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ZeroXClem/Qwen3-4B-Hermes-Axion-Pro
Qwen3-4B-Hermes-Axion-Pro is a text generation model from ZeroXClem. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
Downloads · 30 days
29
7% of all-time downloads
All-time downloads
415
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4B
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.safetensors8 GB · 100%
From the Hugging Face model README

ZeroXClem-Qwen3-4B-Hermes-Axion-Pro is a powerful, safety-conscious, and deeply intelligent merge crafted via Model Stock merging using MergeKit. This 4B-parameter model blends the best of Hermes-3, Axion-Thinking, and Qwen3-Pro, optimized for deep reasoning, safe generation, and dynamic roleplay.
It’s designed to excel in structured problem-solving, multi-turn dialogue, and creative writing, while maintaining safe behavior aligned through red teaming and post-training.
This model is VERY good with reasoning, and hard tasks! Use the default Template(Jinja) setting in LMStudio for best inference.
ZeroXClem-Qwen3-4B-Hermes-Axion-Promodel_stockbunnycore/Qwen3-4B-Probfloat16Qwen/Qwen3-4B-Thinking-2507name: ZeroXClem-Qwen3-4B-Hermes-Axion-Pro
base_model: bunnycore/Qwen3-4B-Pro
dtype: bfloat16
merge_method: model_stock
models:
- model: ertghiu256/Qwen3-4b-tcomanr-merge-v2.2
- model: ertghiu256/Qwen3-4B-Thinking-2507-Hermes-3
- model: Qwen/Qwen3-4B-Thinking-2507
tokenizer_source: Qwen/Qwen3-4B-Thinking-2507
ertghiu256/Qwen3-4B-Thinking-2507-Hermes-3Finetuned on the Hermes 3 dataset for instruction alignment and coherent multi-step thinking.
AdvRahul/Axion-Thinking-4BSafety-tested and enhanced via red teaming protocols. Based on Qwen3-4B-Thinking-2507, with refined behavior for ethical deployment.
ertghiu256/Qwen3-4b-tcomanr-merge-v2.2Strong logic and instruction-following merge with emphasis on quality output in diverse domains.
bunnycore/Qwen3-4B-ProA professional-grade Qwen variant tuned for real-world applications like coding, RP, creative writing, and structured tasks.
🔹 Deep Thinking & Problem Solving — Inspired by Hermes-3 and Axion, this model handles multi-step logical reasoning and instruction-following with clarity.
🔹 Safe, Aligned Outputs — Red team finetuning and post-training ensure behavior safety and moderation-ready generation.
🔹 Creative Writing & Roleplay — Retains high fluency and character immersion for natural roleplay and storytelling.
🔹 Coding & Engineering Tasks — Competent in code generation, debugging, and technical explanations.
🔹 Efficient & Lightweight — At just 4B parameters, it's easy to deploy locally or in constrained environments.
For optimal inference, use a higher quant such as Q6 Here.
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "ZeroXClem/Qwen3-4B-Hermes-Axion-Pro"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
prompt = "Describe the principles of quantum entanglement in simple terms."
messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True
)
inputs = tokenizer([text], return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
For LM Studio Users: When using this model in LM Studio, select the Qwen3-Chat Template from the template dropdown menu. This official template ensures proper prompt formatting for consistent multi-turn conversations and system instruction handling.
enable_thinking=True and /think prompt control.We welcome community feedback, prompts, benchmarks, and merge ideas! Reach out via HF comments or GitHub for collaboration.