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
```

prithivMLmods commited on Dec 3, 2024

Commit

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@@ -53,24 +53,6 @@ The **SmolLM2-Math-IIO-1.7B-Instruct** model is a fine-tuned variant of the **Sm
 - **Base Model:** [SmolLM2-1.7B](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct)
 - **Dataset:** Trained on **Math-IIO-68K-Mini**, a dataset focused on mathematical instructions and logic-based queries, with a total of 68.8k examples.
----
-### **File Details:**
-| **File Name**                   | **Size**       | **Description**                                  |
-|-----------------------------------|----------------|--------------------------------------------------|
-| `.gitattributes`                  | 1.52 kB        | Git attributes configuration file.               |
-| `README.md`                       | 287 Bytes      | Updated README file with model details.          |
-| `config.json`                     | 940 Bytes      | Configuration file for model setup.              |
-| `generation_config.json`          | 162 Bytes      | Configuration for generation-specific settings.  |
-| `merges.txt`                      | 515 kB         | Tokenizer merging rules (Byte Pair Encoding).    |
-| `pytorch_model.bin`               | 3.42 GB        | Full model weights in PyTorch format.            |
-| `special_tokens_map.json`         | 572 Bytes      | Special token mappings for the tokenizer.        |
-| `tokenizer.json`                  | 3.77 MB        | Tokenizer configuration and vocabulary.          |
-| `tokenizer_config.json`           | 3.95 kB        | Tokenizer configuration for loading.             |
-| `vocab.json`                      | 801 kB         | Vocabulary file for the tokenizer.               |
----
 ### **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.

 - **Base Model:** [SmolLM2-1.7B](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct)
 - **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.