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 24, 2024

Commit

6c880da

verified ·

1 Parent(s): 0855753

Update README.md

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Files changed (1) hide show

README.md +19 -0

README.md CHANGED Viewed

@@ -21,6 +21,25 @@ This llama model was trained 2x faster with [Unsloth](https://github.com/unsloth
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
 # ■ kazuHF/llm-jp-3-13b-it2_loraの概要

 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+# ■ kazuHF/llm-jp-3-13b-it2_loraによる推論方法
+1. 環境設定
+- 以下の説明はGoogle Colaboratory上での利用を想定している。事前にhardware acceleratorでGPUを選択しておく。
+```bash
+# Googleドライブに接続
+from google.colab import drive
+drive.mount('/content/drive')
+# 接続しているGPUの種類
+!nvidia-smi
+# 必要なライブラリのインストール
+!pip install unsloth -q
+!pip uninstall unsloth -y && pip install --upgrade --no-cache-dir "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git" -q
+!pip install -U torch -q
+!pip install -U peft -q
+```
 # ■ kazuHF/llm-jp-3-13b-it2_loraの概要