original model weblab-10b-instruction-sft
This is 4bit GPTQ Version.
The size is smaller and the execution speed is faster, but the inference performance may be a little worse.
Benchmark results are in progress. I will upload it at a later date.
sample code ''' pip install auto-gptq '''
''' from transformers import AutoTokenizer from auto_gptq import AutoGPTQForCausalLM
quantized_model_dir = "dahara1/weblab-10b-instruction-sft-GPTQ" model_basename = "gptq_model-4bit-128g"
tokenizer = AutoTokenizer.from_pretrained(quantized_model_dir)
model = AutoGPTQForCausalLM.from_quantized( quantized_model_dir, model_basename=model_basename, use_safetensors=True, device="cuda:0")
prompt = "スタジオジブリの作品を５つ教えてください" prompt_template = f"### Instruction: {prompt}\n### Response:"
tokens = tokenizer(prompt_template, return_tensors="pt").to("cuda:0").input_ids output = model.generate(input_ids=tokens, max_new_tokens=100, do_sample=True, temperature=0.8) print(tokenizer.decode(output[0])) '''
See Also https://github.com/PanQiWei/AutoGPTQ/blob/main/docs/tutorial/01-Quick-Start.md