Instructions to use moelanoby/phi-3-M3-coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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)# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("moelanoby/phi-3-M3-coder", trust_remote_code=True) model = AutoModelForMultimodalLM.from_pretrained("moelanoby/phi-3-M3-coder", trust_remote_code=True) 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=40) 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
π© Report: Fake & Spam
False claims , fake numbers and benchmark, just fishing for likes
Agreed
It's not "spam".
read the code.
that his evaluation methods are not convincing / complete, doesn't render the model meritless.
I downloaded the model and tried it for myself before actually submitting this , and its no where near what it claims .
Comparing 4b model to sota models and claiming to out perform them by a mile is just too far fetched.
It falls under the "omg this small model fine-tuned is better than sota models " hype spam.
It's not "spam".
read the code.
that his evaluation methods are not convincing / complete, doesn't render the model meritless.
No it's spam because it's same model. I'm not even sure there was much tuning. Performs on simple question exactly the same as phi3-mini
It's not "spam".
read the code.
that his evaluation methods are not convincing / complete, doesn't render the model meritless.
No it's spam because it's same model. I'm not even sure there was much tuning. Performs on simple question exactly the same as phi3-mini
I am currently trying to implement the LLAMA version, which is an improvement on the architecture, and I hope my implementation is correct.This is not just a fine-tuning, it is an architectural improvement, very interesting~
It's not "spam".
read the code.
that his evaluation methods are not convincing / complete, doesn't render the model meritless.
No it's spam because it's same model. I'm not even sure there was much tuning. Performs on simple question exactly the same as phi3-mini
Perhaps freezing the original weights, and training the added components.
Although I have said so much, there are always some ignorant and unmotivated people who just deny it.
