README.md

---
base_model: llm-jp/llm-jp-3-13b
library_name: peft
license: apache-2.0
tags:
- unsloth
- Transformers
- trl
---

# Model Card for Model ID

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## Model Details

### Model Description

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

### Model Sources [optional]

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- **Paper [optional]:** [More Information Needed]
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## Uses

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### 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.
```python
from transformers import AutoModelForCausalLM,AutoTokenizer,BitsAndBytesConfig
from peft import PeftModel,PeftConfig
import torch

HF_TOKEN = "your token"
model_name = "llm-jp/llm-jp-3-13b"
adapter_name = "yossy0125/llm-jp-3-13b-it_lora/"

#QLoRaの量子化に合わせる
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type= "nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,

)

#BaseModel
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    quantization_config = bnb_config,
    device_map="auto",
    token=HF_TOKEN
)
tokenizer = AutoTokenizer.from_pretrained(model_name,trust_remote_code=True,token=HF_TOKEN)

#adapterをBaseModelに統合
model = PeftModel.from_pretrained(model,adapter_name,token=HF_TOKEN)

input = "カレーの具材は何ですか？"
prompt = f"""以下はタスクを説明する指示です。
要求を適切に満たす応答を出力しなさい。

### 指示:{input}

### 応答:
"""

tokenized_input = tokenizer.encode(prompt,add_special_tokens=False,return_tensors="pt").to(model.device)
attention_mask = torch.ones_like(tokenized_input)
outputs = None
with torch.no_grad():
    outputs = model.generate(
        tokenized_input,
        attention_mask=attention_mask,
        max_new_tokens=2048, #生成するトークン数
        do_sample=False,
        repetition_penalty=1.2,
        pad_token_id=tokenizer.eos_token_id
    )[0]
output = tokenizer.decode(outputs[tokenized_input.size(1):],skip_special_tokens=True)
```

[More Information Needed]

## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### 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]

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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]

<!-- Relevant interpretability work for the model goes here -->

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## 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

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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### Framework versions

- PEFT 0.13.2