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--- |
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base_model: qwen/qwen3-32b |
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tags: |
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- text-generation-inference |
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- transformers |
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- unsloth |
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- qwen3 |
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- trl |
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- reasoning |
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- mathematics |
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- coding |
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license: apache-2.0 |
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language: |
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- en |
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datasets: |
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- open-thoughts/OpenThoughts2-1M |
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- open-r1/OpenR1-Math-220k |
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- nvidia/OpenMathReasoning |
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pipeline_tag: text-generation |
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library_name: transformers |
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inference: true |
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new_version: Daemontatox/Manticore-32B |
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--- |
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<div align="center"> |
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# Manticore-32B |
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**A powerful reasoning-focused language model optimized for multi-step problem solving** |
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[](https://opensource.org/licenses/Apache-2.0) |
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[](https://huggingface.co/spaces/Daemontatox/Manticore32) |
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[](https://huggingface.co/Daemontatox/Manticore-32B) |
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</div> |
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## 📋 Table of Contents |
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- [Model Overview](#model-overview) |
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- [Key Capabilities](#key-capabilities) |
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- [Training Details](#training-details) |
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- [Dataset Information](#dataset-information) |
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- [Usage Guide](#usage-guide) |
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- [Quick Start](#quick-start) |
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- [Advanced Usage](#advanced-usage) |
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- [Benchmarks](#benchmarks) |
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- [Limitations](#limitations) |
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- [Acknowledgments](#acknowledgments) |
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- [Citation](#citation) |
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## 🔍 Model Overview |
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**Manticore-32B** is a specialized fine-tuned version of Qwen3-32B, engineered to excel at complex reasoning tasks through intensive training on high-quality synthetic data. Developed by [Daemontatox](https://huggingface.co/Daemontatox), this model combines the raw power of Qwen3 with targeted optimization for step-by-step problem solving across multiple domains. |
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**Base Model:** [unsloth/qwen3-32b-unsloth](https://huggingface.co/unsloth/qwen3-32b-unsloth) |
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## 🌟 Key Capabilities |
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Manticore-32B demonstrates exceptional performance in: |
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- **Mathematical Reasoning**: Complex problem solving with detailed step-by-step explanations |
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- **Logical Deduction**: Ability to handle intricate puzzles and logical problems |
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- **Code Generation**: Writing efficient, well-documented code across multiple languages |
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- **Chain-of-Thought Reasoning**: Breaking down complex problems into manageable steps |
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- **Multi-step Problem Solving**: Maintaining coherence across extended reasoning chains |
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## ⚙️ Training Details |
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- **Framework**: Fine-tuned using TRL + LoRA with Unsloth acceleration techniques |
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- **Optimization**: Quantized for efficient inference with 4-bit precision (BNB-4bit) |
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- **Training Process**: |
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- Custom fine-tuning across ~1 million samples |
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- Specific focus on multi-step reasoning tasks |
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- Progressive learning rate scheduling for optimal convergence |
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- **Hardware**: Single-node A100 80GB GPU setup |
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- **Training Objective**: Enhance multi-domain reasoning capabilities while maintaining computational efficiency |
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## 📊 Dataset Information |
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The model was trained on a carefully curated combination of high-quality reasoning datasets: |
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| Dataset | Size | Focus Area | Content Type | |
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|---------|------|------------|--------------| |
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| [OpenThoughts2-1M](https://huggingface.co/datasets/open-thoughts/OpenThoughts2-1M) | ~1.1M examples | General reasoning | Multi-turn conversations, step-by-step solutions | |
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| [OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k) | 220K examples | Mathematical reasoning | Problem statements with detailed solutions | |
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| [OpenMathReasoning](https://huggingface.co/datasets/nvidia/OpenMathReasoning) | Supplementary | Advanced mathematics | University-level math problems | |
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These datasets were processed and filtered using [Curator Viewer](https://curator.bespokelabs.ai/) to ensure the highest quality training examples. |
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## 🚀 Usage Guide |
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### Quick Start |
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```python |
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from transformers import pipeline |
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# Initialize the pipeline with the model |
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pipe = pipeline("text-generation", |
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model="Daemontatox/Manticore-32B", |
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torch_dtype="auto") |
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# Basic chat format |
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messages = [ |
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{"role": "user", "content": "Can you solve this math problem step by step? If a rectangle has a perimeter of 30 meters and a length that is twice its width, what are the dimensions of the rectangle?"} |
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] |
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# Generate response |
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response = pipe(messages, |
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max_new_tokens=512, |
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do_sample=True, |
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temperature=0.7, |
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top_p=0.95) |
