Instructions to use Johnblick187/SmartCoderMoE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

SmartCoderMoE / tokenizer_config.json

Johnblick187

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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>",
	"<jupyter_text>",
	"<jupyter_code>",
	"<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>",
	"<pr_in_reply_to_review_id>",
	"<pr_in_reply_to_comment_id>",
	"<pr_diff_hunk_comment_line>",
	"<NAME>",
	"<EMAIL>",
	"<KEY>",
	"<PASSWORD>"
	],
	"is_local": false,
	"local_files_only": false,
	"model_max_length": 32768,
	"pad_token": null,
	"tokenizer_class": "GPT2Tokenizer",
	"unk_token": "<\|endoftext\|>",
	"vocab_size": 49152
	}