Instructions to use VHemanthC/model_3_st with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VHemanthC/model_3_st with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="VHemanthC/model_3_st")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("VHemanthC/model_3_st") model = AutoModelForCausalLM.from_pretrained("VHemanthC/model_3_st", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use VHemanthC/model_3_st with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "VHemanthC/model_3_st" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "VHemanthC/model_3_st", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/VHemanthC/model_3_st
- SGLang
How to use VHemanthC/model_3_st 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 "VHemanthC/model_3_st" \ --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": "VHemanthC/model_3_st", "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 "VHemanthC/model_3_st" \ --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": "VHemanthC/model_3_st", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Unsloth Desktop
- Docker Model Runner
How to use VHemanthC/model_3_st with Docker Model Runner:
docker model run hf.co/VHemanthC/model_3_st
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Download README.md from VHemanthC/model_3_st: direct link, hf CLI and curl.
- Browser
- Download file 596 Bytes
-
https://huggingface.co/VHemanthC/model_3_st/resolve/main/README.md
- Command line
-
hf download hf://VHemanthC/model_3_st/README.md
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curl -L -o README.md https://huggingface.co/VHemanthC/model_3_st/resolve/main/README.md
596 Bytes
| base_model: unsloth/meta-llama-3.1-8b-bnb-4bit | |
| tags: | |
| - text-generation-inference | |
| - transformers | |
| - unsloth | |
| - llama | |
| - trl | |
| - sft | |
| license: apache-2.0 | |
| language: | |
| - en | |
| # Uploaded model | |
| - **Developed by:** Batman-VH | |
| - **License:** apache-2.0 | |
| - **Finetuned from model :** unsloth/meta-llama-3.1-8b-bnb-4bit | |
| This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. | |
| [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) | |