Feature Extraction
Transformers
Safetensors
internlm2
internlm 2.5
gptq
4bit
gptqmodel
custom_code
4-bit precision
Instructions to use ModelCloud/internlm-2.5-7b-gptq-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ModelCloud/internlm-2.5-7b-gptq-4bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ModelCloud/internlm-2.5-7b-gptq-4bit", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ModelCloud/internlm-2.5-7b-gptq-4bit", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
This model has been quantized using GPTQModel.
- bits: 4
- group_size: 128
- desc_act: true
- static_groups: false
- sym: true
- lm_head: false
- damp_percent: 0.01
- true_sequential: true
- model_name_or_path: ""
- model_file_base_name: "model"
- quant_method: "gptq"
- checkpoint_format: "gptq"
- meta:
- quantizer: "gptqmodel:0.9.5"
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