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Model: nivektk/BullSolve Source: Original Platform
2026-05-09 19:35:30 +08:00
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+FROM /content/nivektk/BullSolve/unsloth.F16.gguf
+
+TEMPLATE """{{ if .System }}<|start_header_id|>system<|end_header_id|>
+
+{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>
+
+{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>
+
+{{ .Response }}<|eot_id|>"""
+
+PARAMETER stop "<|end_header_id|>"
+PARAMETER stop "<|python_tag|>"
+PARAMETER stop "<|eom_id|>"
+PARAMETER stop "<|start_header_id|>"
+PARAMETER stop "<|finetune_right_pad_id|>"
+PARAMETER stop "<|end_of_text|>"
+PARAMETER stop "<|eot_id|>"
+PARAMETER stop "<|reserved_special_token_"
+PARAMETER temperature 1.5
+PARAMETER min_p 0.1
+SYSTEM "Below are some instructions that describe some tasks. Write responses that appropriately complete each request."
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+---
+base_model: unsloth/llama-3.1-8B-Instruct-unsloth-bnb-4bit
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- llama
+- gguf
+- text-generation
+- math
+- fine-tuning
+- llama-3
+license: apache-2.0
+language:
+- en
+dataset:
+- nivektk/math-augmented-dataset
+task_categories:
+- text-generation
+- question-answering
+size_categories:
+- 1K<n<10K
+model_name: BullSolve
+---
+
+
+# BullSolve: Fine-Tuned LLaMA 3 Model for Math Problem Solving
+
+## Model Description
+BullSolve is a fine-tuned version of `unsloth/llama-3.1-8B-Instruct-unsloth-bnb-4bit`, optimized for solving advanced math problems. The model was trained using LoRA adapters with the `nivektk/math-augmented-dataset`, which contains algebra problems and their solutions.
+
+This model is optimized for low VRAM usage and efficient inference while maintaining high accuracy in mathematical problem-solving tasks.
+
+# Training Data
+
+The model was fine-tuned using a subset of the [MATH Dataset](https://arxiv.org/abs/2103.03874), specifically the **Algebra** category, containing **1,006 validated examples**. This dataset, originally developed by Dan Hendrycks et al., consists of mathematical problems structured in JSON format, with attributes:
+
+- `problem`: Problem statement in text with LaTeX expressions.
+- `level`: Difficulty level (1 to 5).
+- `type`: Mathematical domain (e.g., Algebra, Geometry).
+- `solution`: Step-by-step solution in English.
+
+For fine-tuning, the dataset was preprocessed into ShareGPT format with the structure:
+
+```
+{question}[[
+Solution:
+{solution}
+]]
+```
+
+Additionally, a chat template was applied for better inference compatibility.
+
+## Training Configuration
+The model was trained using **Unsloth** with LoRA, optimizing memory efficiency and inference speed. Key parameters:
+- **Model**: `unsloth/llama-3.1-8B-Instruct-unsloth-bnb-4bit`
+- **Max Sequence Length**: 2048 tokens
+- **LoRA Config**:
+  - Rank (`r`): 16
+  - Alpha: 16
+  - Dropout: 0
+  - Target Modules: `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
+- **Training Arguments**:
+  - Batch Size: 1
+  - Gradient Accumulation: 4
+  - Max Steps: 25
+  - Learning Rate: 1e-4
+  - Optimizer: AdamW (8-bit)
+  - Weight Decay: 0.01
+  - LR Scheduler: Linear
+
+## Inference
+BullSolve is optimized for fast inference and mathematical problem-solving. Example usage:
+```python
+from transformers import TextStreamer
+from unsloth import FastLanguageModel
+import torch
+
+model, tokenizer = FastLanguageModel.from_pretrained("nivektk/BullSolve")
+FastLanguageModel.for_inference(model)
+
+messages = [{"role": "user", "content": "Evaluate $\\log_{5^2}5^4$."}]
+input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to("cuda")
+
+text_streamer = TextStreamer(tokenizer, skip_prompt=True)
+_ = model.generate(input_ids, streamer=text_streamer, max_new_tokens=2000, pad_token_id=tokenizer.eos_token_id)
+```
+
+## Model Usage
+This model is suitable for:
+- Math tutoring and automated problem-solving
+- AI-assisted mathematical reasoning
+- Education-based chatbot assistants
+
+## Limitations
+- The model is trained only on algebra problems and may not generalize well to other areas of mathematics.
+- It is optimized for inference efficiency rather than large-scale fine-tuning.
+
+## Acknowledgments
+- **Unsloth** for efficient LoRA fine-tuning
+- **MATH Dataset** by Dan Hendrycks for problem-solving benchmarks
+
+## Citation
+If you use this model, please cite:
+```bibtex
+@article{BullSolve2025,
+  title={BullSolve: Fine-Tuned LLaMA 3 for Math Problems},
+  authors={Kevin Fabio Ramos López and Kevin Camilo Rincon Bohorquez and Nolhan Dumoulin},
+  year={2025},
+  journal={Hugging Face Models}
+}
+```
+# Uploaded  model
+
+- **Developed by:** nivektk
+- **License:** apache-2.0
+- **Finetuned from model :** unsloth/llama-3.1-8B-Instruct-unsloth-bnb-4bit
+
+This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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+    "model_type": "llama"
+}
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