Best Model

Has more neurons that you probably do

README.md

@@ -1,3 +1,159 @@
+# Matrix 2
+
+## Model Description
+
+**Matrix 2** is a fine-tuned version of [DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B), trained on a focused mixture of chain-of-thought reasoning, math, coding, and logic data. It is the flagship reasoning model of the Inelly lineup -- built for deep, accurate, step-by-step problem solving.
+
+- **Developed by:** Bry (GenueAI)
+- **Base model:** DeepSeek-R1-Distill-Qwen-7B
+- **Fine-tuning method:** QLoRA (4-bit NF4, rank 16)
+- **Parameters:** 7.62B (base) + ~6.5M trainable (LoRA adapters)
+- **License:** MIT (inherited from DeepSeek-R1)
+
 ---
-license: mit
+
+## Intended Use
+
+Matrix 2 is intended for:
+
+- **Deep Chain-of-Thought reasoning** – Multi-step problem solving with clear logic
+- **Mathematics** – Algebra, arithmetic, word problems, multi-step calculations
+- **Code generation** – Python functions with proper logic and comments
+- **Logical deduction** – Syllogisms, puzzles, transitive reasoning
+- **Scientific explanations** – Physics, biology, general science
+- **Complex instruction following** – Multi-part tasks requiring structured thinking
+
+### Out of Scope
+
+- Not intended for production deployment without further safety evaluation
+- Safety alignment inherited from DeepSeek-R1 base; fine-tuning data did not include adversarial safety examples
+- Larger memory footprint than 1.5B/3B variants (~5.2GB)
+
 ---
+
+## Training Data
+
+Matrix 2 was fine-tuned for 1 epoch on ~5,225 samples drawn from:
+
+| Dataset | Samples | Purpose |
+|---|---|---|
+| [Bespoke-Stratos-35k](https://huggingface.co/datasets/bespokelabs/Bespoke-Stratos-35k) | 3,000 | Chain-of-thought math & reasoning |
+| [OpenThoughts-114k](https://huggingface.co/datasets/open-thoughts/OpenThoughts-114k) | 2,500 | Code generation with reasoning |
+| [dolphin-r1](https://huggingface.co/datasets/cognitivecomputations/dolphin-r1) | 2,000 | General reasoning (DeepSeek-R1 distill) |
+
+All samples were deduplicated and reasoning-weighted (2x oversample for CoT examples). Maximum sequence length: 512 tokens.
+
+---
+
+## Training Hyperparameters
+
+| Parameter | Value |
+|---|---|
+| Base model | DeepSeek-R1-Distill-Qwen-7B |
+| Quantization | 4-bit NF4 (bitsandbytes) |
+| LoRA rank | 16 |
+| LoRA alpha | 32 |
+| LoRA dropout | 0.05 |
+| Learning rate | 2e-4 |
+| Batch size | 8 (gradient accumulation) |
+| Epochs | 1 |
+| Max seq length | 512 |
+| Optimizer | AdamW 8-bit |
+| LR scheduler | cosine |
+| Warmup ratio | 0.05 |
+| Training time | ~74 min |
+| Hardware | RTX 3090 (24GB VRAM) |
+
+---
+
+## Model Architecture
+
+| Property | Value |
+|---|---|
+| Model type | Qwen2ForCausalLM |
+| Hidden size | 3,584 |
+| Layers | 28 |
+| Attention heads | 28 |
+| Head dim | 128 |
+| Intermediate size | 18,944 |
+| Vocab size | 152,064 |
+| Context length | 131,072 |
+| Total parameters | ~7.62B |
+| Trainable parameters | ~6.5M (LoRA) |
+
+---
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("path/to/matrix-2", torch_dtype=torch.float16, device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("path/to/matrix-2")
+
+messages = [{"role": "user", "content": "Solve for x: 3x + 7 = 22. Show all steps."}]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+
+output = model.generate(**inputs, max_new_tokens=256, temperature=0.7, top_p=0.9)
+response = tokenizer.decode(output[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
+print(response)
+```
+
+---
+
+## Performance
+
+Informal GPU testing across 8 categories:
+
+| Category | Result |
+|---|---|
+| Chain-of-Thought reasoning | ✅ Excellent multi-step logic |
+| Math | ✅ Accurate with detailed work shown |
+| Code generation | ✅ Clean, well-commented Python |
+| Logic puzzles | ✅ Thorough deductive reasoning |
+| General knowledge | ✅ Accurate, detailed explanations |
+| Complex reasoning | ✅ Handles multi-step word problems well |
+
+---
+
+## Inelly / GenueAI Model Family
+
+| Model | Size | Focus |
+|---|---|---|
+| **Matrix 2** (this model) | 7B | Deep CoT reasoning, math, coding |
+| Inelly 4.5 | 3B | Conversation + politeness + CoT |
+| Inelly 4.5 Blaze | 1.5B | Fast reasoning + CoT |
+
+---
+
+## Limitations
+
+- **Safety:** Inherited from DeepSeek-R1 base; not specifically safety-tuned. May occasionally follow harmful instructions.
+- **Memory:** Requires ~5.2GB VRAM for inference (FP16)
+- **Context length:** Fine-tuned on 512-token sequences; base supports 128K but fine-tuned performance is optimized for shorter contexts
+- **Factual accuracy:** May hallucinate in specialized domains (law, medicine, finance)
+- **Speed:** Slower than 1.5B/3B variants due to size
+
+---
+
+## Acknowledgments
+
+- [DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) by DeepSeek AI (base model)
+- [Bespoke Labs](https://huggingface.co/bespokelabs) for Stratos dataset
+- [OpenThoughts](https://huggingface.co/datasets/open-thoughts/OpenThoughts-114k) team
+- [Cognitive Computations](https://huggingface.co/cognitivecomputations) for dolphin-r1
+
+---
+
+## Citation
+
+```
+@misc{matrix2,
+  title = {Matrix 2: A 7B Chain-of-Thought Reasoning Model},
+  author = {Bry},
+  organization = {GenueAI},
+  year = {2026},
+  note = {Fine-tuned from DeepSeek-R1-Distill-Qwen-7B using QLoRA},
+}
+```