Instructions to use LoneStriker/Nous-Capybara-7B-V1.9-4.0bpw-h6-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LoneStriker/Nous-Capybara-7B-V1.9-4.0bpw-h6-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="LoneStriker/Nous-Capybara-7B-V1.9-4.0bpw-h6-exl2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("LoneStriker/Nous-Capybara-7B-V1.9-4.0bpw-h6-exl2")
model = AutoModelForMultimodalLM.from_pretrained("LoneStriker/Nous-Capybara-7B-V1.9-4.0bpw-h6-exl2")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LoneStriker/Nous-Capybara-7B-V1.9-4.0bpw-h6-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LoneStriker/Nous-Capybara-7B-V1.9-4.0bpw-h6-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LoneStriker/Nous-Capybara-7B-V1.9-4.0bpw-h6-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LoneStriker/Nous-Capybara-7B-V1.9-4.0bpw-h6-exl2

SGLang

How to use LoneStriker/Nous-Capybara-7B-V1.9-4.0bpw-h6-exl2 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 "LoneStriker/Nous-Capybara-7B-V1.9-4.0bpw-h6-exl2" \
    --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": "LoneStriker/Nous-Capybara-7B-V1.9-4.0bpw-h6-exl2",
		"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 "LoneStriker/Nous-Capybara-7B-V1.9-4.0bpw-h6-exl2" \
        --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": "LoneStriker/Nous-Capybara-7B-V1.9-4.0bpw-h6-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LoneStriker/Nous-Capybara-7B-V1.9-4.0bpw-h6-exl2 with Docker Model Runner:
```
docker model run hf.co/LoneStriker/Nous-Capybara-7B-V1.9-4.0bpw-h6-exl2
```

Nous-Capybara-7B V1.9 Preview (V2 COMING SOON)

This is the reccomended version of Capybara to use until V2 releases.

Leverages novel de-alignment techniques, enhanced quality curation for training and a significantly better foundation model!

This is a version of Capybara trained on Mistral instead of Llama, as well as using an improved dataset distribution and should be even better at avoiding censorship.

The Capybara series is made with a novel synthesis method in mind, Amplify-instruct, with a goal of having a synergistic combination of different data seeds and techniques used for SOTA models such as Airoboros, Evol-Instruct, Orca, Vicuna, Know_Logic, Lamini, FLASK and others, all into one lean holistically formed dataset and model. The seed instructions used for the start of synthesized conversations are largely based on highly acclaimed datasets like Airoboros, Know logic, EverythingLM, GPTeacher and even entirely new seed instructions derived from posts on the website LessWrong, as well as being supplemented with certain multi-turn datasets like Dove(A successor to Puffin).

Entirely contained under 20K training examples, mostly comprised of newly synthesized tokens never used for model training until now!

Downloads last month: 2

LoneStriker
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Nous-Capybara-7B-V1.9-4.0bpw-h6-exl2

Nous-Capybara-7B V1.9 Preview (V2 COMING SOON)

Datasets used to train LoneStriker/Nous-Capybara-7B-V1.9-4.0bpw-h6-exl2