Update model card: add zen/zenlm tags, fix branding
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README.md
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---
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tags:
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- text-generation
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- code
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- reasoning
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- zen
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- hanzo
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---
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# Zen4 Coder Pro
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##
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|----------|-------|
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| Parameters | 685B MoE |
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| Context Length | 163K tokens |
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| Architecture | Zen MoDE (Mixture of Distilled Experts) |
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| Generation | Zen4 |
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```python
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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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---
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language: en
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license: apache-2.0
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tags:
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- text-generation
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- zen
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- zenlm
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- hanzo
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- zen4
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- code
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- coding
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pipeline_tag: text-generation
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library_name: transformers
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---
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# Zen4 Coder Pro
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Professional-grade Zen4 code model for complex software engineering tasks.
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## Overview
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Built on **Zen MoDE (Mixture of Distilled Experts)** architecture with 32B parameters and 128K context window.
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Developed by [Hanzo AI](https://hanzo.ai) and the [Zoo Labs Foundation](https://zoo.ngo).
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## Quick Start
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "zenlm/zen4-coder-pro"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
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messages = [{"role": "user", "content": "Hello!"}]
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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][inputs.input_ids.shape[1]:], skip_special_tokens=True))
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```
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## API Access
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```bash
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curl https://api.hanzo.ai/v1/chat/completions \
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-H "Authorization: Bearer $HANZO_API_KEY" \
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-H "Content-Type: application/json" \
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-d '{"model": "zen4-coder-pro", "messages": [{"role": "user", "content": "Hello"}]}'
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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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## Model Details
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| Attribute | Value |
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|-----------|-------|
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| Parameters | 32B |
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| Architecture | Zen MoDE |
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| Context | 128K tokens |
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| License | Apache 2.0 |
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## License
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Apache 2.0
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