metadata

inference: false
language:
  - ja

weblab-10b-instruction-sft-GPTQ

original model weblab-10b-instruction-sft which is a Japanese-centric multilingual GPT-NeoX model of 10 billion parameters.

This model is A quantized(miniaturized) version of the original model(21.42GB).

There are currently two well-known quantization methods.
(1)GPTQ model(This model. 6.3 GB)
The size is smaller and the execution speed is faster, but the inference performance may be a little worse than original model.
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.
You need autoGPTQ library to use this model.

(2)gguf model(matsuolab-weblab-10b-instruction-sft-gguf 6.03GB) created by mmnga.
You can use gguf model with llama.cpp at cpu only machine.
But maybe gguf model little bit slower then GPTQ especialy long text.

sample code

Try it on Google Colab. Under development

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_text = "スタジオジブリの作品を５つ教えてください"
prompt_template = f'以下は、タスクを説明する指示です。要求を適切に満たす応答を書きなさい。\n\n### 指示:\n{prompt_text}\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]))

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

dahara1
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weblab-10b-instruction-sft-GPTQ

weblab-10b-instruction-sft-GPTQ

sample code

Other documents

Benchmark