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
```

jrc commited on May 20, 2024

Commit

dc1a633

verified ·

1 Parent(s): 2e99b69

Update README.md

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README.md +11 -18

README.md CHANGED Viewed

@@ -15,11 +15,7 @@ pipeline_tag: text-generation
 <!-- Provide a quick summary of what the model is/does. -->
-Phi-3 Mini 4k Instruct model finetuned on math datasets.
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 ## How to Get Started with the Model
@@ -35,7 +31,7 @@ model = AutoModelForCausalLM.from_pretrained("jrc/phi3-mini-math", trust_remote_
 ## Training Details
-Phi3 was trained using [torchtune]() and the training script + config file are located in this repository.
 ```bash
 tune run lora_finetune_distributed.py --config mini_lora.yaml
@@ -45,11 +41,17 @@ tune run lora_finetune_distributed.py --config mini_lora.yaml
 <!-- 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. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 ## Evaluation
@@ -75,15 +77,6 @@ tune run eleuther_eval --config eleuther_evaluation \
 | - minerva_math_precalc             |      1|none  |     4|exact_match|0.0623|±  |0.0104|
-## Technical Specifications [optional]
-#### Hardware
-4 x NVIDIA A100 GPUs
-Max VRAM used per GPU: 29 GB
 ## Model Card Contact
 [More Information Needed]

 <!-- 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
 ## 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
 <!-- 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: An advanced math-specific dataset with 894k samples.
+#### Hardware
+4 x NVIDIA A100 GPUs
+Max VRAM used per GPU: 29 GB
+Real time: 12 hours
 ## Evaluation
 | - minerva_math_precalc             |      1|none  |     4|exact_match|0.0623|±  |0.0104|
 ## Model Card Contact
 [More Information Needed]