Text Generation
Transformers
English
llama
Merge
mergekit
lazymergekit
NousResearch/Nous-Hermes-llama-2-7b
NousResearch/Nous-Capybara-7B-V1
Instructions to use LTC-AI-Labs/Hermes-Capybara-7B-Test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LTC-AI-Labs/Hermes-Capybara-7B-Test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LTC-AI-Labs/Hermes-Capybara-7B-Test")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("LTC-AI-Labs/Hermes-Capybara-7B-Test") model = AutoModelForCausalLM.from_pretrained("LTC-AI-Labs/Hermes-Capybara-7B-Test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use LTC-AI-Labs/Hermes-Capybara-7B-Test with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LTC-AI-Labs/Hermes-Capybara-7B-Test" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LTC-AI-Labs/Hermes-Capybara-7B-Test", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/LTC-AI-Labs/Hermes-Capybara-7B-Test
- SGLang
How to use LTC-AI-Labs/Hermes-Capybara-7B-Test 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 "LTC-AI-Labs/Hermes-Capybara-7B-Test" \ --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": "LTC-AI-Labs/Hermes-Capybara-7B-Test", "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 "LTC-AI-Labs/Hermes-Capybara-7B-Test" \ --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": "LTC-AI-Labs/Hermes-Capybara-7B-Test", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use LTC-AI-Labs/Hermes-Capybara-7B-Test with Docker Model Runner:
docker model run hf.co/LTC-AI-Labs/Hermes-Capybara-7B-Test
Download model-00007-of-00007.safetensors from LTC-AI-Labs/Hermes-Capybara-7B-Test: direct link, hf CLI and curl.
- Browser
- Download file 1.84 GB
-
https://huggingface.co/LTC-AI-Labs/Hermes-Capybara-7B-Test/resolve/main/model-00007-of-00007.safetensors
- Command line
-
hf download hf://LTC-AI-Labs/Hermes-Capybara-7B-Test/model-00007-of-00007.safetensors
-
curl -L -o model-00007-of-00007.safetensors https://huggingface.co/LTC-AI-Labs/Hermes-Capybara-7B-Test/resolve/main/model-00007-of-00007.safetensors
1.84 GB
- Xet hash:
- c1a75fba485497cc7ff97177a03d407881e65adab08f12af5183f516ed94d604
- Size of remote file:
- 1.84 GB
- SHA256:
- 252f44f3fd2f9d98e464027f9c6812d4eb4263aafa6a308a1e81c51e2d900607
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