初始化项目，由ModelHub XC社区提供模型

Model: dahara1/llama3.1-8b-Instruct-awq
Source: Original Platform

README.md

@@ -0,0 +1,63 @@
+---
+language:
+- en
+- ja
+---
+
+
+llama3.1-8bのAWQ量子化版です。  
+4GB超のGPUメモリがあれば高速に動かす事ができます。  
+
+This is the AWQ quantization version of llama3.1-8b.  
+If you have more than 4GB of GPU memory, you can run it at high speed.　　
+
+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  
+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).  
+
+
+## セットアップ(setup)
+```
+pip install transformers==4.43.3 autoawq==0.2.6 accelerate==0.33.0
+```
+
+
+## サンプルスクリプト(sample script)
+```
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, AwqConfig
+
+model_id = "dahara1/llama3.1-8b-Instruct-awq"
+
+quantization_config = AwqConfig(
+    bits=4,
+    fuse_max_seq_len=512, # Note: Update this as per your use-case
+    do_fuse=True,
+)
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+  model_id,
+  torch_dtype=torch.float16,
+  low_cpu_mem_usage=True,
+  device_map="auto",
+  quantization_config=quantization_config
+)
+
+prompt = [
+  {"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"},
+  {"role": "user", "content": "ディープラーニングとは何ですか？"},
+]
+inputs = tokenizer.apply_chat_template(
+  prompt,
+  tokenize=True,
+  add_generation_prompt=True,
+  return_tensors="pt",
+  return_dict=True,
+).to("cuda")
+
+outputs = model.generate(**inputs, do_sample=True, max_new_tokens=256)
+print(tokenizer.batch_decode(outputs[:, inputs['input_ids'].shape[1]:], skip_special_tokens=True)[0])
+
+```
+
+![kaizoku](kaizoku.png)