Instructions to use jrc/phi3-mini-math with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jrc/phi3-mini-math with Transformers:

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

pipe = pipeline("text-generation", model="jrc/phi3-mini-math", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jrc/phi3-mini-math", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("jrc/phi3-mini-math", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jrc/phi3-mini-math with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jrc/phi3-mini-math"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jrc/phi3-mini-math",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jrc/phi3-mini-math

SGLang

How to use jrc/phi3-mini-math 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 "jrc/phi3-mini-math" \
    --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": "jrc/phi3-mini-math",
		"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 "jrc/phi3-mini-math" \
        --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": "jrc/phi3-mini-math",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jrc/phi3-mini-math with Docker Model Runner:
```
docker model run hf.co/jrc/phi3-mini-math
```

phi3-mini-math

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---
license: apache-2.0
datasets:
- TIGER-Lab/MATH-plus
language:
- en
tags:
- torchtune
- minerva-math
library_name: transformers
pipeline_tag: text-generation
---

# jrc/phi3-mini-math

<!-- Provide a quick summary of what the model is/does. -->

Math majors - who needs em? This model can answer any math questions you have.

## How to Get Started with the Model

Use the code below to get started with the model.

```python
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jrc/phi3-mini-math", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("jrc/phi3-mini-math", trust_remote_code=True)
```

## Training Details

Phi3 was trained using [torchtune](https://github.com/pytorch/torchtune) and the training script + config file are located in this repository.

```bash
tune run lora_finetune_distributed.py --config mini_lora.yaml 
```

You can see a full Weights & Biases run [here](https://api.wandb.ai/links/jcummings/hkey76vj).

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

This model was finetuned on the following datasets:

* [TIGER-Lab/MATH-plus](https://huggingface.co/datasets/TIGER-Lab/MATH-plus): An advanced math-specific dataset with 894k samples.

#### Hardware

* Machines: 4 x NVIDIA A100 GPUs
* Max VRAM used per GPU: 29 GB
* Real time: 10 hours

## Evaluation

The finetuned model is evaluated on [minerva-math](https://research.google/blog/minerva-solving-quantitative-reasoning-problems-with-language-models/) using [EleutherAI Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness) through torchtune.

```bash
tune run eleuther_eval --config eleuther_evaluation \
          checkpoint.checkpoint_dir=./lora-phi3-math \
          tasks=["minerva_math"] \
          batch_size=32 
```

|               Tasks                |Version|Filter|n-shot|  Metric   |Value |   |Stderr|
|------------------------------------|-------|------|-----:|-----------|-----:|---|-----:|
|minerva_math                        |N/A    |none  |     4|exact_match|0.1670|±  |0.0051|
| - minerva_math_algebra             |      1|none  |     4|exact_match|0.2502|±  |0.0126|
| - minerva_math_counting_and_prob   |      1|none  |     4|exact_match|0.1329|±  |0.0156|
| - minerva_math_geometry            |      1|none  |     4|exact_match|0.1232|±  |0.0150|
| - minerva_math_intermediate_algebra|      1|none  |     4|exact_match|0.0576|±  |0.0078|
| - minerva_math_num_theory          |      1|none  |     4|exact_match|0.1148|±  |0.0137|
| - minerva_math_prealgebra          |      1|none  |     4|exact_match|0.3077|±  |0.0156|
| - minerva_math_precalc             |      1|none  |     4|exact_match|0.0623|±  |0.0104|

This shows a large improvement over the base Phi3 Mini model.

## Model Card Contact

Drop me a line at @official_j3rck