Update model files

README.md

@@ -1,154 +1,46 @@
 ---
 license: apache-2.0
-tags:
-- zen-research
-- zen-ai
-- hypermodal
-- language-model
 language:
 - en
-library_name: transformers
 pipeline_tag: text-generation
+tags:
+- zen
+- hanzo-ai
+- qwen3
+- agent
 ---
 
-# zen-agent-4b
+# zenlm/zen-eco-4b-agent
 
-4B parameter tool-calling agent with Model Context Protocol (MCP) support
+Zen Eco 4B Agent - Tool-calling agent model
 
 ## Model Details
 
-- **Developed by**: Zen Research Authors
-- **Organization**: Zen Research DAO under [Zoo Labs Inc](https://github.com/zenlm) (501(c)(3) Non-Profit)
-- **Location**: San Francisco, California, USA
-- **Model type**: language-model
-- **Architecture**: Qwen3-4B with MCP
+- **Architecture**: Qwen3 base
 - **Parameters**: 4B
-- **License**: Apache 2.0
-- **Training**: Trained with [Zen Gym](https://github.com/zenlm/zen-gym)
-- **Inference**: Optimized for [Zen Engine](https://github.com/zenlm/zen-engine)
-
-## 🌟 Zen AI Ecosystem
-
-This model is part of the **Zen Research** hypermodal AI family - the world's most comprehensive open-source AI ecosystem.
-
-### Complete Model Family
-
-**Language Models:**
-- [zen-nano-0.6b](https://huggingface.co/zenlm/zen-nano-0.6b) - 0.6B edge model (44K tokens/sec)
-- [zen-eco-4b-instruct](https://huggingface.co/zenlm/zen-eco-4b-instruct) - 4B instruction model
-- [zen-eco-4b-thinking](https://huggingface.co/zenlm/zen-eco-4b-thinking) - 4B reasoning model
-- [zen-agent-4b](https://huggingface.co/zenlm/zen-agent-4b) - 4B tool-calling agent
-
-**3D & World Generation:**
-- [zen-3d](https://huggingface.co/zenlm/zen-3d) - Controllable 3D asset generation
-- [zen-voyager](https://huggingface.co/zenlm/zen-voyager) - Camera-controlled world exploration
-- [zen-world](https://huggingface.co/zenlm/zen-world) - Large-scale world simulation
-
-**Video Generation:**
-- [zen-director](https://huggingface.co/zenlm/zen-director) - Text/image-to-video (5B)
-- [zen-video](https://huggingface.co/zenlm/zen-video) - Professional video synthesis
-- [zen-video-i2v](https://huggingface.co/zenlm/zen-video-i2v) - Image-to-video animation
-
-**Audio Generation:**
-- [zen-musician](https://huggingface.co/zenlm/zen-musician) - Music generation (7B)
-- [zen-foley](https://huggingface.co/zenlm/zen-foley) - Video-to-audio Foley effects
-
-**Infrastructure:**
-- [Zen Gym](https://github.com/zenlm/zen-gym) - Unified training platform
-- [Zen Engine](https://github.com/zenlm/zen-engine) - High-performance inference
+- **Training**: Fine-tuned with Zen identity
+- **Developer**: Hanzo AI
 
 ## Usage
 
-### Quick Start
-
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-model = AutoModelForCausalLM.from_pretrained("zenlm/zen-agent-4b")
-tokenizer = AutoTokenizer.from_pretrained("zenlm/zen-agent-4b")
-
-inputs = tokenizer("Hello!", return_tensors="pt")\noutputs = model.generate(**inputs)\nprint(tokenizer.decode(outputs[0]))
-```
-
-### With Zen Engine
-
-```bash
-# High-performance inference (44K tokens/sec on M3 Max)
-zen-engine serve --model zenlm/zen-agent-4b --port 3690
-```
-
-```python
-# OpenAI-compatible API
-from openai import OpenAI
+model = AutoModelForCausalLM.from_pretrained("zenlm/zen-eco-4b-agent")
+tokenizer = AutoTokenizer.from_pretrained("zenlm/zen-eco-4b-agent")
 
-client = OpenAI(base_url="http://localhost:3690/v1")
-response = client.chat.completions.create(
-    model="zenlm/zen-agent-4b",
-    messages=[{"role": "user", "content": "Hello!"}]
-)
+prompt = "Hello, who are you?"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=50)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
 ```
 
 ## Training
 
-Fine-tune with [Zen Gym](https://github.com/zenlm/zen-gym):
-
-```bash
-git clone https://github.com/zenlm/zen-gym
-cd zen-gym
-
-# LoRA fine-tuning
-llamafactory-cli train --config configs/zen_lora.yaml \
-    --model_name_or_path zenlm/zen-agent-4b
-
-# GRPO reinforcement learning (40-60% memory reduction)
-llamafactory-cli train --config configs/zen_grpo.yaml \
-    --model_name_or_path zenlm/zen-agent-4b
-```
-
-Supported methods: LoRA, QLoRA, DoRA, GRPO, GSPO, DPO, PPO, KTO, ORPO, SimPO, Unsloth
-
-## Performance
-
-- **Speed**: 28K tokens/sec (RTX 4090)
-- **Memory**: 2.5GB (Q4_K_M) to 8GB (F16)
-- **MCP**: Full Model Context Protocol support
-- **Tools**: 100+ function calling accuracy
-
-## Ethical Considerations
-
-- **Open Research**: Released under Apache 2.0 for maximum accessibility
-- **Environmental Impact**: Optimized for eco-friendly deployment
-- **Transparency**: Full training details and model architecture disclosed
-- **Safety**: Comprehensive testing and evaluation
-- **Non-Profit**: Developed by Zoo Labs Inc (501(c)(3)) for public benefit
-
-## Citation
-
-```bibtex
-@misc{zenzenagent4b2025,
-  title={zen-agent-4b: 4B parameter tool-calling agent with Model Context Protocol (MCP) support},
-  author={Zen Research Authors},
-  year={2025},
-  publisher={Zoo Labs Inc},
-  organization={Zen Research DAO},
-  url={https://huggingface.co/zenlm/zen-agent-4b}
-}
-```
-
-## Links
-
-- **Organization**: [github.com/zenlm](https://github.com/zenlm) • [huggingface.co/zenlm](https://huggingface.co/zenlm)
-- **Training Platform**: [Zen Gym](https://github.com/zenlm/zen-gym)
-- **Inference Engine**: [Zen Engine](https://github.com/zenlm/zen-engine)
-- **Parent Org**: [Zoo Labs Inc](https://github.com/zenlm) (501(c)(3) Non-Profit, San Francisco)
-- **Contact**: dev@hanzo.ai • +1 (913) 777-4443
+Trained with fixed seed (42) for reproducibility.
+Base model: Qwen3-4B
 
 ## License
 
-Apache License 2.0
-
-Copyright 2025 Zen Research Authors
-
----
-
-**Zen Research** - Building open, eco-friendly AI for everyone 🌱
+Apache 2.0