Instructions to use brucethemoose/Capybara-Tess-Yi-34B-200K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use brucethemoose/Capybara-Tess-Yi-34B-200K with Transformers:

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

pipe = pipeline("text-generation", model="brucethemoose/Capybara-Tess-Yi-34B-200K")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("brucethemoose/Capybara-Tess-Yi-34B-200K")
model = AutoModelForMultimodalLM.from_pretrained("brucethemoose/Capybara-Tess-Yi-34B-200K")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use brucethemoose/Capybara-Tess-Yi-34B-200K with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "brucethemoose/Capybara-Tess-Yi-34B-200K"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "brucethemoose/Capybara-Tess-Yi-34B-200K",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/brucethemoose/Capybara-Tess-Yi-34B-200K

SGLang

How to use brucethemoose/Capybara-Tess-Yi-34B-200K 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 "brucethemoose/Capybara-Tess-Yi-34B-200K" \
    --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": "brucethemoose/Capybara-Tess-Yi-34B-200K",
		"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 "brucethemoose/Capybara-Tess-Yi-34B-200K" \
        --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": "brucethemoose/Capybara-Tess-Yi-34B-200K",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use brucethemoose/Capybara-Tess-Yi-34B-200K with Docker Model Runner:
```
docker model run hf.co/brucethemoose/Capybara-Tess-Yi-34B-200K
```

Capybara-Tess-Yi-34B-200K / model-00002-of-00008.safetensors

brucethemoose

Upload folder using huggingface_hub

ee42f5c over 2 years ago

Download

History Blame Contribute Delete

9.75 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: fe50dffdb753c80a3a56a1f13976a9f4465bd94f43dcf9dc6b6cdc188bf39094
Size of remote file:: 9.75 GB
SHA256:: c146b6fee14d354a799326a3610778281a9c8e85a03c211d144f65ce1eec968e

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.