metadata

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

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|>