Instructions to use mitnamin/codeparrot-ds with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mitnamin/codeparrot-ds with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mitnamin/codeparrot-ds")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mitnamin/codeparrot-ds") model = AutoModelForCausalLM.from_pretrained("mitnamin/codeparrot-ds") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use mitnamin/codeparrot-ds with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mitnamin/codeparrot-ds" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mitnamin/codeparrot-ds", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mitnamin/codeparrot-ds
- SGLang
How to use mitnamin/codeparrot-ds 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 "mitnamin/codeparrot-ds" \ --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": "mitnamin/codeparrot-ds", "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 "mitnamin/codeparrot-ds" \ --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": "mitnamin/codeparrot-ds", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use mitnamin/codeparrot-ds with Docker Model Runner:
docker model run hf.co/mitnamin/codeparrot-ds
End of training
Browse files- README.md +2 -2
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README.md
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The following hyperparameters were used during training:
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- learning_rate: 0.0005
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- train_batch_size: 32
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- eval_batch_size:
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- seed: 42
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 256
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_steps: 1000
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- num_epochs:
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- mixed_precision_training: Native AMP
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### Training results
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The following hyperparameters were used during training:
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- learning_rate: 0.0005
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- train_batch_size: 32
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 256
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_steps: 1000
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- num_epochs: 6000
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- mixed_precision_training: Native AMP
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### Training results
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model.safetensors
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