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
```

Johnblick187 commited on 17 days ago

Commit

0b84fe4

verified ·

1 Parent(s): eb107fe

Upload config.json

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config.json +28 -0

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	@@ -0,0 +1,28 @@

+{
+  "architectures": ["Starcoder2ForCausalLM"],
+  "model_type": "starcoder2",
+  "hidden_size": 2048,
+  "num_hidden_layers": 40,
+  "num_attention_heads": 16,
+  "num_key_value_heads": 4,
+  "intermediate_size": 8192,
+  "vocab_size": 65536,
+  "max_position_embeddings": 16384,
+  "rope_theta": 999999.0,
+  "sliding_window": null,
+  "use_cache": true,
+  "tie_word_embeddings": false,
+  "norm_epsilon": 1e-5,
+  "bos_token_id": 0,
+  "eos_token_id": 0,
+  "pad_token_id": 0,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "5.9.0",
+  "_smartcoder_moe": {
+    "n_experts": 32,
+    "expert_dim": 512,
+    "top_k": 2,
+    "total_experts": 1280,
+    "dense_intermediate": 8192
+  }
+}