Update README.md

README.md

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 ---
 library_name: transformers
-tags: []
+tags:
+  - japanese
+  - instruction-following
+  - lora
+  - causal-lm
+license: cc-by-nc-sa-4.0
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+# Model Card for Fine-Tuned LLM-JP-3-13B
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+This model is a Fine-Tuned Version of the `llm-jp-3-13b` base model for Japanese text tasks. Fine-tuning was performed using the **Ichikara Instruction Dataset** for supervised instruction-following tasks. It enables the model to generate structured responses based on specific prompts.
 
-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:** Omnicampus User  
+- **Model type:** Causal Language Model (LoRA Fine-Tuning)  
+- **Language(s) (NLP):** Japanese  
+- **License:** CC-BY-NC-SA-4.0 (inherits from training data license)  
+- **Finetuned from model:** `llm-jp/llm-jp-3-13b`  
 
-- **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
 
-### Model Sources [optional]
+- **Repository:** [Hugging Face Link](https://huggingface.co/Hiro00099/llm-jp-3-13b-finetune)  
+- **Dataset:** [Ichikara Instruction Dataset](https://liat-aip.sakura.ne.jp/wp/llmのための日本語インストラクションデータ作成/llmのための日本語インストラクションデータ-公開/)  
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+---
 
 ## 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 model is designed for Japanese instruction-following tasks, such as:
+- Summarization
+- Question-answering
+- Dialogue generation
 
-<!-- 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]
+### Downstream Use
+The model can be further fine-tuned for:
+- Machine Translation
+- Sentiment Analysis
+- Task-Specific Question-Answering  
 
 ### Out-of-Scope Use
+This model may not perform well on:
+- Non-Japanese languages
+- Highly technical or niche domains without additional fine-tuning
+- Malicious or harmful content generation  
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+---
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+While the model was fine-tuned on curated Japanese instruction data, biases in the training data may still persist:
+- Potential biases related to Japanese cultural norms and values
+- Limited performance on underrepresented topics or linguistic patterns
 
 ### Recommendations
+Users should evaluate the outputs, particularly for applications involving sensitive data or domains.
 
-<!-- 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]
-
-## 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. -->
-
-[More Information Needed]
-
-#### 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
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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]
-
-<!-- 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]
+Below is an example for generating predictions using the fine-tuned model:
 
-[More Information Needed]
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-## Model Card Authors [optional]
+model_id = "Hiro00099/llm-jp-3-13b-finetune"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
 
-[More Information Needed]
+input_prompt = """### 指示
+野球選手が今シーズン活躍するために取り組むべき5つのことを教えてください。
 
-## Model Card Contact
+### 回答"""
+input_ids = tokenizer(input_prompt, return_tensors="pt").input_ids
+output_ids = model.generate(input_ids, max_new_tokens=100)
+output = tokenizer.decode(output_ids[0], skip_special_tokens=True)
 
-[More Information Needed]
+print(output)