Instructions to use epinnock/wizardcoder-1b-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use epinnock/wizardcoder-1b-merged with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="epinnock/wizardcoder-1b-merged")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("epinnock/wizardcoder-1b-merged") model = AutoModelForCausalLM.from_pretrained("epinnock/wizardcoder-1b-merged", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use epinnock/wizardcoder-1b-merged with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "epinnock/wizardcoder-1b-merged" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "epinnock/wizardcoder-1b-merged", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/epinnock/wizardcoder-1b-merged
- SGLang
How to use epinnock/wizardcoder-1b-merged 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 "epinnock/wizardcoder-1b-merged" \ --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": "epinnock/wizardcoder-1b-merged", "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 "epinnock/wizardcoder-1b-merged" \ --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": "epinnock/wizardcoder-1b-merged", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use epinnock/wizardcoder-1b-merged with Docker Model Runner:
docker model run hf.co/epinnock/wizardcoder-1b-merged
Commit History
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Update readme to fix typo in model ID (#3)
- Update readme to fix typo in model ID (b5fb0f9d14658b01881385565d481189814b99bb) Co-authored-by: Pedro Batista <pedrovhb@users.noreply.huggingface.co>59a8a70
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commit for readme
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Create README.md
# WizardCoder-1B 1B version of [StarCoderBase](https://huggingface.co/bigcode/starcoderbase) finetuned on the the Evol-Instruct-Code-80k-v1 dataset generated using the [Evol-Teacher](https://github.com/nickrosh/evol-teacher) repo. Based on the previous work of @WizardLM ## Table of Contents 1. [Model Summary](##model-summary) 2. [Use](##use) 3. [Limitations](##limitations) 4. [Training](##training) 5. [License](##license) 6. [Citation](##citation) ## Model Summary WizardCoder-1B is a 1B parameter model trained on 80+ programming languages from [The Stack (v1.2)](https://huggingface.co/datasets/bigcode/the-stack), with opt-out requests excluded. The model uses [Multi Query Attention](https://arxiv.org/abs/1911.02150), [a context window of 8192 tokens](https://arxiv.org/abs/2205.14135), and was trained using the [Fill-in-the-Middle objective](https://arxiv.org/abs/2207.14255) on 1 trillion tokens. It is then finetuned on 8 RTXA5000 for ~4 epochs (2400 steps) using LoRA and merged…b459148 -
Upload tokenizer
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Upload GPTBigCodeForCausalLM
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initial commit
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