72 lines
1.9 KiB
Markdown
72 lines
1.9 KiB
Markdown
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---
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library_name: transformers
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pipeline_tag: text-generation
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base_model: Qwen/Qwen3-8B
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tags:
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- text-generation
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- conversational
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- multilingual
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- zen
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- zen3
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- hanzo
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- zenlm
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license: apache-2.0
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language:
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- en
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---
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# zen3-nano
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Compact, capable language model for fast inference, part of the OSS-clean Zen model line.
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Repackaged from [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) (apache-2.0, Alibaba Qwen). **Not trained from scratch** — a permissively-licensed redistribution for the OSS-clean Zen model line.
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## Specs
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| Property | Value |
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|----------|-------|
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| Parameters | 8B (dense) |
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| Architecture | Qwen3 (`Qwen3ForCausalLM`) |
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| Context Length | 40K tokens |
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| Generation | Zen3 |
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("zenlm/zen3-nano", torch_dtype="auto")
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tokenizer = AutoTokenizer.from_pretrained("zenlm/zen3-nano")
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messages = [{"role": "user", "content": "Explain quantum computing in simple terms."}]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer([text], return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=512)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## API Access
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Also served via the Hanzo AI API:
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```python
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from openai import OpenAI
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client = OpenAI(base_url="https://api.hanzo.ai/v1", api_key="YOUR_KEY")
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response = client.chat.completions.create(
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model="zen3-nano",
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messages=[{"role": "user", "content": "Hello"}],
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)
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print(response.choices[0].message.content)
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```
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Get your API key at [console.hanzo.ai](https://console.hanzo.ai) — $5 free credit on signup.
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## License
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`apache-2.0`. Upstream: **Qwen/Qwen3-8B** by Alibaba Qwen. Upstream LICENSE/NOTICE retained in-repo.
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---
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*Zen LM is developed by [Hanzo AI](https://hanzo.ai) — Frontier AI infrastructure.*
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