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README.md

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 ---
 license: apache-2.0
+base_model: Qwen/Qwen2.5-3B-Instruct
+language:
+  - en
+tags:
+  - reasoning
+  - math
+  - chain-of-thought
+  - qwen2.5
+  - gguf
+  - text-generation
+  - conversational
+pipeline_tag: text-generation
+library_name: transformers
 ---
-# Boomslang: a 3B parameter reasoning model based off Qwen-2.5-3B.
-![1789911910f175](https://cdn-uploads.huggingface.co/production/uploads/6a5f8c87a38cac087c2c6c05/Ag4eWdDI71YPtuwsP7Z5e.png)
+
+<div align="center">
+
+# 🐍 Boomslang (3B Reasoning & Math Engine)
+
+**A compact, high-efficiency 3-billion-parameter model fine-tuned for deep chain-of-thought mathematical reasoning, logic, and algebra—without sacrificing natural conversational ability.**
+
+[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Creator-Monster--Code-blue)](https://huggingface.co/Monster-Code)
+[![License](https://img.shields.io/badge/License-Apache_2.0-green.svg)](https://opensource.org/licenses/Apache-2.0)
+[![GGUF Available](https://img.shields.io/badge/GGUF-Included-purple.svg)](https://huggingface.co/Monster-Code/Boomslang/blob/main/boomslang-3b-qwen.gguf)
+
+---
+
+### ❤️ If you find Boomslang useful, please hit the **Like** button at the top of this page and [Follow @Monster-Code](https://huggingface.co/Monster-Code) for more open-weights AI releases!
+
+---
+
+</div>
+
+## 💡 What is Boomslang?
+
+Most small models (1B–3B parameters) struggle with two extremes: they are either polite chatbots that completely hallucinate basic arithmetic, or narrow math models that forget how to hold a conversation and start writing unprompted proofs when you simply say "Hi."
+
+**Boomslang** was trained to bridge that gap.
+
+Starting from the strong foundation of **`Qwen/Qwen2.5-3B-Instruct`**, Boomslang was post-trained on an **NVIDIA RTX PRO 6000 Blackwell** across a curated ~17,500-sample reasoning mixture:
+1. **DeepSeek-R1 Distilled Proofs (`open-r1/OpenR1-Math-220k`):** Teaches the network an internal self-reflection loop (`<think> ... </think>`) to break down complex algebraic expressions, geometry, and multi-step deduction before committing to an answer.
+2. **Step-by-Step Arithmetic Rigor (`openai/gsm8k`):** Calibrates attention heads on strict order-of-operations arithmetic and unambiguous answer derivation.
+
+The result is an edge-friendly 3B model that works through tricky algebra and word puzzles methodically, but still greets you warmly and follows instructions when you just want to talk.
+
+---
+
+## 📦 What's Inside This Repository?
+
+* **Single Standalone `model.safetensors`:** No multi-part file splits. The entire 3-billion parameter model is packed into a single, clean ~6.1 GB file.
+* **Pre-Converted GGUF (`boomslang-3b-qwen.gguf`):** Directly ready for **Ollama**, **LM Studio**, and **llama.cpp** on your local machine (MacBook, laptop, or home GPU).
+* Full configuration and tokenizer files for immediate `transformers` plug-and-play.
+
+---
+
+## ⚡ Quickstart: Python & Transformers
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer
+
+MODEL_ID = "Monster-Code/Boomslang"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
+    device_map="auto"
+)
+
+# Standard ChatML format
+messages = [
+    {
+        "role": "system",
+        "content": "You are Boomslang, a helpful and precise reasoning assistant. Reason step-by-step before providing your final answer."
+    },
+    {
+        "role": "user",
+        "content": "A box has 36 chocolates. Tom eats 1/4 of them, and Sarah eats 9. How many chocolates are left in the box?"
+    }
+]
+
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+
+with torch.no_grad():
+    model.generate(
+        **inputs,
+        streamer=streamer,
+        max_new_tokens=1024,
+        temperature=0.6,
+        top_p=0.9
+    )
+```
+
+---
+
+## 💻 Running Locally with Ollama / LM Studio
+
+### 1. Using Ollama:
+Create a `Modelfile` in the same directory as the downloaded `.gguf`:
+```dockerfile
+FROM ./boomslang-3b-qwen.gguf
+
+TEMPLATE """{{ if .System }}<|im_start|>system
+{{ .System }}<|im_end|>
+{{ end }}{{ if .Prompt }}<|im_start|>user
+{{ .Prompt }}<|im_end|>
+{{ end }}<|im_start|>assistant
+{{ .Response }}<|im_end|>"""
+
+PARAMETER stop "<|im_start|>"
+PARAMETER stop "<|im_end|>"
+PARAMETER temperature 0.6
+```
+
+Build and run:
+```bash
+ollama create boomslang -f Modelfile
+ollama run boomslang
+```
+
+### 2. Using LM Studio:
+1. Download **`boomslang-3b-qwen.gguf`** from the [Files and versions tab](https://huggingface.co/Monster-Code/Boomslang/tree/main).
+2. Drop it into your local LM Studio models folder.
+3. Start chatting with GPU offloading enabled!
+
+---
+
+## 🧠 Sample Interaction
+
+**User:**
+> *"Solve for x: 5x - 10 = 25. Show your work."*
+
+**Boomslang:**
+```text
+<think>
+The equation is 5x - 10 = 25.
+Our goal is to isolate the variable x on one side.
+
+Step 1: Add 10 to both sides to cancel out the -10 constant:
+5x - 10 + 10 = 25 + 10
+5x = 35
+
+Step 2: Divide both sides by the coefficient 5:
+5x / 5 = 35 / 5
+x = 7
+
+Verification:
+Substitute x = 7 into the original expression:
+5(7) - 10 = 35 - 10 = 25.
+Both sides match.
+</think>
+
+The answer is x = 7.
+```
+
+---
+
+## 🛠️ Training Details
+
+* **Base Architecture:** Qwen2.5 3B (Decoder-only Transformer)
+* **Hardware:** NVIDIA RTX PRO 6000 Blackwell Server Edition
+* **Precision:** BF16 Native Mixed Precision with Fused AdamW
+* **Effective Batch Size:** 32 (8 per device × 4 gradient accumulation steps)
+* **Learning Rate:** 1.5e-4 with dynamic linear warmup
+* **Label Masking:** Dynamic batch padding via `DataCollatorForSeq2Seq` with `-100` masking to guarantee loss is never calculated on padding noise
+
+---
+
+## 🤝 Community & Support
+
+* 👤 **Creator:** [Monster-Code](https://huggingface.co/Monster-Code)
+* 💬 Have suggestions, evaluation runs, or dataset ideas? Leave a note in the **Discussions** tab!
+* ⭐ **If Boomslang helps your workflow, please consider starring/liking the repository!**