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")
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]:]))

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

Myth-4B / README.md

dracko14

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	---
	license: mit
	library_name: transformers
	tags:
	- merge
	- slerp
	- qwen3.5
	- myth
	- reasoning
	- coding
	- math
	base_model:
	- Qwen/Qwen3.5-4B
	- Jackrong/Qwen3.5-4B-Neo
	pipeline_tag: text-generation
	language:
	- en
	- multilingual
	extra_gated_prompt: "This model is open for all. No special permission needed."
	---

	# 🧬 Myth 4B

	Myth 4B is a fusion of [Qwen 3.5 4B](https://huggingface.co/Qwen/Qwen3.5-4B) and [Qwen3.5-4B-Neo](https://huggingface.co/Jackrong/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)

	```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

	```bash
	# 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](https://huggingface.co/dracko14/Myth-4B/blob/main/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.

	## Links

	- 📄 [System Prompt](https://huggingface.co/dracko14/Myth-4B/blob/main/myth_system_prompt.md)
	- 🌐 [Space Documentation](https://huggingface.co/spaces/dracko14/myth)
	- ⚙️ [Fusion Engine](https://github.com/yourusername/myth-fusion) (coming soon)
	- 🧬 [Qwythos-9B-v2](https://huggingface.co/empero-ai/Qwythos-9B-v2) (Mythos-class 9B model)

	---

	Built with ❤️ using [myth_fusion.py](https://huggingface.co/spaces/dracko14/myth) — a custom SLERP fusion engine