Instructions to use moelanoby/phi-3-M3-coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use moelanoby/phi-3-M3-coder with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="moelanoby/phi-3-M3-coder", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("moelanoby/phi-3-M3-coder", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("moelanoby/phi-3-M3-coder", trust_remote_code=True, device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use moelanoby/phi-3-M3-coder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "moelanoby/phi-3-M3-coder"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "moelanoby/phi-3-M3-coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/moelanoby/phi-3-M3-coder

SGLang

How to use moelanoby/phi-3-M3-coder 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 "moelanoby/phi-3-M3-coder" \
    --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": "moelanoby/phi-3-M3-coder",
		"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 "moelanoby/phi-3-M3-coder" \
        --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": "moelanoby/phi-3-M3-coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use moelanoby/phi-3-M3-coder with Docker Model Runner:
```
docker model run hf.co/moelanoby/phi-3-M3-coder
```

moelanoby commited on Jun 30, 2025

Commit

e712113

verified ·

1 Parent(s): fa1f6ec

Update README.md

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@@ -34,7 +34,7 @@ The benchmark results demonstrate a level of performance that significantly surp
 | Model                               | HumanEval Pass@1 Score | Note                   |
 | :---------------------------------- | :--------------------: | :--------------------- |
-| **moelanoby/phi3-M3-V2 (This Model)** |       **98.17%**       | **Commercial License** |
 | GPT-4.5 / "Orion"                   |       `~96.00%`        | Projected (Late 2025)  |
 | Gemini 2.5 Pro                      |       `~95.00%`        | Projected (Late 2025)  |
 | Claude 4                            |       `~94.00%`        | Projected (Late 2025)  |
@@ -121,7 +121,9 @@ except AttributeError:
 # (Example generation code would follow here)
 ```
 ## Acknowledgements
 -   The base of this model utilizes the **Phi-3** architecture developed by Microsoft.

 | Model                               | HumanEval Pass@1 Score | Note                   |
 | :---------------------------------- | :--------------------: | :--------------------- |
+| **moelanoby/phi3-M3-V2 (This Model)** |       **95.12%/98.17%/98.56%**       | **Commercial License** and they are ordered with 0,1,2 self corrections |
 | GPT-4.5 / "Orion"                   |       `~96.00%`        | Projected (Late 2025)  |
 | Gemini 2.5 Pro                      |       `~95.00%`        | Projected (Late 2025)  |
 | Claude 4                            |       `~94.00%`        | Projected (Late 2025)  |
 # (Example generation code would follow here)
 ```
+## HUGE NOTES
+- downside: the model might grow more incoherent and less accurate as you add more self corrections
+- recommendations: you could use 1,2,3 self corrections if needed and 2 self corrections is the most recommended
 ## Acknowledgements
 -   The base of this model utilizes the **Phi-3** architecture developed by Microsoft.