Text Generation
Transformers
Safetensors
English
smartcoder_moe
Mixture of Experts
starcoder2
mixture-of-experts
code
smartcoder
conversational
custom_code
Instructions to use Johnblick187/SmartCoderMoE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Johnblick187/SmartCoderMoE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Johnblick187/SmartCoderMoE", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Johnblick187/SmartCoderMoE", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Johnblick187/SmartCoderMoE with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Johnblick187/SmartCoderMoE" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Johnblick187/SmartCoderMoE", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Johnblick187/SmartCoderMoE
- SGLang
How to use Johnblick187/SmartCoderMoE 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 "Johnblick187/SmartCoderMoE" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Johnblick187/SmartCoderMoE", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "Johnblick187/SmartCoderMoE" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Johnblick187/SmartCoderMoE", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Johnblick187/SmartCoderMoE with Docker Model Runner:
docker model run hf.co/Johnblick187/SmartCoderMoE
Commit History
-
Update README.md
Verified signaturecc30de2 -
Update config.json
Verified signature4e7c0cc -
Update config.json
Verified signaturea0db50a
-
Update modeling_smartcoder_moe.py
Verified signaturef10d44f -
Update modeling_smartcoder_moe.py
Verified signature5fc333f -
Update modeling_smartcoder_moe.py
Verified signaturef46564f -
Update modeling_smartcoder_moe.py
Verified signature63a6988
-
Update modeling_smartcoder_moe.py
Verified signaturec06d951
-
Update config.json
Verified signature9d345fa -
Update modeling_smartcoder_moe.py
Verified signatured9c1d79 -
Rename modeling_smartcoder.py to modeling_smartcoder_moe.py
Verified signatured00ea53
-
Update config.json
Verified signature39d4f1a -
Update config.json
Verified signaturee719add -
Update config.json
Verified signature32dbfa0 -
Upload modeling_smartcoder.py
Verified signature32dba37
-
Update README.md
Verified signature21425ee -
Update README.md
Verified signature54fb1ca -
Update README.md
Verified signature4f864d1 -
Create README.md
Verified signatureec57f1e -
Upload config.json
Verified signature0b84fe4 -
Delete smartcoder_meta.json
Verified signatureeb107fe -
Upload folder using huggingface_hub
Verified signaturec5d4cf7 -
initial commit
Verified signature795da42