Hermes-4-14B-GGUF

<span style="color: #7FFF7F;">NousResearch/Hermes-4-14B GGUF Models</span>

<span style="color: #7F7FFF;">Model Generation Details</span>

This model was generated using llama.cpp at commit 4fd1242b.

---

<a href="https://readyforquantum.com/huggingface_gguf_selection_guide.html" style="color: #7FFF7F;"> Click here to get info on choosing the right GGUF model format </a>

---

<!--Begin Original Model Card-->

Hermes 4 — Qwen 3 14B

Model Description

Hermes 4 14B is a frontier, hybrid-mode reasoning model based on Qwen 3 14B by Nous Research that is aligned to you.

Read the Hermes 4 technical report here: <a href="https://arxiv.org/abs/2508.18255">Hermes 4 Technical Report</a>

Chat with Hermes in Nous Chat: https://chat.nousresearch.com

Training highlights include a newly synthesized post-training corpus emphasizing verified reasoning traces, massive improvements in math, code, STEM, logic, creativity, and format-faithful outputs, while preserving general assistant quality and broadly neutral alignment.

What’s new vs Hermes 3

• Post-training corpus: Massively increased dataset size from 1M samples and 1.2B tokens to ~5M samples / ~60B tokens blended across reasoning and non-reasoning data.
• Hybrid reasoning mode with explicit <think>…</think> segments when the model decides to deliberate, and options to make your responses faster when you want.
• Reasoning that is top quality, expressive, improves math, code, STEM, logic, and even creative writing and subjective responses.
• Schema adherence & structured outputs: trained to produce valid JSON for given schemas and to repair malformed objects.
• Much easier to steer and align: extreme improvements on steerability, especially on reduced refusal rates.

Our Mission: Frontier Capabilities Aligned to You

In pursuit of the mission of producing models that are open, steerable and capable of producing the full range of human expression, while being able to be aligned to your values, we created a new benchmark, RefusalBench, that tests the models willingness to be helpful in a variety of scenarios commonly disallowed by closed and open models.

Hermes 4 achieves SOTA on RefusalBench across all popular closed and open models in being helpful and conforming to your values, without censorship.

Benchmarks (Hermes 4 14B)

Full tables, settings, and comparisons are in the technical report.

Prompt Format

Hermes 4 uses ChatML format with role headers and special tags.

Basic chat:

<|im_start|>system

You are Hermes 4. Be concise and helpful.<|im_end|>
<|im_start|>user

Explain the photoelectric effect simply.<|im_end|>
<|im_start|>assistant

Reasoning mode

Reasoning mode can be activated with the chat template via the flag thinking=True or by using the following 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 <think> </think> tags, and then provide your solution or response to the problem.

Note that you can add any additional system instructions before or after this system message, and it will adjust the models policies, style, and effort of thinking, as well as its post-thinking style, format, identity, and more. You may also interleave the tool definition system message with the reasoning one.

When the model chooses to deliberate, it emits:

<|im_start|>assistant
<think>
…model’s internal reasoning may appear here…
</think>
Final response starts here…<|im_end|>

Additionally, we provide a flag to keep the content inbetween the <think> ... </think> that you can play with by setting keep_cots=True

Function Calling & Tool Use

Hermes 4 supports function/tool calls within a single assistant turn, produced after it's reasoning:

System message (example):

<|im_start|>system
You are a function-calling AI. Tools are provided inside <tools>…</tools>.
When appropriate, call a tool by emitting a <tool_call>{...}</tool_call> object.
After a tool responds (as <tool_response>), continue reasoning inside <think> and produce the final answer.
<tools>
{"type":"function","function":{"name":"get_weather","description":"Get weather by city","parameters":{"type":"object","properties":{"city":{"type":"string"}},"required":["city"]}}}
</tools><|im_end|>

Note that you may also simply place tool definitions into the "tools:" field of your messages, and the chat template will parse and create the system prompt for you. This also works with reasoning mode for improved accuracy of tool use.

The model will then generate tool calls within <tool_call> {tool_call} </tool_call> tags, for easy parsing. The tool_call tags are also added tokens, so it makes it easy to parse while streaming! There are also automatic tool parsers built-in to VLLM and SGLang for Hermes, just set the tool parser in VLLM to hermes and in SGLang to qwen25.

Inference Notes

• Sampling defaults that work well: temperature=0.6, top_p=0.95, top_k=20.
• Template: Use the ChatML chat format for Hermes 4 14B as shown above, or set add_generation_prompt=True when using tokenizer.apply_chat_template(...).

