Zero-Shot Classification
Transformers
Safetensors
qwen3_5
image-text-to-text
auto-round
intel
quantization
4bit
decision-model
multimodal
vision
video
vllm-semantic-router
custom_code
4-bit precision
Instructions to use Vishva007/d3-edge-W4A16-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vishva007/d3-edge-W4A16-AutoRound with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="Vishva007/d3-edge-W4A16-AutoRound", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Vishva007/d3-edge-W4A16-AutoRound", trust_remote_code=True) model = AutoModelForMultimodalLM.from_pretrained("Vishva007/d3-edge-W4A16-AutoRound", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,601 Bytes
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"bits": 4,
"data_type": "int",
"group_size": 32,
"sym": true,
"iters": 1200,
"autoround_version": "0.16.0",
"block_name_to_quantize": "model.language_model.layers",
"quant_method": "auto-round",
"packing_format": "auto_round:auto_gptq",
"extra_config": {
"model.language_model.layers.0.linear_attn.in_proj_b": {
"bits": 16,
"data_type": "fp"
},
"model.language_model.layers.0.linear_attn.in_proj_a": {
"bits": 16,
"data_type": "fp"
},
"model.language_model.layers.1.linear_attn.in_proj_b": {
"bits": 16,
"data_type": "fp"
},
"model.language_model.layers.1.linear_attn.in_proj_a": {
"bits": 16,
"data_type": "fp"
},
"model.language_model.layers.3.linear_attn.in_proj_b": {
"bits": 16,
"data_type": "fp"
},
"model.language_model.layers.3.linear_attn.in_proj_a": {
"bits": 16,
"data_type": "fp"
},
"model.language_model.layers.4.linear_attn.in_proj_b": {
"bits": 16,
"data_type": "fp"
},
"model.language_model.layers.4.linear_attn.in_proj_a": {
"bits": 16,
"data_type": "fp"
},
"model.language_model.layers.6.linear_attn.in_proj_b": {
"bits": 16,
"data_type": "fp"
},
"model.language_model.layers.6.linear_attn.in_proj_a": {
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"data_type": "fp"
},
"model.language_model.layers.7.linear_attn.in_proj_b": {
"bits": 16,
"data_type": "fp"
},
"model.language_model.layers.7.linear_attn.in_proj_a": {
"bits": 16,
"data_type": "fp"
}
}
} |