Instructions to use NousResearch/Nous-Hermes-2-Yi-34B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NousResearch/Nous-Hermes-2-Yi-34B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="NousResearch/Nous-Hermes-2-Yi-34B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("NousResearch/Nous-Hermes-2-Yi-34B") model = AutoModelForMultimodalLM.from_pretrained("NousResearch/Nous-Hermes-2-Yi-34B") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use NousResearch/Nous-Hermes-2-Yi-34B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "NousResearch/Nous-Hermes-2-Yi-34B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "NousResearch/Nous-Hermes-2-Yi-34B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/NousResearch/Nous-Hermes-2-Yi-34B
- SGLang
How to use NousResearch/Nous-Hermes-2-Yi-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-Hermes-2-Yi-34B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "NousResearch/Nous-Hermes-2-Yi-34B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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-Hermes-2-Yi-34B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "NousResearch/Nous-Hermes-2-Yi-34B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use NousResearch/Nous-Hermes-2-Yi-34B with Docker Model Runner:
docker model run hf.co/NousResearch/Nous-Hermes-2-Yi-34B
200K Version?
Could we get a version of this model trained on Yi 200K instead?
It doesn't have to be trained at the full 200K CTX size. Finetunes trained at lesser context sizes still seem to hold the long context performance.
Would love to see this
Yeah, 4096 is too short for most task that requires text summerization
It doesn't have to be trained at the full 200K CTX size. Finetunes trained at lesser context sizes still seem to hold the long context performance.
I may have spoke too soon on this. It seems that Bagel's 4K training actually hurt Yi 200K's context considerably.
I suspect its because it was partially a full finetune, not a lora?