Instructions to use tdrussell/Mixtral-8x22B-Capyboros-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tdrussell/Mixtral-8x22B-Capyboros-v1 with Transformers:

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

pipe = pipeline("text-generation", model="tdrussell/Mixtral-8x22B-Capyboros-v1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tdrussell/Mixtral-8x22B-Capyboros-v1")
model = AutoModelForCausalLM.from_pretrained("tdrussell/Mixtral-8x22B-Capyboros-v1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tdrussell/Mixtral-8x22B-Capyboros-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tdrussell/Mixtral-8x22B-Capyboros-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tdrussell/Mixtral-8x22B-Capyboros-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/tdrussell/Mixtral-8x22B-Capyboros-v1

SGLang

How to use tdrussell/Mixtral-8x22B-Capyboros-v1 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 "tdrussell/Mixtral-8x22B-Capyboros-v1" \
    --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": "tdrussell/Mixtral-8x22B-Capyboros-v1",
		"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 "tdrussell/Mixtral-8x22B-Capyboros-v1" \
        --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": "tdrussell/Mixtral-8x22B-Capyboros-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use tdrussell/Mixtral-8x22B-Capyboros-v1 with Docker Model Runner:
```
docker model run hf.co/tdrussell/Mixtral-8x22B-Capyboros-v1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

QLoRA fine-tune of Mixtral-8x22B-v0.1 on a combination of the Capybara and Airoboros datasets.

Uses Mistral instruct formatting, like this: [INST] Describe quantum computing to a layperson. [/INST]

Model details:

Trained with QLoRA, on 4 4090s, using my own qlora-pipe training script
LoRA rank 64
4096 sequence length
2 epochs

You can find the LoRA adapter files here. I have also uploaded a single quant (GGUF q4_k_s) here if you want to try it without quantizing yourself or waiting for someone else to make all the quants. It fits with at least 16k context length on 96GB VRAM.

Downloads last month: 24

Safetensors

Model size

141B params

Tensor type

BF16

Model tree for tdrussell/Mixtral-8x22B-Capyboros-v1

Finetunes

1 model

Quantizations

1 model

tdrussell
/

Mixtral-8x22B-Capyboros-v1

Model tree for tdrussell/Mixtral-8x22B-Capyboros-v1

Datasets used to train tdrussell/Mixtral-8x22B-Capyboros-v1