---
license: apache-2.0
language:
- en
- ja
programming_language:
- C
- C++
- C#
- Go
- Java
- JavaScript
- Lua
- PHP
- Python
- Ruby
- Rust
- Scala
- TypeScript
pipeline_tag: text-generation
library_name: transformers
inference: false
tags:
- mlx
base_model: llm-jp/llm-jp-3-13b
---

# niryuu/llm-jp-3-13b-ha

The Model [niryuu/llm-jp-3-13b-ha](https://huggingface.co/niryuu/llm-jp-3-13b-ha) was
converted to MLX format from [llm-jp/llm-jp-3-13b](https://huggingface.co/llm-jp/llm-jp-3-13b)
using mlx-lm version **0.20.1**.

It remains compatibility with HF Transformers.

And then fine-tuned using LoRA with dataset:
- h: kanhatakeyama/ramdom-to-fixed-multiturn-Calm3
- a: Aratako/Magpie-Tanuki-8B-97k

## Use for Evaluation

```python
# -*- coding: utf-8 -*-

!pip install -U bitsandbytes
!pip install -U transformers
!pip install -U accelerate
!pip install -U datasets
!pip install -U peft

from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
)
from peft import PeftModel
import torch
from tqdm import tqdm
import json

# Hugging Faceで取得したTokenをこちらに貼る。
HF_TOKEN = "dummy"

model_id = "niryuu/llm-jp-3-13b-ha"

# QLoRA config
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
)

# Load model
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    quantization_config=bnb_config,
    device_map="auto",
    token = HF_TOKEN
)

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True, token = HF_TOKEN)

# load dataset
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 = ""

results = []
for data in tqdm(datasets):

  input = data["input"]
  token_ids = tokenizer.apply_chat_template([{"role": "user", "content": input}], tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)

  outputs = model.generate(input_ids, max_new_tokens=2048, do_sample=False, repetition_penalty=1.2,)
  output = tokenizer.decode(outputs[0][token_ids.size(1) :], skip_special_tokens=True)

  results.append({"task_id": data["task_id"], "input": input, "output": output})

# save outputs
import re
jsonl_id = re.sub(".*/", "", model_id)
with open(f"./{jsonl_id}-outputs.jsonl", 'w', encoding='utf-8') as f:
    for result in results:
        json.dump(result, f, ensure_ascii=False)  # ensure_ascii=False for handling non-ASCII characters
        f.write('\n')
```

## Use with mlx

```bash
pip install mlx-lm
```

```python
from mlx_lm import load, generate

model, tokenizer = load("niryuu/llm-jp-3-13b-ha")

prompt="hello"

if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)
```