Create README.md

README.md

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+---
+language:
+- ja
+license: apache-2.0
+tags:
+- text-generation-inference
+- code
+- transformers
+- trl
+- qwen2
+datasets:
+- sakusakumura/databricks-dolly-15k-ja-scored
+- nu-dialogue/jmultiwoz
+- kunishou/amenokaku-code-instruct
+- HachiML/alpaca_jp_python
+base_model: Qwen/Qwen2.5-Coder-7B-Instruct
+---
+
+# Uploaded  model
+
+- **Developed by:** taoki
+- **License:** apache-2.0
+- **Finetuned from model :** Qwen/Qwen2.5-Coder-7B-Instruct
+
+
+# Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "taoki/Qwen2.5-Coder-7B-Instruct_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "OpenCVを用いて定点カメラから画像を保存するコードを示してください。"
+messages = [
+    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=False)[0]
+print(response)
+```
+
+# Output
+````
+<|im_start|>system
+You are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>
+<|im_start|>user
+クイックソートのアルゴリズムを書いて<|im_end|>
+<|im_start|>assistant
+```python
+def quicksort(arr):
+    if len(arr) <= 1:
+        return arr
+    pivot = arr[len(arr) // 2]
+    left = [x for x in arr if x < pivot]
+    middle = [x for x in arr if x == pivot]
+    right = [x for x in arr if x > pivot]
+    return quicksort(left) + middle + quicksort(right)
+
+print(quicksort([3,6,8,10,1,2,1]))
+```<|im_end|>
+````