---
base_model: llm-jp/llm-jp-3-13b
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---

# Uploaded model

- **Developed by:** kmagai
- **License:** apache-2.0
- **Finetuned from model:** llm-jp/llm-jp-3-13b

This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

## JSONL Output Process

### Model Inference Setup

```python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch
from tqdm import tqdm
import json

# QLoRA config for 4-bit quantization
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=False,
)

# Load model and tokenizer
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)
```

### Input Data Processing

The script reads input data from a JSONL file (`elyza-tasks-100-TV_0.jsonl`). Each line contains a JSON object with task information:

```python
datasets = []
with open("./elyza-tasks-100-TV_0.jsonl", "r") as f:
    item = ""
    for line in f:
        line = line.strip()
        item += line
        if item.endswith("}"):
            datasets.append(json.loads(item))
            item = ""
```

### Generation Process

For each input in the dataset:

1. Format the prompt with instruction template
2. Tokenize the input
3. Generate response using the model
4. Decode the output
5. Create result object with task_id and output

```python
results = []
for data in tqdm(datasets):
    input = data["input"]
    prompt = f"""### Instruction
    {input}
    ### Response:
    """
    
    tokenized_input = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt").to(model.device)
    with torch.no_grad():
        outputs = model.generate(
            tokenized_input,
            max_new_tokens=100,
            do_sample=False,
            repetition_penalty=1.2
        )[0]
    output = tokenizer.decode(outputs[tokenized_input.size(1):], skip_special_tokens=True)
    
    results.append({"task_id": data["task_id"], "input": input, "output": output})
```

### Generation Parameters

- `max_new_tokens=100`: Maximum number of tokens to generate
- `do_sample=False`: Deterministic generation (same output every time)
- `repetition_penalty=1.2`: Penalize repetition in generated text

### Output Format

The generated responses are saved in a JSONL file with the following format:

```json
{"task_id": "task_1", "input": "input text", "output": "generated response"}
```

Required fields:
- `task_id`: Unique identifier for the task
- `output`: Response generated by the model

Optional fields:
- `input`: Input text (can be omitted in submission)

## Training Data Format

The training data should be provided in JSONL (JSON Lines) format, where each line represents a single JSON object containing the following fields:

```json
{
    "instruction": "Task instruction text",
    "input": "Input text (optional)",
    "output": "Expected output text"
}
```

### Fields Description

- `instruction`: Task instruction that tells the model what to do
- `input`: (Optional) Input text that provides specific context for the instruction
- `output`: Expected output that represents the ideal response

### Example

```json
{"instruction": "以下の文章を要約してください。", "input": "人工知能（AI）は、人間の知能を模倣し、学習、推論、判断などを行うコンピュータシステムです。近年、機械学習や深層学習の発展により、画像認識、自然言語処理、ゲームなど様々な分野で人間に匹敵する、あるいは人間を超える性能を示しています。", "output": "AIは人間の知能を模倣するコンピュータシステムで、機械学習の発展により多くの分野で高い性能を示している。"}
{"instruction": "次の英文を日本語に翻訳してください。", "input": "Artificial Intelligence is transforming the way we live and work.", "output": "人工知能は私たちの生活と仕事の仕方を変革しています。"}