Instructions to use MaziyarPanahi/Goku-8x22B-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MaziyarPanahi/Goku-8x22B-v0.2 with Transformers:

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

pipe = pipeline("text-generation", model="MaziyarPanahi/Goku-8x22B-v0.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/Goku-8x22B-v0.2")
model = AutoModelForMultimodalLM.from_pretrained("MaziyarPanahi/Goku-8x22B-v0.2")
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 Settings

vLLM

How to use MaziyarPanahi/Goku-8x22B-v0.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MaziyarPanahi/Goku-8x22B-v0.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MaziyarPanahi/Goku-8x22B-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MaziyarPanahi/Goku-8x22B-v0.2

SGLang

How to use MaziyarPanahi/Goku-8x22B-v0.2 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 "MaziyarPanahi/Goku-8x22B-v0.2" \
    --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": "MaziyarPanahi/Goku-8x22B-v0.2",
		"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 "MaziyarPanahi/Goku-8x22B-v0.2" \
        --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": "MaziyarPanahi/Goku-8x22B-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MaziyarPanahi/Goku-8x22B-v0.2 with Docker Model Runner:
```
docker model run hf.co/MaziyarPanahi/Goku-8x22B-v0.2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Goku-8x22B-v0.2 (Goku 141b-A35b)

A fine-tuned version of v2ray/Mixtral-8x22B-v0.1 model on the following datasets:

teknium/OpenHermes-2.5
WizardLM/WizardLM_evol_instruct_V2_196k
microsoft/orca-math-word-problems-200k

This model has a total of 141b parameters with 35b only active. The major difference in this version is that the model was trained on more datasets and with an 8192 sequence length. This results in the model being able to generate longer and more coherent responses.

How to use it

Use a pipeline as a high-level helper:

from transformers import pipeline

pipe = pipeline("text-generation", model="MaziyarPanahi/Goku-8x22B-v0.2")

Load model directly:


from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/Goku-8x22B-v0.2")
model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/Goku-8x22B-v0.2")

Downloads last month: 4

Safetensors

Model size

141B params

Tensor type

F32

Model tree for MaziyarPanahi/Goku-8x22B-v0.2

Base model

v2ray/Mixtral-8x22B-v0.1

Finetuned

(2)

this model

Finetunes

1 model

Quantizations

1 model

Datasets used to train MaziyarPanahi/Goku-8x22B-v0.2

Collections including MaziyarPanahi/Goku-8x22B-v0.2