Instructions to use kazuHF/llm-jp-3-13b-it2_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kazuHF/llm-jp-3-13b-it2_lora with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="kazuHF/llm-jp-3-13b-it2_lora")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kazuHF/llm-jp-3-13b-it2_lora")
model = AutoModelForCausalLM.from_pretrained("kazuHF/llm-jp-3-13b-it2_lora")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kazuHF/llm-jp-3-13b-it2_lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kazuHF/llm-jp-3-13b-it2_lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kazuHF/llm-jp-3-13b-it2_lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/kazuHF/llm-jp-3-13b-it2_lora

SGLang

How to use kazuHF/llm-jp-3-13b-it2_lora with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "kazuHF/llm-jp-3-13b-it2_lora" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kazuHF/llm-jp-3-13b-it2_lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "kazuHF/llm-jp-3-13b-it2_lora" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kazuHF/llm-jp-3-13b-it2_lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio

How to use kazuHF/llm-jp-3-13b-it2_lora with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for kazuHF/llm-jp-3-13b-it2_lora to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for kazuHF/llm-jp-3-13b-it2_lora to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kazuHF/llm-jp-3-13b-it2_lora to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="kazuHF/llm-jp-3-13b-it2_lora",
    max_seq_length=2048,
)

Docker Model Runner
How to use kazuHF/llm-jp-3-13b-it2_lora with Docker Model Runner:
```
docker model run hf.co/kazuHF/llm-jp-3-13b-it2_lora
```

kazuHF commited on Dec 17, 2024

Commit

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@@ -39,7 +39,7 @@ This llama model was trained 2x faster with [Unsloth](https://github.com/unsloth
 - 推論による出力のkeyは “task_id”, “input”, “output”
 4. 推論方法
-- Hugging FaceのIDとして、model_id = "llm-jp/llm-jp-3-13b”, adapter_id = "kazuHF/llm-jp-3-13b-it2_lora" と指定し、FastLanguageModel.from_pretrained( … model_id … )
 で元のモデルをロードする。
 - そして model = PeftModel.from_pretrained( … adaptor_id … )によって元のモデルとLoRAのアダプターを結合し、そのモデルのモードを FastLanguageModel.for_inference(model) によって推論モードに変更する。
 - 入力を”””###\n 指示 入力 \n### 回答\n”””の形式にしてトークン化し、model.generate( “input_ids”: …, “attention_mask”: …, …) によってpredictionを行い、それをdecodeして出力とする。

 - 推論による出力のkeyは “task_id”, “input”, “output”
 4. 推論方法
+- Hugging FaceのIDは model_id = "llm-jp/llm-jp-3-13b”, adapter_id = "kazuHF/llm-jp-3-13b-it2_lora" と指定し、FastLanguageModel.from_pretrained( … model_id … )
 で元のモデルをロードする。
 - そして model = PeftModel.from_pretrained( … adaptor_id … )によって元のモデルとLoRAのアダプターを結合し、そのモデルのモードを FastLanguageModel.for_inference(model) によって推論モードに変更する。
 - 入力を”””###\n 指示 入力 \n### 回答\n”””の形式にしてトークン化し、model.generate( “input_ids”: …, “attention_mask”: …, …) によってpredictionを行い、それをdecodeして出力とする。