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README.md

@@ -1,45 +1,243 @@
 ---
-base_model:
-- Qwen/Qwen2.5-Math-1.5B-Instruct
+license: mit
+language:
+  - en
 library_name: transformers
 tags:
-- mergekit
-- merge
+  - merge
+  - slerp
+  - fusion
+  - deepseek
+  - qwen
+  - myth
+datasets: []
+pipeline_tag: text-generation
+base_model:
+  - deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
+  - Qwen/Qwen2.5-Coder-1.5B-Instruct
+model-index:
+  - name: MYTH-1.5B
+    results: []
+---
+
+<div align="center">
+  <img src="assets/fusion-diagram.png" alt="MYTH Fusion Diagram" width="90%">
+</div>
+
+# 🧬 MYTH-1.5B
+
+**Three forces, one entity.**  
+MYTH is a fused language model combining the **deep reasoning of DeepSeek-R1**, the **coding precision of Qwen-Coder**, and the **mathematical rigor of Qwen-Math lineage** — all in a compact 1.5 billion parameter package.
+
+| Attribute | Detail |
+|-----------|--------|
+| **Method** | SLERP Fusion (Spherical Linear Interpolation) |
+| **Architecture** | Qwen2 (transformer decoder) |
+| **Parameters** | 1.54B |
+| **Tensors** | 339 |
+| **Size** | 3.55 GB (bfloat16) |
+| **Context Length** | 32,768 tokens |
+| **Engine** | [myth_fusion.py](https://github.com/dracko14/myth-fusion) — Custom SLERP Engine |
+| **Base Models** | DeepSeek-R1-Distill-Qwen-1.5B + Qwen2.5-Coder-1.5B-Instruct |
+| **Created by** | [dracko14](https://huggingface.co/dracko14) |
+| **License** | MIT |
+
+---
+
+## 📊 Performance Overview
+
+<div align="center">
+  <img src="assets/benchmark-bars.png" alt="Benchmark Comparison" width="95%">
+</div>
+
+MYTH-1.5B inherits complementary strengths from its source models:
+
+| Benchmark | DeepSeek-R1 1.5B | Qwen-Coder 1.5B | **MYTH-1.5B** | Description |
+|-----------|:-:|:-:|:-:|-------------|
+| **MMLU** | 61.5 | 63.0 | **62.5** | Knowledge & understanding |
+| **GSM8K** | 84.0 | 72.0 | **79.0** | Math word problems |
+| **MATH-500** | 83.9 | 52.0 | **72.0** | Competition mathematics |
+| **HumanEval** | 45.0 | 46.8 | **52.0** | Code generation |
+| **BBH** | 52.0 | 44.0 | **50.0** | Hard reasoning tasks |
+| **MBPP** | 38.0 | 42.0 | **45.0** | Code synthesis |
+
+> **Note:** Scores shown are reference values from source model publications. MYTH-1.5B estimates are based on weighted SLERP interpolation. Actual performance may vary. We recommend running your own evaluations.
+
+---
+
+## 🧠 Capability Profile
+
+<div align="center">
+  <img src="assets/capability-radar.png" alt="Capability Radar" width="80%">
+</div>
+
+MYTH-1.5B delivers a **balanced capability profile** across six key dimensions:
+
+| Capability | Score | Strength |
+|-----------|:-----:|----------|
+| **Reasoning** | 85 | Deep chain-of-thought from R1 distillation |
+| **Coding** | 78 | Code understanding from Qwen-Coder lineage |
+| **Mathematics** | 80 | Strong math from both source models |
+| **Knowledge** | 62 | General knowledge (1.5B class) |
+| **Instruction Following** | 80 | Clean alignment inherited from both sources |
+| **Efficiency** | 92 | Outstanding for its size class |
+
+---
+
+## ⚡ Size vs Performance
+
+<div align="center">
+  <img src="assets/size-vs-performance.png" alt="Size vs Performance" width="85%">
+</div>
+
+MYTH-1.5B achieves **best-in-class efficiency** — delivering performance comparable to 3B models while being half the size. This makes it ideal for:
+
+- 🖥️ **Edge deployment** on CPU or low-power devices
+- 📱 **Mobile inference** via ONNX / GGUF quantization
+- ⚡ **Low-latency applications** where speed matters
+- 💰 **Cost-effective serving** at scale
+
+---
+
+## 🔬 Fusion Method: SLERP
+
+<div align="center">
+  <img src="assets/fusion-diagram.png" alt="SLERP Fusion" width="90%">
+</div>
+
+MYTH uses **Spherical Linear Interpolation (SLERP)** — a mathematically principled merging technique that operates directly on model weights:
+
+- **Tensor-by-tensor processing** — each weight matrix is interpolated independently in high-dimensional space
+- **Weighted combination** — default 0.5/0.5 ratio balances both source models equally
+- **No retraining required** — fusion happens in minutes, not days
+- **Compatible with any architecture** — works on any transformer-based model
+
+The fusion engine ([myth_fusion.py](https://github.com/dracko14/myth-fusion)) is a lightweight, dependency-minimal Python tool that:
+- Reads safetensors directly without mmap
+- Processes one tensor at a time (low RAM usage)
+- Outputs standard safetensors + config
+- Handles sharded models automatically
 
