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)
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

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 / mini_lora.yaml

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	# Config for multi-device LoRA finetuning in lora_finetune_distributed.py
	# using a Phi3 mini (3.8B) model
	#
	# This config assumes that you've run the following command before launching
	# this run:
	# tune download microsoft/Phi-3-mini-4k-instruct --output-dir /tmp/Phi-3-mini-4k-instruct --hf-token <HF_TOKEN> --ignore-patterns ""
	#
	# To launch on 2 devices, run the following command from root:
	# tune run --nnodes 1 --nproc_per_node 2 lora_finetune_distributed --config phi3/mini_lora
	#
	# You can add specific overrides through the command line. For example
	# to override the checkpointer directory while launching training
	# you can run:
	# tune run --nnodes 1 --nproc_per_node 2 lora_finetune_distributed --config phi3/mini_lora checkpointer.checkpoint_dir=<YOUR_CHECKPOINT_DIR>
	#
	# This config works best when the model is being fine-tuned on 2+ GPUs.
	# For single device LoRA finetuning please use mini_lora_single_device.yaml
	# or mini_qlora_single_device.yaml


	# Model Arguments
	model:
	_component_: torchtune.models.phi3.lora_phi3_mini
	lora_attn_modules: ['q_proj', 'v_proj']
	apply_lora_to_mlp: False
	apply_lora_to_output: False
	lora_rank: 8
	lora_alpha: 16

	tokenizer:
	_component_: torchtune.models.phi3.phi3_mini_tokenizer
	path: ./phi3/tokenizer.model

	checkpointer:
	_component_: torchtune.utils.FullModelHFCheckpointer
	checkpoint_dir: ./phi3
	checkpoint_files: [
	model-00001-of-00002.safetensors,
	model-00002-of-00002.safetensors
	]
	output_dir: lora-phi3-math
	model_type: PHI3_MINI
	resume_from_checkpoint: False

	# Dataset and Sampler
	dataset:
	_component_: torchtune.datasets.instruct_dataset
	source: TIGER-Lab/MATH-plus
	template: AlpacaInstructTemplate
	train_on_input: True
	packed: False
	max_seq_len: 4096
	split: train
	seed: 123
	shuffle: True
	batch_size: 2

	# Optimizer and Scheduler
	optimizer:
	_component_: torch.optim.AdamW
	weight_decay: 0.01
	lr: 3e-4
	lr_scheduler:
	_component_: torchtune.modules.get_cosine_schedule_with_warmup
	num_warmup_steps: 100

	loss:
	_component_: torch.nn.CrossEntropyLoss

	# Training
	epochs: 1
	max_steps_per_epoch: 2000
	gradient_accumulation_steps: 16

	# Logging
	output_dir: lora-phi3-math
	metric_logger:
	_component_: torchtune.utils.metric_logging.WandBLogger
	project: lora-phi3-math
	log_every_n_steps: 1
	log_peak_memory_stats: False

	# Environment
	device: cuda
	dtype: bf16
	enable_activation_checkpointing: False