Text Generation
Transformers
Safetensors
English
llama
Llama-3.1
instruct
finetune
reasoning
hybrid-mode
chatml
function calling
tool use
json mode
structured outputs
atropos
dataforge
long context
roleplaying
chat
conversational
text-generation-inference
Instructions to use NousResearch/Hermes-4-70B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NousResearch/Hermes-4-70B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="NousResearch/Hermes-4-70B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("NousResearch/Hermes-4-70B") model = AutoModelForCausalLM.from_pretrained("NousResearch/Hermes-4-70B") 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
- vLLM
How to use NousResearch/Hermes-4-70B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "NousResearch/Hermes-4-70B" # 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/Hermes-4-70B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/NousResearch/Hermes-4-70B
- SGLang
How to use NousResearch/Hermes-4-70B 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/Hermes-4-70B" \ --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/Hermes-4-70B", "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/Hermes-4-70B" \ --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/Hermes-4-70B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use NousResearch/Hermes-4-70B with Docker Model Runner:
docker model run hf.co/NousResearch/Hermes-4-70B
Update tokenizer_config.json
Browse files- tokenizer_config.json +0 -1
tokenizer_config.json
CHANGED
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"clean_up_tokenization_spaces": true,
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"eos_token": "<|eot_id|>",
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"extra_special_tokens": {},
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-
"chat_template": "{%- if tools %}\n {{- '<|start_header_id|>system<|end_header_id|>\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Hermes, created by Nous Research.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou are a function calling AI model. You may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": \\\"<function-name>\\\", \\\"arguments\\\": <args-json-object>}\\n</tool_call><|eot_id|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|start_header_id|>system<|end_header_id|>\\n\\n' + messages[0]['content'] + '<|eot_id|>\\n' }}\n {%- else %}\n {{- '<|start_header_id|>system<|end_header_id|>\\n\\nYou are Hermes, created by Nous Research.<|eot_id|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|start_header_id|>' + message.role + '<|end_header_id|>\\n\\n' + message.content + '<|eot_id|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|start_header_id|>' + message.role + '<|end_header_id|>\\n' }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|eot_id|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|eot_id|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
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"model_input_names": [
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"input_ids",
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"attention_mask"
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"clean_up_tokenization_spaces": true,
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"eos_token": "<|eot_id|>",
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"extra_special_tokens": {},
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"model_input_names": [
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"input_ids",
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"attention_mask"
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