Instructions to use ajibawa-2023/Code-33B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ajibawa-2023/Code-33B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ajibawa-2023/Code-33B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ajibawa-2023/Code-33B") model = AutoModelForCausalLM.from_pretrained("ajibawa-2023/Code-33B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use ajibawa-2023/Code-33B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ajibawa-2023/Code-33B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ajibawa-2023/Code-33B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ajibawa-2023/Code-33B
- SGLang
How to use ajibawa-2023/Code-33B 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 "ajibawa-2023/Code-33B" \ --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": "ajibawa-2023/Code-33B", "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 "ajibawa-2023/Code-33B" \ --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": "ajibawa-2023/Code-33B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ajibawa-2023/Code-33B with Docker Model Runner:
docker model run hf.co/ajibawa-2023/Code-33B
Commit ·
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Parent(s): 804a538
Update README.md
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README.md
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Entire dataset was trained on 4 x A100 80GB. For 3 epoch, training took 6 days & 5 hours. DeepSpeed codebase was used for training purpose. This was trained on Llama-1 by Meta.
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This is a full fine tuned model. Links for quantized models
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**GPTQ GGUF & AWQ**
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AWQ: [Link](https://huggingface.co/TheBloke/Code-33B-AWQ)
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**Example Prompt:**
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Entire dataset was trained on 4 x A100 80GB. For 3 epoch, training took 6 days & 5 hours. DeepSpeed codebase was used for training purpose. This was trained on Llama-1 by Meta.
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This is a full fine tuned model. Links for quantized models are given below.
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**GPTQ GGUF & AWQ**
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AWQ: [Link](https://huggingface.co/TheBloke/Code-33B-AWQ)
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Extremely thankful to [TheBloke](https://huggingface.co/TheBloke) for making Quantized versions of model.
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**Example Prompt:**
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