Instructions to use R136a1/MythoMax-L2-13B-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use R136a1/MythoMax-L2-13B-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="R136a1/MythoMax-L2-13B-exl2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("R136a1/MythoMax-L2-13B-exl2")
model = AutoModelForCausalLM.from_pretrained("R136a1/MythoMax-L2-13B-exl2")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use R136a1/MythoMax-L2-13B-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "R136a1/MythoMax-L2-13B-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "R136a1/MythoMax-L2-13B-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/R136a1/MythoMax-L2-13B-exl2

SGLang

How to use R136a1/MythoMax-L2-13B-exl2 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 "R136a1/MythoMax-L2-13B-exl2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "R136a1/MythoMax-L2-13B-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "R136a1/MythoMax-L2-13B-exl2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "R136a1/MythoMax-L2-13B-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use R136a1/MythoMax-L2-13B-exl2 with Docker Model Runner:
```
docker model run hf.co/R136a1/MythoMax-L2-13B-exl2
```

EXL2 Quantization of Gryphe's MythoMax L2 13B.

Other quantized models are available from TheBloke: GGML - GPTQ - GGUF - AWQ

Model details

Base Perplexity : 5.7447

Branch	bits	Perplexity	Description
3bit	3.73	5.8251	Low bits quant while still good
4bit	4.33	5.7784	can go 6K context on T4 GPU
main	5.33	5.7427	4k Context on T4 GPU (recommended if you use Google Colab)
6bit	6.13	5.7347	For those who want better quality and capable of running it

Prompt Format

Alpaca format:

### Instruction:





### Response:

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Collection including R136a1/MythoMax-L2-13B-exl2

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Collection

11 items • Updated Mar 2