Instructions to use Kukedlc/NeuralExperiment-7b-MagicCoder-v7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kukedlc/NeuralExperiment-7b-MagicCoder-v7 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Kukedlc/NeuralExperiment-7b-MagicCoder-v7")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Kukedlc/NeuralExperiment-7b-MagicCoder-v7") model = AutoModelForCausalLM.from_pretrained("Kukedlc/NeuralExperiment-7b-MagicCoder-v7") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Kukedlc/NeuralExperiment-7b-MagicCoder-v7 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Kukedlc/NeuralExperiment-7b-MagicCoder-v7" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Kukedlc/NeuralExperiment-7b-MagicCoder-v7", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Kukedlc/NeuralExperiment-7b-MagicCoder-v7
- SGLang
How to use Kukedlc/NeuralExperiment-7b-MagicCoder-v7 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 "Kukedlc/NeuralExperiment-7b-MagicCoder-v7" \ --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": "Kukedlc/NeuralExperiment-7b-MagicCoder-v7", "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 "Kukedlc/NeuralExperiment-7b-MagicCoder-v7" \ --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": "Kukedlc/NeuralExperiment-7b-MagicCoder-v7", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Kukedlc/NeuralExperiment-7b-MagicCoder-v7 with Docker Model Runner:
docker model run hf.co/Kukedlc/NeuralExperiment-7b-MagicCoder-v7
Update README.md
Browse files
README.md
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- ise-uiuc/Magicoder-Evol-Instruct-110K
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# Datacard for Custom Trained Model
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- Base Model : [Kukedlc/NeuralExperiment-7b-dare-ties](https://huggingface.co/Kukedlc/NeuralExperiment-7b-dare-ties)
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outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
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print(outputs[0]["generated_text"])
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```
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- ise-uiuc/Magicoder-Evol-Instruct-110K
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- Vezora/Tested-22k-Python-Alpaca
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---
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# Datacard for Custom Trained Model
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- Base Model : [Kukedlc/NeuralExperiment-7b-dare-ties](https://huggingface.co/Kukedlc/NeuralExperiment-7b-dare-ties)
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outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
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print(outputs[0]["generated_text"])
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```
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