2.3 KiB
license, base_model, tags, datasets, language, library_name, pipeline_tag
| license | base_model | tags | datasets | language | library_name | pipeline_tag | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| apache-2.0 | Qwen/Qwen3-8B |
|
|
|
transformers | text-generation |
Superseded — archived checkpoint. This is the v1.0.1 family overview; its weights are a Qwen3-8B fine-tune. See the current lineup at huggingface.co/zenlm. Weights are kept available for reproducibility.
Zen AI Model Family (v1.0.1, archived)
Base model & attribution
Fine-tuned from Qwen/Qwen3-8B (Apache-2.0) with Hanzo identity + agentic-data training + abliteration.
Zen fine-tunes the best open-weight model of each era. Across this family that is the Qwen3 line from Alibaba Cloud — base, VL, Omni, Embedding, Reranker, TTS, ASR, Guard, Coder — with media models on permissive bases (Wan, FLUX, TRELLIS, YuE). Hanzo adds only identity training, agentic-data fine-tuning, and abliteration. There is no from-scratch Zen model.
- Upstream model: Qwen/Qwen3-8B
- Upstream project: Qwen (Alibaba Cloud)
- Upstream license: Apache-2.0
Quick start
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("zenlm/zen-family")
tokenizer = AutoTokenizer.from_pretrained("zenlm/zen-family")
messages = [{"role": "user", "content": "Hi, what can you help me with?"}]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))
Models
Browse the current, canonical lineup at huggingface.co/zenlm.
Citation
@misc{zen_family_2025,
title = {Zen AI Model Family},
author = {Hanzo AI and Zoo Labs Foundation},
year = {2025},
url = {https://huggingface.co/zenlm}
}
License
Apache-2.0, inherited from the upstream Qwen3-8B base. See LICENSE and NOTICE.
Built by Hanzo AI and Zoo Labs Foundation.