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

Libraries

How to use NousResearch/Nous-Capybara-34B with Transformers:

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

pipe = pipeline("text-generation", model="NousResearch/Nous-Capybara-34B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NousResearch/Nous-Capybara-34B")
model = AutoModelForCausalLM.from_pretrained("NousResearch/Nous-Capybara-34B")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use NousResearch/Nous-Capybara-34B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NousResearch/Nous-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": "NousResearch/Nous-Capybara-34B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/NousResearch/Nous-Capybara-34B

SGLang

How to use NousResearch/Nous-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 "NousResearch/Nous-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": "NousResearch/Nous-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 "NousResearch/Nous-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": "NousResearch/Nous-Capybara-34B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

Any benchmarks yet?

#14

by lemon07r - opened Dec 11, 2023

Discussion

lemon07r

Dec 11, 2023

It's been a while so I'm surprised there are no benchmarks for this model yet. I wanted to see where it fits on the leaderboard (not that I think benchmarks are super important).

CombinHorizon

Apr 21, 2024

•

edited Apr 28, 2024

the leaderboard is currently does not allow for models that use YiTokenizer (maybe it's possible if you talk to the staff)

but here are some test results (but not the standard benchmark results)
https://old.reddit.com/r/LocalLLaMA/comments/18s61fb/pressuretested_the_most_popular_opensource_llms/

here's a small user-test (not automatic/standard, not comprehensive), but it relatively scores best for its size
https://old.reddit.com/r/LocalLLaMA/comments/1bcdtt0/llm_comparisontest_new_api_edition_claude_3_opus/

there's also
https://huggingface.co/datasets/ChuckMcSneed/WolframRavenwolfs_benchmark_results
https://huggingface.co/spaces/DontPlanToEnd/UGI-Leaderboard
https://huggingface.co/datasets/ChuckMcSneed/NeoEvalPlusN_benchmark

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