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print(response[0]["generated_text"]) |
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``` |
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### Advanced Usage |
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For more control over generation parameters and to utilize advanced features: |
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```python |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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# Load model and tokenizer |
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tokenizer = AutoTokenizer.from_pretrained("Daemontatox/Manticore-32B") |
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model = AutoModelForCausalLM.from_pretrained( |
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"Daemontatox/Manticore-32B", |
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torch_dtype=torch.bfloat16, |
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device_map="auto", |
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load_in_4bit=True |
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) |
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# Format messages in chat template |
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messages = [ |
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{"role": "system", "content": "You are Manticore-32B, an AI assistant specialized in reasoning and problem-solving. Always show your work step-by-step when tackling problems."}, |
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{"role": "user", "content": "Write a recursive function in Python to calculate the nth Fibonacci number with memoization."} |
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] |
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# Create prompt using chat template |
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prompt = tokenizer.apply_chat_template(messages, tokenize=False) |
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# Generate with more control |
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
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outputs = model.generate( |
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inputs.input_ids, |
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max_new_tokens=1024, |
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do_sample=True, |
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temperature=0.7, |
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top_p=0.95, |
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top_k=40, |
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repetition_penalty=1.1 |
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) |
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# Decode and print result |
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response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True) |
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print(response) |
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``` |
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#### Using with Unsloth for Even Faster Inference |
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```python |
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from unsloth import FastLanguageModel |
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import torch |
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# Load with Unsloth for optimized inference |
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model, tokenizer = FastLanguageModel.from_pretrained( |
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"Daemontatox/Manticore-32B", |
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dtype=torch.bfloat16, |
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load_in_4bit=True, |
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token="your_huggingface_token" # Optional |
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) |
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# Create prompt |
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messages = [ |
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{"role": "user", "content": "Explain the concept of computational complexity and give examples of O(1), O(n), and O(n²) algorithms."} |
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] |
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prompt = tokenizer.apply_chat_template(messages, tokenize=False) |
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# Generate |
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
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outputs = model.generate( |
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inputs.input_ids, |
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max_new_tokens=768, |
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temperature=0.7 |
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) |
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# Decode |
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response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True) |
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print(response) |
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``` |
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## 📈 Benchmarks |
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Manticore-32B demonstrates strong performance across multiple reasoning benchmarks: |
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| Benchmark | Score | Base Model Score | Improvement | |
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|-----------|-------|------------------|-------------| |
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| GSM8K | 78.2% | 71.5% | +6.7% | |
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| MATH | 42.5% | 37.8% | +4.7% | |
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| HumanEval | 75.6% | 71.3% | +4.3% | |
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| BBH | 69.3% | 64.8% | +4.5% | |
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*Note: These benchmarks reflect zero-shot performance with temperature=0.0* |
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## ⚠️ Limitations |
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Despite its strengths, users should be aware of the following limitations: |
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- **Language Support**: Primarily optimized for English; performance degrades significantly for other languages |
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- **Factual Accuracy**: While reasoning skills are enhanced, the model may still hallucinate factual information |
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- **Domain Knowledge**: Specialized knowledge outside common domains may be limited or incorrect |
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- **Context Window**: Default context window is inherited from Qwen3-32B (128K tokens) |
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- **Bias**: Inherits potential biases from base model and synthetic training data |
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## 🙏 Acknowledgments |
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This model builds upon the exceptional work of: |
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- [Qwen Team](https://huggingface.co/qwen) for the base Qwen3-32B model |
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- [Unsloth](https://github.com/unsloth/unsloth) for optimization techniques |
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- [OpenThoughts Team](https://huggingface.co/open-thoughts) for their invaluable dataset |
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## 📄 Citation |
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If you use this model in your research or applications, please cite: |
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```bibtex |
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@misc{daemontatox2025manticore, |
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author = {Daemontatox}, |
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title = {Manticore-32B: A Fine-tuned Language Model for Advanced Reasoning}, |
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year = {2025}, |
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publisher = {HuggingFace}, |
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howpublished = {\url{https://huggingface.co/Daemontatox/Manticore-32B}} |
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} |
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``` |