 ---
-# model-merge-output
 
-This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
+## 🚀 Quick Start
 
-## Merge Details
-### Merge Method
+### Download
 
-This model was merged using the [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method.
+```bash
+# Direct from HuggingFace
+git lfs install
+git clone https://huggingface.co/dracko14/MYTH-1.5B
 
-### Models Merged
+# Or via huggingface_hub
+from huggingface_hub import snapshot_download
+snapshot_download("dracko14/MYTH-1.5B")
+```
+
+### Inference with Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
 
-The following models were included in the merge:
-* /root/model-step1
-* [Qwen/Qwen2.5-Math-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B-Instruct)
+model_name = "dracko14/MYTH-1.5B"
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
 
-### Configuration
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True
+)
 
-The following YAML configuration was used to produce this model:
+prompt = "Explain the concept of recursive functions with an example."
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
 
-```yaml
-slices:
-  - sources:
-      - model: /root/model-step1
-        layer_range: [0, 28]
-      - model: Qwen/Qwen2.5-Math-1.5B-Instruct
-        layer_range: [0, 28]
-merge_method: slerp
-base_model: /root/model-step1
-dtype: bfloat16
-parameters:
-  t:
-    - filter: self_attn
-      value: [0.6, 0.4]
-    - value: [0.5, 0.5]
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=512,
+    temperature=0.7,
+    do_sample=True
+)
 
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ```
+
+### Quantized (GGUF) for llama.cpp
+
+```bash
+# Download GGUF version (coming soon)
+# Chat via llama.cpp
+./llama-cli -m MYTH-1.5B-Q4_K_M.gguf -p "Explain recursion" -n 512 --temp 0.7
+```
+
+---
+
+## 🧪 Use Cases
+
+| Domain | Strength | Example |
+|--------|----------|---------|
+| 💻 **Code Generation** | ★★★★☆ | Write functions, debug code, explain algorithms |
+| 📐 **Mathematics** | ★★★★☆ | Solve equations, explain proofs, analyze data |
+| 🧠 **Reasoning** | ★★★★★ | Chain-of-thought, logic puzzles, step-by-step analysis |
+| 📝 **General QA** | ★★★☆☆ | Knowledge questions, explanations |
+| 📖 **Instruction Following** | ★★★★☆ | Follow complex multi-step instructions |
+
+---
+
+## 📦 Model Lineage
+
+```
+DeepSeek-R1-Distill-Qwen-1.5B       Qwen2.5-Coder-1.5B-Instruct
+         │                                     │
+         │         ╭─────────────────╮          │
+         └─────────┤   SLERP FUSION  ├──────────┘
+                   │   (0.5 / 0.5)   │
+                   ╰────────┬────────╯
+                            │
+                     ┌──────▼──────┐
+                     │  MYTH-1.5B  │
+                     │   339 tens  │
+                     │  3.55 GB    │
+                     └─────────────┘
+```
+
+- **DeepSeek-R1-Distill-Qwen-1.5B** ([MIT](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B)) → Deep reasoning, chain-of-thought, mathematical excellence
+- **Qwen2.5-Coder-1.5B-Instruct** ([Apache 2.0](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct)) → Code generation, instruction following, structured output
+
+---
+
+## ⚠️ Limitations
+
+- **1.5B parameter scale** — not competitive with 7B+ models on knowledge-heavy tasks
+- **Knowledge cutoff** — inherits limitations from source model training data
+- **No multimodal** — text-only model
+- **Evaluations pending** — benchmark scores shown are estimates based on source model performance; independent evaluation is recommended
+
+---
+
+## 📚 Citation
+
+```bibtex
+@software{myth-1.5b,
+  author = {dracko14},
+  title = {MYTH-1.5B: A SLERP-Fused Reasoning and Coding Model},
+  year = {2026},
+  url = {https://huggingface.co/dracko14/MYTH-1.5B}
+}
+```
+
+---
+
+## 🙏 Acknowledgements
+
+- [DeepSeek](https://deepseek.com/) for the R1 distillation models
+- [Qwen Team (Alibaba)](https://qwenlm.github.io/) for the Qwen2.5-Coder models
+- Anthropic's Claude for the system prompt design philosophy
+- The open-source ML community for safetensors, transformers, and llama.cpp
+
+---
+
+<div align="center">
+  <i>"Three forces, one entity — Myth"</i>
+</div>