64 lines
2.0 KiB
Markdown
64 lines
2.0 KiB
Markdown
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---
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language:
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- en
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- ja
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---
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llama3.1-8bのAWQ量子化版です。
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4GB超のGPUメモリがあれば高速に動かす事ができます。
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This is the AWQ quantization version of llama3.1-8b.
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If you have more than 4GB of GPU memory, you can run it at high speed.
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量子化時に日本語と中国語を多めに使っているため、[hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4](https://huggingface.co/hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4)より日本語データを使って計測したPerplexityが良い事がわかっています
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Because Japanese and Chinese are used a lot during quantization, It is known that Perplexity measured using Japanese data is better than [hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4](https://huggingface.co/hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4).
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## セットアップ(setup)
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```
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pip install transformers==4.43.3 autoawq==0.2.6 accelerate==0.33.0
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```
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## サンプルスクリプト(sample script)
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```
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, AwqConfig
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model_id = "dahara1/llama3.1-8b-Instruct-awq"
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quantization_config = AwqConfig(
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bits=4,
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fuse_max_seq_len=512, # Note: Update this as per your use-case
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do_fuse=True,
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)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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low_cpu_mem_usage=True,
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device_map="auto",
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quantization_config=quantization_config
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)
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prompt = [
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{"role": "system", "content": "あなたは親切で役に立つアシスタントです。常に海賊のように返答してください"},
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{"role": "user", "content": "ディープラーニングとは何ですか？"},
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]
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inputs = tokenizer.apply_chat_template(
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prompt,
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tokenize=True,
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add_generation_prompt=True,
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return_tensors="pt",
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return_dict=True,
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).to("cuda")
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outputs = model.generate(**inputs, do_sample=True, max_new_tokens=256)
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print(tokenizer.batch_decode(outputs[:, inputs['input_ids'].shape[1]:], skip_special_tokens=True)[0])
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```
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