Instructions to use NousResearch/DeepHermes-3-Mistral-24B-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NousResearch/DeepHermes-3-Mistral-24B-Preview with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="NousResearch/DeepHermes-3-Mistral-24B-Preview") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("NousResearch/DeepHermes-3-Mistral-24B-Preview") model = AutoModelForCausalLM.from_pretrained("NousResearch/DeepHermes-3-Mistral-24B-Preview", device_map="auto") 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]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use NousResearch/DeepHermes-3-Mistral-24B-Preview with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "NousResearch/DeepHermes-3-Mistral-24B-Preview" # 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/DeepHermes-3-Mistral-24B-Preview", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/NousResearch/DeepHermes-3-Mistral-24B-Preview
- SGLang
How to use NousResearch/DeepHermes-3-Mistral-24B-Preview 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/DeepHermes-3-Mistral-24B-Preview" \ --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/DeepHermes-3-Mistral-24B-Preview", "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/DeepHermes-3-Mistral-24B-Preview" \ --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/DeepHermes-3-Mistral-24B-Preview", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use NousResearch/DeepHermes-3-Mistral-24B-Preview with Docker Model Runner:
docker model run hf.co/NousResearch/DeepHermes-3-Mistral-24B-Preview
Stops working after first two messages
Had the same issue with QwQ 32b to be honest but this seems to work better for the first two messages it thinks and outputs a response but when the third message is there it either doesn't think anymore or it thinks but never outputs a final answer.
I'm using the Q5 variant in LM studio with temp 0.8 and rep 1.1 32k context , tried with top k on and off, top p on and off, min sampling on and off. Same result.
I'm using the system prompt :
You are a deep thinking AI, you may use extremely long chains of thought to deeply consider the problem and deliberate with yourself via systematic reasoning processes to help come to a correct solution prior to answering. You should enclose your thoughts and internal monologue inside tags, and then provide your solution or response to the problem.
The prompt template though I don't know what's its supposed to be but its the automatically loaded one from LM studio:
{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>
What am I doing wrong or is it like Qwq 32b where there is some kind of error?
I'm seeing the same issue, I can only get it to use reasoning for 1 or 2 responses at most.
Just make sure it's the last thing in the prompt rather than the first, that helped a lot for me.