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