Instructions to use junga/hjys_LLM_final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use junga/hjys_LLM_final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="junga/hjys_LLM_final")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("junga/hjys_LLM_final") model = AutoModelForCausalLM.from_pretrained("junga/hjys_LLM_final") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use junga/hjys_LLM_final with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "junga/hjys_LLM_final" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "junga/hjys_LLM_final", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/junga/hjys_LLM_final
- SGLang
How to use junga/hjys_LLM_final 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 "junga/hjys_LLM_final" \ --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": "junga/hjys_LLM_final", "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 "junga/hjys_LLM_final" \ --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": "junga/hjys_LLM_final", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use junga/hjys_LLM_final with Docker Model Runner:
docker model run hf.co/junga/hjys_LLM_final
Update README.md
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README.md
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@@ -25,7 +25,7 @@ The primary goal of this model is to improve scores on the ko-CommonGen V2 task,
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- **Hidden Size**: 2,048
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- **FFN Size**: 5,632
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- **Maximum Length**: 4,096 tokens
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- **Training Time**:
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#### Limitations and Ethical Considerations
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Like other LLMs, the 42dot LLM-SFT-1.3B may produce hallucinated or biased content. Users should be aware of these limitations and take appropriate actions.
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- **Hidden Size**: 2,048
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- **FFN Size**: 5,632
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- **Maximum Length**: 4,096 tokens
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- **Training Time**: 5 GPU hours on NVIDIA A100 (Google Colab Pro+)
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#### Limitations and Ethical Considerations
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Like other LLMs, the 42dot LLM-SFT-1.3B may produce hallucinated or biased content. Users should be aware of these limitations and take appropriate actions.
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