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
```

Update README.md

by Dampfinchen - opened Mar 14, 2024

base: refs/heads/main

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from: refs/pr/1

Discussion Files changed

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README.md CHANGED Viewed

@@ -11,7 +11,7 @@ base_model:
 tags:
 - mergekit
 - merge
 ---
 # mergeout
@@ -66,3 +66,16 @@ dtype: bfloat16
 name: Mega-Destroyer-8x7B
 ```

 tags:
 - mergekit
 - merge
+license: cc-by-4.0
 ---
 # mergeout
 name: Mega-Destroyer-8x7B
 ```
+Hello everyone, this is Dampf. You might know me as the creator of Mythical-Destroyer-13B.
+This time, I collaborated with Mr.DragonFox aka FoxEngineAi, harnessing his powerful rig to deliver a Merge of multiple high quality Mixtral 8x7B models. My goal was to beat Bagel-Mistery-Tour V2 by Ycros and create the best Mixtral model to date. Did I succeed? Please try it out and decide for yourself!
+Aside from the obvious Mixtral Instruct, to keep its intelligence, I've merged Rombo's excellent Open_Gpt4_v0.2 model that consists of Jon Durbin's Bagel-DPO-8x7B and another highly regarded model, namely smelborp/MixtralOrochi8x7B. This model also combines different datasets together, meaning it should be agood fit for every task you throw at it. This model acts like the reasoning part in the merge.
+In contrast, we have Air-Striker and LimaRP at the creative side which will allow for great roleplays in different styles, they are also a good fit to enhance the model's writing capabilities greatly.
+And finally, I've merged Sao10K/Typhon-Mixtral-v1 to boost the story writing capabilities even further. It includes KoboldAI's latest Holodeck model, as well as a couple of his latest models and combines it into one package. My hope is that this will capture the magic Sao10K/Fimbulvetr-11B-v2 emits, just at the intelligence level of a Mixtral model. This one also includes Nous Hermes 2 DPO, a high quality instruct model that will boost its intelligence and sorta act like a balancer to all the creative stuff in the merge.
+What we have here is a model that should be fantastic at instruct and roleplay/creative tasks a like. So basically a general purpose model. Perhaps the pinnacle of Rocksmashing? Idk xD I just know it includes nearly all datasets on the sun. As a reason, it will likely work with every prompt format as well. So feel free to use Alpaca, Vicuna, ChatML, Llama 2 Chat or whatever your heart desires.
+A huge thank you to the creators of these fantastic datasets and fine tunes in the respective merges, namely Jon Durbin, Teknium, Sao10K, MistralAI, LoneStriker, NeverSleep, Suikamelon, Doctor-Shotgun, KoboldAI and more. All credit goes to them. A thank you to the creators of the different merges I've merged (Mergeception!) as well! And of course a thank you to MrDragonFox for lending his compute! Please enjoy :D