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inference: false

weblab-10b-instruction-sft-GPTQ

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.

sample code

At least one GPU is currently required due to a limitation of the Accelerate library.
So this model cannot be run with the huggingface space free version.

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"### 指示: {prompt}\n\n### 応答:"

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]))

Other documents

https://github.com/PanQiWei/AutoGPTQ/blob/main/docs/tutorial/01-Quick-Start.md

Original Authors

Takeshi Kojima

Benchmark

The results below are preliminary. The blank part is under measurement.
Also, the score may change as a result of tuning after this.

  • Japanese benchmark

    • We used Stability-AI/lm-evaluation-harness + gptq patch for evaluation.
    • The 4-task average accuracy is based on results of JCommonsenseQA-1.1, JNLI-1.1, MARC-ja-1.1, and JSQuAD-1.1.
    • model loading is performed with gptq_use_triton=True, and evaluation is performed with template version 0.3 using the few-shot in-context learning.
    • The number of few-shots is 3,3,3,2.
    Model Average JCommonsenseQA JNLI MARC-ja JSQuAD
    weblab-10b-instruction-sft 78.78 74.35 65.65 96.06 79.04
    weblab-10b 66.38 65.86 54.19 84.49 60.98
    weblab-10b-instruction-sft-GPTQ - 74.53 41.70 - 72.69