68 lines
3.8 KiB
Markdown
68 lines
3.8 KiB
Markdown
---
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license: creativeml-openrail-m
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datasets:
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- prithivMLmods/Math-IIO-68K-Mini
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language:
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- en
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base_model:
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- HuggingFaceTB/SmolLM2-1.7B-Instruct
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- safetensors
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- pytorch
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- llama
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- trl
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- ollama
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- llama-cpp
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- math
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- instruct
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---
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### SmolLM2-Math-IIO-1.7B-Instruct
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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.
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| File Name | Size | Description | Upload Status |
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|----------------------------------------|------------|------------------------------------------------|----------------|
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| `.gitattributes` | 1.52 kB | Git attributes configuration file | Uploaded |
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| `README.md` | 287 Bytes | Updated README file | Updated |
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| `config.json` | 940 Bytes | Model configuration settings | Uploaded |
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| `generation_config.json` | 162 Bytes | Generation-specific configurations | Uploaded |
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| `merges.txt` | 515 kB | Merging information for tokenization | Uploaded |
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| `pytorch_model.bin` | 3.42 GB | Full model weights (PyTorch format) | Uploaded (LFS) |
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| `special_tokens_map.json` | 572 Bytes | Mapping for special tokens used by the model | Uploaded |
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| `tokenizer.json` | 3.77 MB | Tokenizer configuration and vocabulary | Uploaded |
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| `tokenizer_config.json` | 3.95 kB | Tokenizer configuration for loading and usage | Uploaded |
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| `vocab.json` | 801 kB | Vocabulary for the tokenizer | Uploaded |
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### **Key Features:**
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1. **Math-Focused Capabilities:**
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This model is fine-tuned to handle a wide range of mathematical queries, from simple arithmetic to complex equations and mathematical proofs.
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2. **Instruction-Tuned:**
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Specifically trained to follow structured queries and deliver clear, coherent outputs based on instructions, ensuring high-quality, relevant responses to prompts.
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3. **Tokenizer & Custom Tokens:**
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Includes a robust tokenizer configuration with support for mathematical notation, custom tokens, and an extended vocabulary for accurate understanding and output generation.
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---
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### **Training Details:**
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- **Base Model:** [SmolLM2-1.7B](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct)
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- **Dataset:** Trained on **Math-IIO-68K-Mini**, a dataset focused on mathematical instructions and logic-based queries, with a total of 68.8k examples.
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### **Capabilities:**
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- **Mathematical Problem-Solving:** Solves and explains complex mathematical problems, including algebra, calculus, and more advanced topics.
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- **Instruction-Following:** Adheres to structured inputs and outputs, making it effective for generating step-by-step solutions.
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- **Text Generation:** Capable of generating mathematical proofs, explanations, and educational content tailored to various user queries.
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---
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### **Usage Instructions:**
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1. **Model Setup:** Download all model files and ensure the PyTorch model weights and tokenizer configurations are included.
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2. **Inference:** Load the model in a Python environment using frameworks like PyTorch or Hugging Face's Transformers.
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3. **Customization:** Configure the model with the `config.json` and `generation_config.json` files for optimal performance during inference.
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