README.md

---
library_name: transformers
datasets:
- llm-jp/magpie-sft-v1.0
base_model:
- google/gemma-2-9b
license: gemma
language:
- ja
- en
---

# Model Card for Model ID

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## Model Details

### Model Description

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このモデルはgemma-2-9bをbitsandbytesで4bit量子化し、llm-jp/magpie-sft-v0.1を用いQloraでInstruction Turnnigしたモデルです。
loraアダプターはmssfj/gemma-2-9b-4bit-magpieになります。

以下のチャットテンプレートを定義しています。
<bos>{%- for message in messages %}
<start_of_turn>{{ message.role }}: {{ message.content }}<end_of_turn>
{%- endfor %}{% if add_generation_prompt %}
<start_of_turn>assistant: {% endif %}<eos>

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]
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- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

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## Uses

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使用方法は以下です。
```
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch
from peft import PeftModel, PeftConfig

model_name = "mssfj/gemma-2-9b-bnb-4bit-chat-template"
lora_weight = "mssfj/gemma-2-9b-4bit-magpie"

quantization_config = BitsAndBytesConfig(
    load_in_4bit=False,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=False
)

base_model = AutoModelForCausalLM.from_pretrained(
    model_name,
    quantization_config=quantization_config,
    device_map="auto"
    )

model = PeftModel.from_pretrained(base_model, lora_weight)

tokenizer = AutoTokenizer.from_pretrained(model_name)

input="""日本で一番高い山は？
"""

messages = [
    {"role": "system", "content": """あなたは誠実で優秀な日本人のアシスタントです。あなたはユーザと日本語で会話しています。アシスタントは以下の原則を忠実に守り丁寧に回答します。
    - 日本語で簡潔に回答する
    - 回答は必ず完結した文で終える
    - 質問の文脈に沿った自然な応答をする
    """},
    {"role": "user", "content": input},
]

input_ids = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(
    input_ids,
    max_new_tokens=512,
    temperature=0.2,
    do_sample=True,
    eos_token_id=tokenizer.eos_token_id,
    pad_token_id=tokenizer.pad_token_id,
    early_stopping=True,
)

response = tokenizer.decode(outputs[0][input_ids.shape[1]:], skip_special_tokens=True)
```
### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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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.

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

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#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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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).

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## Technical Specifications [optional]

### Model Architecture and Objective

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#### Hardware

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#### Software

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## Citation [optional]

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## Glossary [optional]

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