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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 = "クイックソートのアルゴリズムを書いて"
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|>
```` |