126 lines
4.9 KiB
Markdown
126 lines
4.9 KiB
Markdown
---
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license: creativeml-openrail-m
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datasets:
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- microsoft/orca-math-word-problems-200k
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language:
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- en
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base_model:
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- allenai/Llama-3.1-Tulu-3-8B
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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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- math
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- tulu
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- trl
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- llama
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- text-generation-inference
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- math_lingo
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---
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# Tulu-MathLingo-8B Model Files
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The **Tulu-MathLingo-8B** model is a fine-tuned version of **meta-llama/Llama-3.1-8B**, optimized for solving mathematical word problems and reasoning tasks in English. The model integrates advanced language understanding and reasoning capabilities with a focus on providing solutions to math-related queries.
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| **File Name** | **Size** | **Description** | **Upload Status** |
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| `.gitattributes` | 1.57 kB | Configures LFS tracking for large files. | Updated |
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| `README.md` | 292 Bytes | Basic details about the uploaded model. | Updated |
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| `config.json` | 988 Bytes | Contains model architecture and metadata. | Uploaded |
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| `generation_config.json` | 241 Bytes | Parameters for text generation (e.g., length, temperature). | Uploaded |
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| `model-00001-of-00004.safetensors`| 4.98 GB | Part 1 of model weights. | Uploaded (LFS) |
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| `model-00002-of-00004.safetensors`| 5 GB | Part 2 of model weights. | Uploaded (LFS) |
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| `model-00003-of-00004.safetensors`| 4.92 GB | Part 3 of model weights. | Uploaded (LFS) |
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| `model-00004-of-00004.safetensors`| 1.17 GB | Part 4 of model weights. | Uploaded (LFS) |
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| `model.safetensors.index.json` | 25.4 kB | Index file for multi-part model weights. | Uploaded |
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| `special_tokens_map.json` | 462 Bytes | Maps special tokens (e.g., `<PAD>`, `<EOS>`). | Uploaded |
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| `tokenizer.json` | 17.2 MB | Full tokenizer configuration. | Uploaded (LFS) |
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| `tokenizer_config.json` | 57.6 kB | Metadata for tokenizer usage. | Uploaded |
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### Sample Solve
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### **Key Features**
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1. **Multilingual Math Reasoning:**
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- Designed for solving complex math problems in **English** and **Tulu**.
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2. **Text Generation:**
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- Generates detailed and contextually accurate text responses.
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3. **Fine-Tuned Specializations:**
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- Trained on the **microsoft/orca-math-word-problems-200k** dataset for word problem-solving.
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4. **Special Token Mapping:**
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- Configured to use tokens for specific functions such as `<PAD>` and `<EOS>` effectively.
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5. **Secure and Efficient Storage:**
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- Model weights are stored in the **Safetensors** format for secure and faster inference.
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6. **Large Parameter Size:**
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- 8.03 billion parameters enable handling complex queries and multi-turn conversations.
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---
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### **Training Details**
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- **Base Model:** [meta-llama/Llama-3.1-8B](#)
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- **Fine-Tuned:**
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- Through multiple stages: **SFT (Supervised Fine-Tuning)** and **DPO (Direct Preference Optimization)**.
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- **Dataset:**
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- Trained on **200k word problems** from the **Microsoft Orca Math Word Problems Dataset**.
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- **Model Size:**
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- 8.03B parameters, optimized for **FP16** tensor type.
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---
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### **Applications**
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1. **Mathematical Word Problems:**
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- Solve structured or unstructured math problems in natural language.
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2. **Conversational AI for Math:**
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- Engage users in interactive dialogues focused on math and logic reasoning.
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3. **Multilingual Support:**
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- Supports queries in **Tulu** and **English**, enhancing accessibility.
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4. **Education Tools:**
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- Useful in tutoring systems for math, helping students with problem-solving.
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---
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### **Usage**
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#### **Loading the Model**
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "prithivMLmods/Tulu-MathLingo-8B"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="fp16")
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```
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---
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##### **Math Word Problem**
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```python
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query = "If a train travels 60 miles in 2 hours, what is its average speed?"
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inputs = tokenizer(query, return_tensors="pt")
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outputs = model.generate(**inputs, max_length=100)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print("Answer:", response)
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```
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### **Performance Requirements**
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- **Hardware:**
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- Requires a GPU with at least **24GB VRAM** for optimal performance due to model size and FP16 usage.
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- **Optimization:**
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- Use mixed precision (`fp16`) for reduced memory footprint.
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- Split inference across multiple GPUs if necessary.
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--- |