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 15 days ago

Commit

55c313a

verified ·

1 Parent(s): 1997d68

Update modeling_smartcoder_moe.py

Browse files

Files changed (1) hide show

modeling_smartcoder_moe.py +2 -0

modeling_smartcoder_moe.py CHANGED Viewed

@@ -35,6 +35,7 @@ class SmartCoderMoEConfig(PretrainedConfig):
         dense_intermediate_size=8192,
         num_experts=32,
         expert_dim=512,
         num_experts_per_tok=2,
         max_position_embeddings=16384,
         rope_theta=10000.0,
@@ -54,6 +55,7 @@ class SmartCoderMoEConfig(PretrainedConfig):
         self.dense_intermediate_size = dense_intermediate_size
         self.num_experts = num_experts
         self.expert_dim = expert_dim
         self.num_experts_per_tok = num_experts_per_tok
         self.max_position_embeddings = max_position_embeddings
         self.rope_theta = rope_theta

         dense_intermediate_size=8192,
         num_experts=32,
         expert_dim=512,
+        expert_intermediate_size=5120,
         num_experts_per_tok=2,
         max_position_embeddings=16384,
         rope_theta=10000.0,
         self.dense_intermediate_size = dense_intermediate_size
         self.num_experts = num_experts
         self.expert_dim = expert_dim
+        self.expert_intermediate_size = expert_intermediate_size
         self.num_experts_per_tok = num_experts_per_tok
         self.max_position_embeddings = max_position_embeddings
         self.rope_theta = rope_theta