Instructions to use King3Djbl/mythos-v2-8b-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use King3Djbl/mythos-v2-8b-lora with Transformers:

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

pipe = pipeline("text-generation", model="King3Djbl/mythos-v2-8b-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("King3Djbl/mythos-v2-8b-lora", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use King3Djbl/mythos-v2-8b-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "King3Djbl/mythos-v2-8b-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "King3Djbl/mythos-v2-8b-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/King3Djbl/mythos-v2-8b-lora

SGLang

How to use King3Djbl/mythos-v2-8b-lora 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 "King3Djbl/mythos-v2-8b-lora" \
    --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": "King3Djbl/mythos-v2-8b-lora",
		"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 "King3Djbl/mythos-v2-8b-lora" \
        --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": "King3Djbl/mythos-v2-8b-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use King3Djbl/mythos-v2-8b-lora with Docker Model Runner:
```
docker model run hf.co/King3Djbl/mythos-v2-8b-lora
```

mythos-v2-8b-lora

Uncensored, agentic model — tool-use, shell & coding, zero refusals.

Base model: Qwen/Qwen3-8B · Part of the FableForge ecosystem.

🔧 LoRA adapter

Merge with the base model Qwen/Qwen3-8B or load with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM
base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-8B")
model = PeftModel.from_pretrained(base, "King3Djbl/mythos-v2-8b-lora")

⚡ Run it now — Ollama

ollama run hf.co/King3Djbl/mythos-v2-8b-lora:Q4_K_M

📦 GGUF quants: King3Djbl/mythos-v2-8b-lora

🧠 Prompt format (ChatML)

<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

🌌 See it live — the FableForge demos

This family powers a galaxy of free, interactive HF Spaces:

⭐ Like & share — it helps people find the source instead of a mirror.

Downloads last month: -; Downloads are not tracked for this model. How to track

Model tree for King3Djbl/mythos-v2-8b-lora

Base model

Qwen/Qwen3-8B-Base

Finetuned

Qwen/Qwen3-8B

Adapter

(1830)

this model