Myth-4B

🧬 Myth 4B

Myth 4B is a fusion of Qwen 3.5 4B and 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

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

Usage

Transformers (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

# 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

Benchmarks

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

Training & Merge Details

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
• 🌐 Space Documentation
• ⚙️ Fusion Engine (coming soon)
• 🧬 Qwythos-9B-v2 (Mythos-class 9B model)

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Built with ❤️ using myth_fusion.py — a custom SLERP fusion engine