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@@ -53,24 +53,6 @@ The **SmolLM2-Math-IIO-1.7B-Instruct** model is a fine-tuned variant of the **Sm
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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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- ---
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-
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- ### **File Details:**
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- | **File Name** | **Size** | **Description** |
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- |-----------------------------------|----------------|--------------------------------------------------|
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- | `.gitattributes` | 1.52 kB | Git attributes configuration file. |
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- | `README.md` | 287 Bytes | Updated README file with model details. |
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- | `config.json` | 940 Bytes | Configuration file for model setup. |
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- | `generation_config.json` | 162 Bytes | Configuration for generation-specific settings. |
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- | `merges.txt` | 515 kB | Tokenizer merging rules (Byte Pair Encoding). |
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- | `pytorch_model.bin` | 3.42 GB | Full model weights in PyTorch format. |
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- | `special_tokens_map.json` | 572 Bytes | Special token mappings for the tokenizer. |
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- | `tokenizer.json` | 3.77 MB | Tokenizer configuration and vocabulary. |
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- | `tokenizer_config.json` | 3.95 kB | Tokenizer configuration for loading. |
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- | `vocab.json` | 801 kB | Vocabulary file for the tokenizer. |
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- ---
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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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  - **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.