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
Efficiency	92	Outstanding for its size class

⚡ 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 HuggingFacegit lfs installgit clone https://huggingface.co/dracko14/MYTH-1.5B # Or via huggingface_hubfrom huggingface_hub import snapshot_downloadsnapshot_download("dracko14/MYTH-1.5B")

Inference with Transformers

python

from transformers import AutoModelForCausalLM, AutoTokenizerimport 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

text

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

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

Base

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

Merged

this model

Input Modalities

Text

Output Modalities

Text

Supported Functionality

Dedicated Endpoints

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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
Efficiency	92	Outstanding for its size class

⚡ 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 HuggingFacegit lfs installgit clone https://huggingface.co/dracko14/MYTH-1.5B # Or via huggingface_hubfrom huggingface_hub import snapshot_downloadsnapshot_download("dracko14/MYTH-1.5B")

# Direct from HuggingFacegit lfs installgit clone https://huggingface.co/dracko14/MYTH-1.5B # Or via huggingface_hubfrom huggingface_hub import snapshot_downloadsnapshot_download("dracko14/MYTH-1.5B")

Inference with Transformers

python

from transformers import AutoModelForCausalLM, AutoTokenizerimport 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))

from transformers import AutoModelForCausalLM, AutoTokenizerimport 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

# 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

text

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       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}}

@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