library_name, pipeline_tag, base_model, tags, license, language
| library_name | pipeline_tag | base_model | tags | license | language | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| transformers | text-generation | Qwen/Qwen3-8B |
|
apache-2.0 |
|
zen3-nano
Compact, capable language model for fast inference, part of the OSS-clean Zen model line.
Repackaged from Qwen/Qwen3-8B (apache-2.0, Alibaba Qwen). Not trained from scratch — a permissively-licensed redistribution for the OSS-clean Zen model line.
Specs
| Property | Value |
|---|---|
| Parameters | 8B (dense) |
| Architecture | Qwen3 (Qwen3ForCausalLM) |
| Context Length | 40K tokens |
| Generation | Zen3 |
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("zenlm/zen3-nano", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained("zenlm/zen3-nano")
messages = [{"role": "user", "content": "Explain quantum computing in simple terms."}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=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))
API Access
Also served via the Hanzo AI API:
from openai import OpenAI
client = OpenAI(base_url="https://api.hanzo.ai/v1", api_key="YOUR_KEY")
response = client.chat.completions.create(
model="zen3-nano",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)
Get your API key at console.hanzo.ai — $5 free credit on signup.
License
apache-2.0. Upstream: Qwen/Qwen3-8B by Alibaba Qwen. Upstream LICENSE/NOTICE retained in-repo.
Zen LM is developed by Hanzo AI — Frontier AI infrastructure.
Description