Update README.md

README.md

@@ -1,142 +1,98 @@
 ---
 library_name: transformers
-tags: []
+license: mit
+language:
+- ja
+base_model:
+- cyberagent/DeepSeek-R1-Distill-Qwen-14B-Japanese
 ---
 
 # Model Card for Model ID
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-
-
 ## Model Details
 
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
+This model is finetuned on conversational data for chat in Japanese.
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- Developed by: [flypg](https://huggingface.co/flypg)
+- Model type: Causal Lanuage Model
+- Language(s) (NLP): Japanese
+- License: MIT
+- Finetuned from model:cyberagent/DeepSeek-R1-Distill-Qwen-32B-Japanese
 
 ## Uses
-
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
 ### Direct Use
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+The model can be directly used for casual conversation in Japanese.
 
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
+- 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.
+
+```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 Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
+### Training Procedure & Hyperparameters
 
-[More Information Needed]
+  - 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
 
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
 
 ## Environmental Impact
 
@@ -144,56 +100,25 @@ Use the code below to get started with the model.
 
 Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
+- Hardware Type: Nvdia A100 PCle
+- Hours used: 5
+- Cloud Provider: Private Infrastructure
+- Compute Region: US-central
+- Carbon Emitted: 320g CO2 eq.
 
+## Citation
 <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+If you use this model in your research or work, please cite it using the following BibTeX entry:
+
+```bibtex
+@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.