Instructions to use taoki/Qwen2.5-Coder-7B-Instruct_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use taoki/Qwen2.5-Coder-7B-Instruct_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="taoki/Qwen2.5-Coder-7B-Instruct_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("taoki/Qwen2.5-Coder-7B-Instruct_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python")
model = AutoModelForCausalLM.from_pretrained("taoki/Qwen2.5-Coder-7B-Instruct_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use taoki/Qwen2.5-Coder-7B-Instruct_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "taoki/Qwen2.5-Coder-7B-Instruct_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "taoki/Qwen2.5-Coder-7B-Instruct_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/taoki/Qwen2.5-Coder-7B-Instruct_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python

SGLang

How to use taoki/Qwen2.5-Coder-7B-Instruct_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "taoki/Qwen2.5-Coder-7B-Instruct_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "taoki/Qwen2.5-Coder-7B-Instruct_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "taoki/Qwen2.5-Coder-7B-Instruct_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "taoki/Qwen2.5-Coder-7B-Instruct_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use taoki/Qwen2.5-Coder-7B-Instruct_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python with Docker Model Runner:
```
docker model run hf.co/taoki/Qwen2.5-Coder-7B-Instruct_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python
```

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|>

Downloads last month: 3

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for taoki/Qwen2.5-Coder-7B-Instruct_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Coder-7B

Finetuned

Qwen/Qwen2.5-Coder-7B-Instruct

Finetuned

(361)

this model

taoki
/

Qwen2.5-Coder-7B-Instruct_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python

Uploaded model

Usage

Output

Model tree for taoki/Qwen2.5-Coder-7B-Instruct_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python

Datasets used to train taoki/Qwen2.5-Coder-7B-Instruct_lora_jmultiwoz-dolly-amenokaku-alpaca_jp_python