metadata

library_name: transformers
license: mit
language:
  - ja
base_model:
  - cyberagent/DeepSeek-R1-Distill-Qwen-14B-Japanese

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.