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README.md

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+# Inelly 4.5 Blaze
+
+## Model Description
+
+**Inelly 4.5 Blaze** is a fine-tuned version of [Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct), trained on a focused mixture of chain-of-thought reasoning, math, coding, and general knowledge data. It is the compact, fast variant of the Inelly 4.5 family -- optimized for quick inference while retaining strong reasoning capabilities.
+
+- **Developed by:** bry (Braden Lundberg)
+- **Base model:** Qwen2.5-1.5B-Instruct
+- **Fine-tuning method:** QLoRA (4-bit NF4, rank 16)
+- **Parameters:** 1.54B (base) + ~3.1M trainable (LoRA adapters)
+- **License:** Apache 2.0 (inherited from Qwen2.5)
+
 ---
-license: mit
+
+## Intended Use
+
+Inelly 4.5 Blaze is intended for:
+
+- **Chain-of-Thought reasoning** – Step-by-step problem solving
+- **Math** – Algebra, arithmetic, word problems
+- **Code generation** – Python functions with clear logic
+- **Logical deduction** – Syllogisms, puzzles, multi-step reasoning
+- **General knowledge Q&A** – Science, everyday facts
+- **Quick prototyping** – Fast inference on consumer hardware
+
+### Out of Scope
+
+- Not intended for production deployment without further safety evaluation
+- Less conversational polish than the 3B variant (Inelly 4.5)
+- May struggle with very long or complex multi-step tasks
+
 ---
+
+## Training Data
+
+Inelly 4.5 Blaze 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 | Qwen2.5-1.5B-Instruct |
+| 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 | ~35 min |
+| Hardware | RTX 2080 Ti (11GB VRAM) |
+
+---
+
+## Model Architecture
+
+| Property | Value |
+|---|---|
+| Model type | Qwen2ForCausalLM |
+| Hidden size | 1,536 |
+| Layers | 28 |
+| Attention heads | 12 |
+| Head dim | 128 |
+| Intermediate size | 8,960 |
+| Vocab size | 151,936 |
+| Context length | 32,768 |
+| Total parameters | ~1.54B |
+| Trainable parameters | ~3.1M (LoRA) |
+
+---
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("path/to/inelly-4.5-blaze", torch_dtype=torch.float16, device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("path/to/inelly-4.5-blaze")
+
+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 | ✅ Correct step-by-step logic |
+| Math | ✅ Accurate algebraic solutions |
+| Code generation | ✅ Clean Python with comments |
+| Logic puzzles | ✅ Sound deductive reasoning |
+| General knowledge | ✅ Accurate, clear explanations |
+| Speed | ✅ ~1-2s per response (faster than 3B/7B) |
+
+---
+
+## Inelly 4.5 Family Comparison
+
+| Model | Size | Focus | Training Data |
+|---|---|---|---|
+| **Inelly 4.5** | 3B | Conversation + CoT | 5,700 samples (incl. politeness, conv) |
+| **Inelly 4.5 Blaze** (this) | 1.5B | Fast reasoning + CoT | 5,225 samples (reasoning-focused) |
+| Inelly 7B CoT | 7B | Deep reasoning | 5,225 samples (reasoning-focused) |
+
+**When to use Blaze vs standard 4.5:**
+- **Blaze** – When you need fast reasoning, math, or coding help and don't need conversational polish
+- **4.5 (3B)** – When you want a friendly, polite conversationalist that can also reason
+
+---
+
+## Limitations
+
+- **Conversational ability:** Less polished in casual chat compared to the 3B variant (no conversational fine-tuning data)
+- **Safety:** Inherited from Qwen2.5 base; not specifically safety-tuned
+- **Context length:** Fine-tuned on 512-token sequences
+- **Factual accuracy:** May hallucinate in specialized domains
+
+---
+
+## Acknowledgments
+
+- [Qwen2.5](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) by Alibaba Cloud (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{inelly45blaze,
+  title = {Inelly 4.5 Blaze: A Compact Chain-of-Thought Reasoning Model},
+  author = {Braden Lundberg},
+  year = {2026},
+  note = {Fine-tuned from Qwen2.5-1.5B-Instruct using QLoRA},
+}
+```