Instructions to use nikhil07prakash/float-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nikhil07prakash/float-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nikhil07prakash/float-7b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("nikhil07prakash/float-7b") model = AutoModelForCausalLM.from_pretrained("nikhil07prakash/float-7b") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use nikhil07prakash/float-7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nikhil07prakash/float-7b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nikhil07prakash/float-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/nikhil07prakash/float-7b
- SGLang
How to use nikhil07prakash/float-7b 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 "nikhil07prakash/float-7b" \ --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": "nikhil07prakash/float-7b", "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 "nikhil07prakash/float-7b" \ --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": "nikhil07prakash/float-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use nikhil07prakash/float-7b with Docker Model Runner:
docker model run hf.co/nikhil07prakash/float-7b
Update README.md
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README.md
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<!-- Provide the basic links for the model. -->
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- **Repository:** [Link](https://github.com/Nix07/finetuning/)
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- **Paper :** [Fine-Tuning Enhances Existing Mechanisms: A Case Study on Entity Tracking](https://
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## How to Get Started with the Model
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**BibTeX:**
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- **Repository:** [Link](https://github.com/Nix07/finetuning/)
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- **Paper :** [Fine-Tuning Enhances Existing Mechanisms: A Case Study on Entity Tracking](https://arxiv.org/abs/2402.14811)
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## How to Get Started with the Model
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**BibTeX:**
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```python
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@misc{prakash2024finetuning,
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title={Fine-Tuning Enhances Existing Mechanisms: A Case Study on Entity Tracking},
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author={Nikhil Prakash and Tamar Rott Shaham and Tal Haklay and Yonatan Belinkov and David Bau},
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year={2024},
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eprint={2402.14811},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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