Transformers example

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "NousResearch/Hermes-4-14B"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

messages = [
    {"role":"system","content":"You are Hermes 4. Be concise."},
    {"role":"user","content":"Summarize CRISPR in 3 sentences."}
]

inputs = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)

outputs = model.generate(
    **inputs, max_new_tokens=400, temperature=0.6, top_p=0.95, top_k=20, do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

For production serving on multi-GPU nodes, consider tensor parallel inference engines (e.g., SGLang/vLLM backends) with prefix caching.

Inference Providers:

Nous Portal:

<a href="https://portal.nousresearch.com"><img width=256 alt="chutes logo" src="https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/6YytY7N0mjCnBQvWo3qtv.png"></a>

Chutes:

<a href="https://chutes.ai/app"><img width=256 alt="chutes logo" src="https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/l14AWPv6cSvaprpwK_IWY.png"></a>

Nebius:

<a href="https://nebius.com/services/studio-inference-service"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/vhL0oAomFa_awBdt2KF_x.png"> <source media="(prefers-color-scheme: light)" srcset="https://cdn-uploads.huggingface.co/production/uploads/64b21cbb2fc8324fcb1dac03/LjAfeFfAz8ac5rV-iiwj5.png"> <img width=256 alt="nebius.com logo" src="https://cdn-uploads.huggingface.co/production/uploads/64b21cbb2fc8324fcb1dac03/LjAfeFfAz8ac5rV-iiwj5.png"> </picture> </a>

Luminal:

<a href="https://luminalai.com/"> <img width=256 alt="luminal logo" src="https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/FIHsRdjMMP0HUjebiuJyH.png"> </a>

Quantized / Smaller Variants

Hermes 4 is available as BF16 original weights as well as BF16 as well as FP8 variants and GGUF variants by LM Studio.

FP8: https://huggingface.co/NousResearch/Hermes-4-14B-FP8

GGUF (Courtesy of LM Studio team!):

Hermes 4 is also available in larger sizes (e.g., 70B, 405B) with similar prompt formats.

See the Hermes 4 collection to explore them all: https://huggingface.co/collections/NousResearch/hermes-4-collection-68a731bfd452e20816725728

How to cite

@misc{teknium2025hermes4technicalreport,
      title={Hermes 4 Technical Report}, 
      author={Ryan Teknium and Roger Jin and Jai Suphavadeeprasit and Dakota Mahan and Jeffrey Quesnelle and Joe Li and Chen Guang and Shannon Sands and Karan Malhotra},
      year={2025},
      eprint={2508.18255},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2508.18255}, 
}

<!--End Original Model Card-->

---

<span id="testllm" style="color: #7F7FFF;">🚀 If you find these models useful</span>

Help me test my AI-Powered Quantum Network Monitor Assistant with quantum-ready security checks:

👉 Quantum Network Monitor

The full Open Source Code for the Quantum Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) : Source Code Quantum Network Monitor. You will also find the code I use to quantize the models if you want to do it yourself GGUFModelBuilder

💬 How to test:
Choose an AI assistant type:

• TurboLLM (GPT-4.1-mini)
• HugLLM (Hugginface Open-source models)
• TestLLM (Experimental CPU-only)

What I’m Testing

I’m pushing the limits of small open-source models for AI network monitoring, specifically:

• Function calling against live network services
• How small can a model go while still handling:
  • Automated Nmap security scans
  • Quantum-readiness checks
  • Network Monitoring tasks

🟡 TestLLM – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space):

• ✅ Zero-configuration setup
• ⏳ 30s load time (slow inference but no API costs) . No token limited as the cost is low.
• 🔧 Help wanted! If you’re into edge-device AI, let’s collaborate!

Other Assistants

🟢 TurboLLM – Uses gpt-4.1-mini :

• **It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited.
• Create custom cmd processors to run .net code on Quantum Network Monitor Agents
• Real-time network diagnostics and monitoring
• Security Audits
• Penetration testing (Nmap/Metasploit)

🔵 HugLLM – Latest Open-source models:

• 🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita.

💡 Example commands you could test:

1. "Give me info on my websites SSL certificate"
2. "Check if my server is using quantum safe encyption for communication"
3. "Run a comprehensive security audit on my server"
4. '"Create a cmd processor to .. (what ever you want)" Note you need to install a Quantum Network Monitor Agent to run the .net code on. This is a very flexible and powerful feature. Use with caution!

Final Word

I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is open source. Feel free to use whatever you find helpful.

If you appreciate the work, please consider buying me a coffee ☕. Your support helps cover service costs and allows me to raise token limits for everyone.

I'm also open to job opportunities or sponsorship.

Thank you! 😊