Text Generation
Transformers
Safetensors
English
llama
text-generation-inference
unsloth
trl
4-bit precision
bitsandbytes
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
- Transformers
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
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- 東京大学 松尾・岩澤研究室主催の大規模言語モデルDeep Learning応用講座 2024|Fall を受講することで本モデルが作製できた。同講座に関係する方々並びに同講座を受講された方々に心より深謝する。
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- 東京大学 松尾・岩澤研究室主催の大規模言語モデルDeep Learning応用講座 2024|Fall を受講することで本モデルが作製できた。同講座に関係する方々並びに同講座を受講された方々に心より深謝する。
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