dracko14

Myth

📊 Performance Overview

MYTH-1.5B inherits complementary strengths from its source models:

Table with columns: Benchmark, DeepSeek-R1 1.5B, Qwen-Coder 1.5B, MYTH-1.5B, Description
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

MYTH-1.5B delivers a balanced capability profile across six key dimensions:

Table with columns: Capability, Score, Strength
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

⚡ 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

🔬 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

🚀 Quick Start

Download

bash
# 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

python
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

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

Table with columns: Domain, Strength, Example
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

markdown
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

⚠️ 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 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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Model Details

Model Provider

dracko14

Model Tree

Base

Qwen/Qwen2.5-Coder-1.5B-Instruct

Base

deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B

Merged

this model

Input Modalities

Text

Output Modalities

Text

Supported Functionality

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📊 Performance Overview

MYTH-1.5B inherits complementary strengths from its source models:

Table with columns: Benchmark, DeepSeek-R1 1.5B, Qwen-Coder 1.5B, MYTH-1.5B, Description
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

MYTH-1.5B delivers a balanced capability profile across six key dimensions:

Table with columns: Capability, Score, Strength
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

⚡ 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

🔬 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

🚀 Quick Start

Download

bash
# 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

python
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

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

Table with columns: Domain, Strength, Example
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

markdown
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

⚠️ 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 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

Myth

README

📊 Performance Overview

🧠 Capability Profile

⚡ Size vs Performance

🔬 Fusion Method: SLERP

🚀 Quick Start

Download

Inference with Transformers

Quantized (GGUF) for llama.cpp

🧪 Use Cases

📦 Model Lineage

⚠️ Limitations

📚 Citation

🙏 Acknowledgements

Explore FriendliAI today

README

📊 Performance Overview

🧠 Capability Profile

⚡ Size vs Performance

🔬 Fusion Method: SLERP

🚀 Quick Start

Download

Inference with Transformers

Quantized (GGUF) for llama.cpp

🧪 Use Cases

📦 Model Lineage

⚠️ Limitations

📚 Citation

🙏 Acknowledgements