Instructions to use dracko14/Myth-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dracko14/Myth-4B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="dracko14/Myth-4B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("dracko14/Myth-4B")
model = AutoModelForMultimodalLM.from_pretrained("dracko14/Myth-4B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use dracko14/Myth-4B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="dracko14/Myth-4B",
	filename="Myth-4B-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use dracko14/Myth-4B with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf dracko14/Myth-4B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf dracko14/Myth-4B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf dracko14/Myth-4B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf dracko14/Myth-4B:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf dracko14/Myth-4B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf dracko14/Myth-4B:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf dracko14/Myth-4B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dracko14/Myth-4B:Q4_K_M

Use Docker

docker model run hf.co/dracko14/Myth-4B:Q4_K_M

LM Studio
Jan

vLLM

How to use dracko14/Myth-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dracko14/Myth-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dracko14/Myth-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dracko14/Myth-4B:Q4_K_M

SGLang

How to use dracko14/Myth-4B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "dracko14/Myth-4B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dracko14/Myth-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "dracko14/Myth-4B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dracko14/Myth-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use dracko14/Myth-4B with Ollama:
```
ollama run hf.co/dracko14/Myth-4B:Q4_K_M
```

Unsloth Studio

How to use dracko14/Myth-4B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for dracko14/Myth-4B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for dracko14/Myth-4B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for dracko14/Myth-4B to start chatting

How to use dracko14/Myth-4B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf dracko14/Myth-4B:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "dracko14/Myth-4B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use dracko14/Myth-4B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf dracko14/Myth-4B:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default dracko14/Myth-4B:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use dracko14/Myth-4B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf dracko14/Myth-4B:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "dracko14/Myth-4B:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use dracko14/Myth-4B with Docker Model Runner:
```
docker model run hf.co/dracko14/Myth-4B:Q4_K_M
```

Lemonade

How to use dracko14/Myth-4B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dracko14/Myth-4B:Q4_K_M

Run and chat with the model

lemonade run user.Myth-4B-Q4_K_M

List all available models

lemonade list

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🧬 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

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)

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

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.

Model tree for dracko14/Myth-4B

Jackrong/Qwen3.5-4B-Neo

Qwen/Qwen3.5-4B

Merge model

this model

Quantizations

2 models

dracko14
/

Myth-4B

🧬 Myth 4B

Model Details

Architecture

Lineage

Fusion Engine

Files

Usage

Transformers (Python)

llama.cpp / Ollama

System Prompt

Benchmarks

Training & Merge Details

Limitations

License

Links

Model tree for dracko14/Myth-4B