🧬 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 — Custom SLERP Engine
Base Models	DeepSeek-R1-Distill-Qwen-1.5B + Qwen2.5-Coder-1.5B-Instruct
Created by	dracko14
License	MIT

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📊 Performance Overview

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.

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🧠 Capability Profile

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

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⚡ Size vs Performance

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

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🔬 Fusion Method: SLERP

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) 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

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🚀 Quick Start

Download

# Direct from HuggingFace
git lfs install
git clone https://huggingface.co/dracko14/MYTH-1.5B

# Or via huggingface_hub
from huggingface_hub import snapshot_download
snapshot_download("dracko14/MYTH-1.5B")

Inference with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "dracko14/MYTH-1.5B"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

prompt = "Explain the concept of recursive functions with an example."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

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

# 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

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🧪 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

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📦 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) → Deep reasoning, chain-of-thought, mathematical excellence
• Qwen2.5-Coder-1.5B-Instruct (Apache 2.0) → Code generation, instruction following, structured output

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⚠️ 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

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📚 Citation

@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}
}

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🙏 Acknowledgements

• DeepSeek for the R1 distillation models
• Qwen Team (Alibaba) 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

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"Three forces, one entity — Myth"