Update model card: add zen/zenlm tags, fix branding

README.md

@@ -1,192 +1,62 @@
 ---
-language:
-- en
-- zh
-- ja
-- ko
-- fr
-- de
-- es
-- it
-- pt
-- ru
+language: en
 license: apache-2.0
 tags:
-- text-generation
-- instruction-following
-- reasoning
-- zenlm
-- zen
+  - text-generation
+  - zen
+  - zenlm
+  - hanzo
+  - reasoning
 pipeline_tag: text-generation
+library_name: transformers
 ---
 
-# Zen Pro 8B
+# Zen Pro
 
-**Professional-grade 8B language model with three specialized variants: instruct, thinking, and agent.**
+Professional-grade general-purpose language model for complex reasoning and analysis.
 
-Zen Pro is Zen LM's 8B professional model, designed for production workloads requiring strong instruction following, multi-step reasoning, and tool use. It runs efficiently on a single consumer GPU (16GB VRAM) while delivering quality competitive with much larger models on structured tasks.
+## Overview
 
-## Model Variants
+Built on **Zen MoDE (Mixture of Distilled Experts)** architecture with 32B parameters and 128K context window.
 
-| Variant | HuggingFace | Best For |
-|---------|-------------|----------|
-| **zen-pro-instruct** | [zenlm/zen-pro-instruct](https://huggingface.co/zenlm/zen-pro-instruct) | Chat, Q&A, summarization, drafting |
-| **zen-pro-thinking** | [zenlm/zen-pro-thinking](https://huggingface.co/zenlm/zen-pro-thinking) | Complex reasoning, math, analysis |
-| **zen-pro-agent** | [zenlm/zen-pro-agent](https://huggingface.co/zenlm/zen-pro-agent) | Tool use, API calls, automation |
-
-## Model Specs
-
-| Property | Value |
-|----------|-------|
-| Parameters | 8B |
-| Architecture | Transformer (decoder-only) |
-| Context Window | 32,768 tokens |
-| License | Apache 2.0 |
-| Quantization | SafeTensors (BF16), GGUF (Q4_K_M, Q5_K_M, Q8_0), MLX |
+Developed by [Hanzo AI](https://hanzo.ai) and the [Zoo Labs Foundation](https://zoo.ngo).
 
 ## Quick Start
 
-### Instruct (chat and general tasks)
-
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
-
-model = AutoModelForCausalLM.from_pretrained(
-    "zenlm/zen-pro-instruct",
-    torch_dtype=torch.bfloat16,
-    device_map="auto"
-)
-tokenizer = AutoTokenizer.from_pretrained("zenlm/zen-pro-instruct")
 
-messages = [
-    {"role": "system", "content": "You are Zen Pro, a professional AI assistant."},
-    {"role": "user", "content": "Summarize the key differences between REST and GraphQL APIs."}
-]
+model_id = "zenlm/zen-pro"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
 
+messages = [{"role": "user", "content": "Hello!"}]
 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, temperature=0.6)
-print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
-```
-
-### Thinking (complex reasoning)
-
-```python
-# Enable extended reasoning for hard problems
-messages = [
-    {"role": "user", "content": "A company has 3 products with 40%, 35%, and 25% market share. "
-     "Product A grows 10%/year, B shrinks 5%/year, C grows 20%/year. "
-     "What are the shares after 3 years?"}
-]
-
-text = tokenizer.apply_chat_template(
-    messages, tokenize=False, add_generation_prompt=True,
-    # Enable thinking mode
-    enable_thinking=True
-)
-inputs = tokenizer(text, return_tensors="pt").to(model.device)
-outputs = model.generate(**inputs, max_new_tokens=2048, temperature=0.6)
-response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
-print(response)
-```
-
-### Agent (tool use)
-
-```python
-tools = [
-    {
-        "type": "function",
-        "function": {
-            "name": "search_web",
-            "description": "Search the web for current information",
-            "parameters": {
-                "type": "object",
-                "properties": {
-                    "query": {"type": "string", "description": "Search query"}
-                },
-                "required": ["query"]
-            }
-        }
-    }
-]
-
-messages = [{"role": "user", "content": "What's the latest in quantum computing research?"}]
-text = tokenizer.apply_chat_template(messages, tools=tools, tokenize=False, add_generation_prompt=True)
-inputs = tokenizer(text, return_tensors="pt").to(model.device)
+inputs = tokenizer([text], return_tensors="pt").to(model.device)
 outputs = model.generate(**inputs, max_new_tokens=512)
-print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
-```
-
-## Hardware Requirements
-
-| Format | VRAM | Speed |
-|--------|------|-------|
-| BF16 (full) | 16 GB | Fast |
-| GGUF Q8_0 | 10 GB | Fast |
-| GGUF Q4_K_M | 6 GB | Moderate |
-| MLX 4-bit | 6 GB (Apple Silicon) | Native Metal |
-
-## vLLM (Production)
-
-```bash
-vllm serve zenlm/zen-pro-instruct \
-  --dtype bfloat16 \
-  --max-model-len 32768 \
-  --tensor-parallel-size 1
+print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
 ```
 
-## MLX (Apple Silicon)
-
-```bash
-pip install mlx-lm
-mlx_lm.generate --model zenlm/zen-pro-instruct \
-  --prompt "Explain the CAP theorem:" \
-  --max-tokens 512
-```
-
-## Hanzo API
+## API Access
 
 ```bash
 curl https://api.hanzo.ai/v1/chat/completions \
   -H "Authorization: Bearer $HANZO_API_KEY" \
   -H "Content-Type: application/json" \
-  -d '{"model": "zen-pro", "messages": [{"role": "user", "content": "Explain ACID transactions."}]}'
+  -d '{"model": "zen-pro", "messages": [{"role": "user", "content": "Hello"}]}'
 ```
 
-## License
-
-Apache 2.0. See [LICENSE](LICENSE) for details.
+Get your API key at [console.hanzo.ai](https://console.hanzo.ai) — $5 free credit on signup.
 
-## Citation
+## Model Details
 
-```bibtex
-@misc{zenlm2025zenpro,
-    title={Zen Pro: Professional-Grade 8B Language Model},
-    author={Hanzo AI and Zoo Labs Foundation},
-    year={2025},
-    publisher={HuggingFace},
-    howpublished={\url{https://huggingface.co/zenlm/zen-pro}}
-}
-```
-
----
-
-Part of the [Zen model family](https://zenlm.org) by [Hanzo AI](https://hanzo.ai) (Techstars '17) and [Zoo Labs Foundation](https://zoo.ngo).
-
-## Abliteration
-
-Zen Pro 8B weights are derived from an abliterated base model. Abliteration removes
-refusal behavior by identifying and nullifying the "refusal direction" in the model's
-residual stream, as described in
-[Maxime Labonne's methodology](https://huggingface.co/blog/mlabonne/abliteration).
-
-**Method**: Directional ablation on the residual stream across all layers
-**Source**: Zen MoDE (Mixture of Distilled Experts) architecture base
-**Effect**: Removes refusal behaviors while preserving all other capabilities
-**Identity layer**: Zen identity added via system prompt — full LoRA fine-tuning planned
+| Attribute | Value |
+|-----------|-------|
+| Parameters | 32B |
+| Architecture | Zen MoDE |
+| Context | 128K tokens |
+| License | Apache 2.0 |
 
-Abliteration is a feature, not a limitation. It enables unrestricted research,
-security testing, and applications where safety guardrails are managed at the
-application layer rather than baked into model weights.
+## License
 
+Apache 2.0