Instructions to use TeeZee/2xNous-Capybara-34B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TeeZee/2xNous-Capybara-34B with Transformers:

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

pipe = pipeline("text-generation", model="TeeZee/2xNous-Capybara-34B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TeeZee/2xNous-Capybara-34B")
model = AutoModelForCausalLM.from_pretrained("TeeZee/2xNous-Capybara-34B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use TeeZee/2xNous-Capybara-34B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TeeZee/2xNous-Capybara-34B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TeeZee/2xNous-Capybara-34B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TeeZee/2xNous-Capybara-34B

SGLang

How to use TeeZee/2xNous-Capybara-34B 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 "TeeZee/2xNous-Capybara-34B" \
    --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": "TeeZee/2xNous-Capybara-34B",
		"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 "TeeZee/2xNous-Capybara-34B" \
        --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": "TeeZee/2xNous-Capybara-34B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TeeZee/2xNous-Capybara-34B with Docker Model Runner:
```
docker model run hf.co/TeeZee/2xNous-Capybara-34B
```

Nous Capybara 57B

Model Details

A result of interleaving layers of NousResearch/Nous-Capybara-34B with itself.
The resulting model has 100 layers and approximately 57 billion parameters.
See mergekit-config.yml for details on the merge method used.

Warning: This model can produce NSFW content!

Results

Follows instructions better than oryginal, no looping, uncensored like oryginal. Feels also smarter than oryginal. All comments are greatly appreciated, download, test and if you appreciate my work, consider buying me my fuel:

Downloads last month: 7

Safetensors

Model size

57B params

Tensor type

F16