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.jsonandgeneration_config.jsonfiles for optimal performance during inference.