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)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Johnblick187/SmartCoderMoE", trust_remote_code=True, dtype="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
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"add_prefix_space": false,
"backend": "tokenizers",
"bos_token": "<|endoftext|>",
"clean_up_tokenization_spaces": true,
"eos_token": "<|endoftext|>",
"errors": "replace",
"extra_special_tokens": [
"<|endoftext|>",
"<fim_prefix>",
"<fim_middle>",
"<fim_suffix>",
"<fim_pad>",
"<repo_name>",
"<file_sep>",
"<issue_start>",
"<issue_comment>",
"<issue_closed>",
"<jupyter_start>",
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"<jupyter_output>",
"<jupyter_script>",
"<empty_output>",
"<code_to_intermediate>",
"<intermediate_to_code>",
"<pr>",
"<pr_status>",
"<pr_is_merged>",
"<pr_base>",
"<pr_file>",
"<pr_base_code>",
"<pr_diff>",
"<pr_diff_hunk>",
"<pr_comment>",
"<pr_event_id>",
"<pr_review>",
"<pr_review_state>",
"<pr_review_comment>",
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"<pr_in_reply_to_comment_id>",
"<pr_diff_hunk_comment_line>",
"<NAME>",
"<EMAIL>",
"<KEY>",
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],
"is_local": false,
"local_files_only": false,
"model_max_length": 32768,
"pad_token": null,
"tokenizer_class": "GPT2Tokenizer",
"unk_token": "<|endoftext|>",
"vocab_size": 49152
}
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