers.13.mlp_moe_gen.gate_proj", | |
| "layers.13.mlp_moe_gen.up_proj" | |
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| "layers.14.input_layernorm": { | |
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| "best_err": 0.006669960916042328, | |
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| "layers.14.self_attn.q_proj", | |
| "layers.14.self_attn.k_proj", | |
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| }, | |
| "layers.14.input_layernorm_moe_gen": { | |
| "best_alpha": 0.4, | |
| "best_err": 0.008936543483287096, | |
| "consumers": [ | |
| "layers.14.self_attn.q_proj_moe_gen", | |
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