Instructions to use FoxEngineAi/Mega-Destroyer-8x7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FoxEngineAi/Mega-Destroyer-8x7B with Transformers:

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

pipe = pipeline("text-generation", model="FoxEngineAi/Mega-Destroyer-8x7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FoxEngineAi/Mega-Destroyer-8x7B")
model = AutoModelForCausalLM.from_pretrained("FoxEngineAi/Mega-Destroyer-8x7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use FoxEngineAi/Mega-Destroyer-8x7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FoxEngineAi/Mega-Destroyer-8x7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FoxEngineAi/Mega-Destroyer-8x7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FoxEngineAi/Mega-Destroyer-8x7B

SGLang

How to use FoxEngineAi/Mega-Destroyer-8x7B 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 "FoxEngineAi/Mega-Destroyer-8x7B" \
    --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": "FoxEngineAi/Mega-Destroyer-8x7B",
		"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 "FoxEngineAi/Mega-Destroyer-8x7B" \
        --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": "FoxEngineAi/Mega-Destroyer-8x7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FoxEngineAi/Mega-Destroyer-8x7B with Docker Model Runner:
```
docker model run hf.co/FoxEngineAi/Mega-Destroyer-8x7B
```

Can't stop this from rambling and repeating

by FlareRebellion - opened Mar 16, 2024

Discussion

FlareRebellion

Mar 16, 2024

Both of these problems are typical for Mixtral models, but this merge seems super prone to them. It's very easy to get into a situation where it can't be stopped from rambling or repeating at nauseam, no matter the sampler settings. BagelMisteryTour, BondBurger, Fish & co. seem much more usable in that regard.

Maybe there's something that I'm missing with this model, I like the prose and 'character' of it, but it seems to lose its shit at no more than 4k ctx.

MrDragonFox

FoxEngineAi org Mar 17, 2024

merges tend to-do that, its pretty unpredictable - again that's on dampf - i just provided the compute

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