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

Libraries

How to use ycros/DonutHole-8x7B with Transformers:

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

pipe = pipeline("text-generation", model="ycros/DonutHole-8x7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ycros/DonutHole-8x7B")
model = AutoModelForCausalLM.from_pretrained("ycros/DonutHole-8x7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ycros/DonutHole-8x7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ycros/DonutHole-8x7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ycros/DonutHole-8x7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ycros/DonutHole-8x7B

SGLang

How to use ycros/DonutHole-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 "ycros/DonutHole-8x7B" \
    --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": "ycros/DonutHole-8x7B",
		"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 "ycros/DonutHole-8x7B" \
        --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": "ycros/DonutHole-8x7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ycros/DonutHole-8x7B with Docker Model Runner:
```
docker model run hf.co/ycros/DonutHole-8x7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

DonutHole-8x7B

GGUF versions here

Bagel, Mixtral Instruct, Holodeck, LimaRP.

What mysteries lie in the hole of a donut?

Good with Alpaca prompt formats, also works with Mistral format. See usage details below.

This is similar to BagelMIsteryTour, but I've swapped out Sensualize for the new Holodeck. I'm not sure if it's better or not yet, or how it does at higher (8k+) contexts just yet.

Similar sampler advice applies as for BMT: minP (0.07 - 0.3 to taste) -> temp (either dynatemp 0-4ish, or like a temp of 3-4 with a smoothing factor of around 2.5ish). And yes, that's temp last. It does okay without rep pen up to a point, it doesn't seem to get into a complete jam, but it can start to repeat sentences, so you'll probably need some, perhaps 1.02-1.05 at a 1024 range seems okayish. (rep pen sucks, but there are better things coming).

I've mainly tested with LimaRP style Alpaca prompts (instruction/input/response), and briefly with Mistral's own format.

Full credit to all the model and dataset authors, I am but a derp with compute and a yaml file.

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the DARE TIES merge method using mistralai/Mixtral-8x7B-v0.1 as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

base_model: mistralai/Mixtral-8x7B-v0.1
models:
  - model: mistralai/Mixtral-8x7B-v0.1+Doctor-Shotgun/limarp-zloss-mixtral-8x7b-qlora
    parameters:
      density: 0.5
      weight: 0.2
  - model: KoboldAI/Mixtral-8x7B-Holodeck-v1
    parameters:
      density: 0.5
      weight: 0.2
  - model: mistralai/Mixtral-8x7B-Instruct-v0.1
    parameters:
      density: 0.6
      weight: 1.0
  - model: jondurbin/bagel-dpo-8x7b-v0.2
    parameters:
      density: 0.6
      weight: 0.5
merge_method: dare_ties
dtype: bfloat16