Text Generation
Transformers
PyTorch
English
gpt_bigcode
starcoder
wizardcoder
code
self-instruct
distillation
text-generation-inference
Instructions to use NousResearch/Redmond-Hermes-Coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NousResearch/Redmond-Hermes-Coder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="NousResearch/Redmond-Hermes-Coder")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("NousResearch/Redmond-Hermes-Coder") model = AutoModelForCausalLM.from_pretrained("NousResearch/Redmond-Hermes-Coder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use NousResearch/Redmond-Hermes-Coder with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "NousResearch/Redmond-Hermes-Coder" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "NousResearch/Redmond-Hermes-Coder", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/NousResearch/Redmond-Hermes-Coder
- SGLang
How to use NousResearch/Redmond-Hermes-Coder 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/Redmond-Hermes-Coder" \ --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/Redmond-Hermes-Coder", "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/Redmond-Hermes-Coder" \ --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/Redmond-Hermes-Coder", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use NousResearch/Redmond-Hermes-Coder with Docker Model Runner:
docker model run hf.co/NousResearch/Redmond-Hermes-Coder
Download pytorch_model-00002-of-00004.bin from NousResearch/Redmond-Hermes-Coder: direct link, hf CLI and curl.
- Browser
- Download file 9.86 GB
-
https://huggingface.co/NousResearch/Redmond-Hermes-Coder/resolve/main/pytorch_model-00002-of-00004.bin
- Command line
-
hf download hf://NousResearch/Redmond-Hermes-Coder/pytorch_model-00002-of-00004.bin
-
curl -L -o pytorch_model-00002-of-00004.bin https://huggingface.co/NousResearch/Redmond-Hermes-Coder/resolve/main/pytorch_model-00002-of-00004.bin
9.86 GB
- Xet hash:
- 6c0055b89a805f5610a5fb24cf4f94a627c76472168adbbc2e9010f76af7f76a
- Size of remote file:
- 9.86 GB
- SHA256:
- 926f98a133e87848b4ff8b5f9e5d6733fd144776df6e008b21276e5f24455994
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