Instructions to use prithivMLmods/SmolLM2-Math-IIO-1.7B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/SmolLM2-Math-IIO-1.7B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/SmolLM2-Math-IIO-1.7B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/SmolLM2-Math-IIO-1.7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("prithivMLmods/SmolLM2-Math-IIO-1.7B-Instruct")
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 prithivMLmods/SmolLM2-Math-IIO-1.7B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/SmolLM2-Math-IIO-1.7B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/SmolLM2-Math-IIO-1.7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/SmolLM2-Math-IIO-1.7B-Instruct

SGLang

How to use prithivMLmods/SmolLM2-Math-IIO-1.7B-Instruct 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 "prithivMLmods/SmolLM2-Math-IIO-1.7B-Instruct" \
    --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": "prithivMLmods/SmolLM2-Math-IIO-1.7B-Instruct",
		"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 "prithivMLmods/SmolLM2-Math-IIO-1.7B-Instruct" \
        --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": "prithivMLmods/SmolLM2-Math-IIO-1.7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/SmolLM2-Math-IIO-1.7B-Instruct with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/SmolLM2-Math-IIO-1.7B-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

SmolLM2-Math-IIO-1.7B-Instruct

The SmolLM2-Math-IIO-1.7B-Instruct model is a fine-tuned variant of the SmolLM2-1.7B architecture, optimized for mathematical instruction and reasoning tasks. It is particularly suited for applications that require mathematical problem-solving, logical inference, and detailed step-by-step explanations.

File Name	Size	Description	Upload Status
`.gitattributes`	1.52 kB	Git attributes configuration file	Uploaded
`README.md`	287 Bytes	Updated README file	Updated
`config.json`	940 Bytes	Model configuration settings	Uploaded
`generation_config.json`	162 Bytes	Generation-specific configurations	Uploaded
`merges.txt`	515 kB	Merging information for tokenization	Uploaded
`pytorch_model.bin`	3.42 GB	Full model weights (PyTorch format)	Uploaded (LFS)
`special_tokens_map.json`	572 Bytes	Mapping for special tokens used by the model	Uploaded
`tokenizer.json`	3.77 MB	Tokenizer configuration and vocabulary	Uploaded
`tokenizer_config.json`	3.95 kB	Tokenizer configuration for loading and usage	Uploaded
`vocab.json`	801 kB	Vocabulary for the tokenizer	Uploaded

Key Features:

Math-Focused Capabilities:
This model is fine-tuned to handle a wide range of mathematical queries, from simple arithmetic to complex equations and mathematical proofs.
Instruction-Tuned:
Specifically trained to follow structured queries and deliver clear, coherent outputs based on instructions, ensuring high-quality, relevant responses to prompts.
Tokenizer & Custom Tokens:
Includes a robust tokenizer configuration with support for mathematical notation, custom tokens, and an extended vocabulary for accurate understanding and output generation.

Training Details:

Base Model: SmolLM2-1.7B
Dataset: Trained on Math-IIO-68K-Mini, a dataset focused on mathematical instructions and logic-based queries, with a total of 68.8k examples.

Capabilities:

Mathematical Problem-Solving: Solves and explains complex mathematical problems, including algebra, calculus, and more advanced topics.
Instruction-Following: Adheres to structured inputs and outputs, making it effective for generating step-by-step solutions.
Text Generation: Capable of generating mathematical proofs, explanations, and educational content tailored to various user queries.

Usage Instructions:

Model Setup: Download all model files and ensure the PyTorch model weights and tokenizer configurations are included.
Inference: Load the model in a Python environment using frameworks like PyTorch or Hugging Face's Transformers.
Customization: Configure the model with the config.json and generation_config.json files for optimal performance during inference.

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