Instructions to use flypg/DeepSeek-R1-Distill-Qwen-14B-Japanese-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use flypg/DeepSeek-R1-Distill-Qwen-14B-Japanese-chat with Transformers:

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

pipe = pipeline("text-generation", model="flypg/DeepSeek-R1-Distill-Qwen-14B-Japanese-chat")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("flypg/DeepSeek-R1-Distill-Qwen-14B-Japanese-chat")
model = AutoModelForCausalLM.from_pretrained("flypg/DeepSeek-R1-Distill-Qwen-14B-Japanese-chat")
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 Settings

vLLM

How to use flypg/DeepSeek-R1-Distill-Qwen-14B-Japanese-chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "flypg/DeepSeek-R1-Distill-Qwen-14B-Japanese-chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "flypg/DeepSeek-R1-Distill-Qwen-14B-Japanese-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/flypg/DeepSeek-R1-Distill-Qwen-14B-Japanese-chat

SGLang

How to use flypg/DeepSeek-R1-Distill-Qwen-14B-Japanese-chat 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 "flypg/DeepSeek-R1-Distill-Qwen-14B-Japanese-chat" \
    --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": "flypg/DeepSeek-R1-Distill-Qwen-14B-Japanese-chat",
		"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 "flypg/DeepSeek-R1-Distill-Qwen-14B-Japanese-chat" \
        --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": "flypg/DeepSeek-R1-Distill-Qwen-14B-Japanese-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use flypg/DeepSeek-R1-Distill-Qwen-14B-Japanese-chat with Docker Model Runner:
```
docker model run hf.co/flypg/DeepSeek-R1-Distill-Qwen-14B-Japanese-chat
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for Model ID

Model Details

Model Description

This model is finetuned on conversational data for chat in Japanese.

Developed by: flypg
Model type: Causal Lanuage Model
Language(s) (NLP): Japanese
License: MIT
Finetuned from model:cyberagent/DeepSeek-R1-Distill-Qwen-32B-Japanese

Uses

Direct Use

The model can be directly used for casual conversation in Japanese.

Bias, Risks, and Limitations

Small Dataset: the model is finetuned on relatively small dataset (<1000 conversations). The model may overfit or produce repetitive answers.
Bias / Toxicity: As with any LLM, it could generate offensive or biased outputs in certain contexts.
Limitations: Please take your only risk using the model beyond casual converstaion.

Get Started with the Model

Below is a minimal example of how to load and use this model for inference in Python.

  import torch
  from transformers import AutoModelForCausalLM, AutoTokenizer
  
  model_name = "flypg/DeepSeek-R1-Distill-Qwen-14B-Japanese-chat"
  
  tokenizer = AutoTokenizer.from_pretrained(
      model_name,
  )
  
  model = AutoModelForCausalLM.from_pretrained(
      model_name,
      trust_remote_code=True,
      device_map="auto",
      torch_dtype=torch.float16
  )
  model.eval()
  
  prompt = "your prompt"
  inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
  
  with torch.no_grad():
      output_ids = model.generate(
          **inputs,
          max_new_tokens=100,
          temperature=0.7,
          top_p=0.9,
          do_sample=True,
          pad_token_id=tokenizer.eos_token_id
      )
  
  response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
  print(response)

Training Details

Training Procedure & Hyperparameters

Fine-Tuning Method: LoRA
Framework & Tools: Hugging Face Transformers PEFT
Hyperparameters:
- Learning rate: 1e-5
- Batch size: 2 (with gradient accumulation)
- Num epochs: 3
- Training regime: fp16 mixed precision

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: Nvdia A100 PCle
Hours used: 5
Cloud Provider: Private Infrastructure
Compute Region: US-central
Carbon Emitted: 320g CO2 eq.

Citation

If you use this model in your research or work, please cite it using the following BibTeX entry:

@misc{DeepSeek R1-Qwen Model for Chat in Japenese,
  title={DeepSeek-R1-Distill-Qwen-14B-Japanese-chat: A Fine-Tuned Qwen-based Model for Chat in Japenese},
  author={flypg},
  year={2025},
  howpublished={\url{https://huggingface.co/flypg/DeepSeek-R1-Distill-Qwen-14B-Japanese-chat}},
  note={Accessed: YYYY-MM-DD}
}

## Contact
[kenkun091](https://github.com/kenkun091)
Please feel free to open an issue.