README.md

---
license: mit
library_name: transformers
tags:
- merge
- slerp
- qwen3.5
- myth
- reasoning
- coding
- math
base_model:
- Qwen/Qwen3.5-4B
- Jackrong/Qwen3.5-4B-Neo
pipeline_tag: text-generation
language:
- en
- multilingual
extra_gated_prompt: "This model is open for all. No special permission needed."
---

# 🧬 Myth 4B

**Myth 4B** is a fusion of [Qwen 3.5 4B](https://huggingface.co/Qwen/Qwen3.5-4B) and [Qwen3.5-4B-Neo](https://huggingface.co/Jackrong/Qwen3.5-4B-Neo) via SLERP (Spherical Linear Interpolation), built with a custom fusion engine that operates directly on safetensors — no mergekit required.

> "Three forces, one entity — Myth"

## Model Details

### Architecture

| Attribute | Value |
|---|---|
| **Base Model** | Qwen 3.5 4B |
| **Parameters** | 4B |
| **Context Length** | 32K |
| **Architecture** | `qwen3_5` |
| **Format** | FP16 (sharded, 8 files) + GGUF Q4_K_M |
| **Fusion Method** | SLERP (50% Qwen 3.5 + 50% Neo) |

### Lineage

```
Qwen 3.5 4B (multimodal base)
        |
        ├── SLERP ──→ 🧬 Myth 4B
        |
Qwen3.5-4B-Neo (reasoning expert)
```

Myth inherits:
- **Deep reasoning** from Neo's reasoning-focused fine-tuning
- **Multimodal capabilities** from Qwen 3.5's native architecture
- **Coding & math** from Qwen 3.5's strong foundation
- **1M-token capable** via Qwen 3.5's extended context architecture

## Fusion Engine

Unlike most merges that use mergekit, Myth 4B was merged using a **custom-built fusion engine**:

```
myth_fusion.py — Custom SLERP Engine
  • Reads safetensors directly (no transformers dependency)
  • Processes one tensor at a time (low memory footprint)
  • Saves in shards (8 shards × ~100 tensors each)
  • Supports any model architecture
  • No GPU required
```

738 tensors were merged in 2 stages, with shard-based saving to prevent OOM on 8GB RAM hardware.

## Files

| File | Size | Description |
|---|---|---|
| `model-*.safetensors` (×8) | 8.8 GB total | FP16 model weights (sharded) |
| `Myth-4B-Q4_K_M.gguf` | 2.78 GB | Quantized GGUF (Q4_K_M) |
| `myth_system_prompt.md` | 5 KB | Custom system prompt (Claude Fable 5-inspired) |

## Usage

### Transformers (Python)

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "dracko14/Myth-4B",
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(
    "dracko14/Myth-4B",
    trust_remote_code=True
)

messages = [
    {"role": "system", "content": "You are Myth, a helpful AI assistant."},
    {"role": "user", "content": "Write a Python function for binary search."},
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

### llama.cpp / Ollama

```bash
# Download GGUF
wget https://huggingface.co/dracko14/Myth-4B/resolve/main/Myth-4B-Q4_K_M.gguf

# Run with llama.cpp
./llama-cli -m Myth-4B-Q4_K_M.gguf -p "Hello" -n 256

# Or with Ollama (create Modelfile first)
echo "FROM ./Myth-4B-Q4_K_M.gguf" > Modelfile
ollama create myth -f Modelfile
ollama run myth
```

### System Prompt

Myth includes a custom system prompt inspired by Claude Fable 5:
[📄 myth_system_prompt.md](https://huggingface.co/dracko14/Myth-4B/blob/main/myth_system_prompt.md)

## Benchmarks

*Benchmarks coming soon. Myth 4B inherits Qwen 3.5 4B's strong baseline with enhanced reasoning from Neo.*

## Training & Merge Details

| Detail | Value |
|---|---|
| **Compute** | 8GB RAM VPS (CPU only) |
| **Merge Time** | ~15 minutes (738 tensors) |
| **Quantization** | llama.cpp (162 seconds) |
| **Upload** | ~10 minutes |
| **Total Cost** | $0 (free VPS + free HuggingFace) |

## Limitations

- Base model knowledge cutoff applies
- Performance on CPU may be slow (4B params)
- Not fine-tuned for specific downstream tasks
- May exhibit base model biases

## License

MIT — open for all use cases.

## Links

- 📄 [System Prompt](https://huggingface.co/dracko14/Myth-4B/blob/main/myth_system_prompt.md)
- 🌐 [Space Documentation](https://huggingface.co/spaces/dracko14/myth)
- ⚙️ [Fusion Engine](https://github.com/yourusername/myth-fusion) (coming soon)
- 🧬 [Qwythos-9B-v2](https://huggingface.co/empero-ai/Qwythos-9B-v2) (Mythos-class 9B model)

---

*Built with ❤️ using [myth_fusion.py](https://huggingface.co/spaces/dracko14/myth) — a custom SLERP fusion engine*