Update README.md

README.md

@@ -1,169 +1,111 @@
 ---
 license: apache-2.0
-pipeline_tag: image-text-to-text
 library_name: transformers
+language:
+- en
+pipeline_tag: image-text-to-text
+tags:
+- text-generation
+- instruct
+- coding
+- research
+- qwen
+- hyze
+- Hitesh
+metrics:
+- accuracy
+base_model:
+- Qwen/Qwen3-VL-30B-A3B-Instruct
 ---
-<a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;">
-    <img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
-</a>
-
-
-# Qwen3-VL-30B-A3B-Instruct
-
-
-Meet Qwen3-VL — the most powerful vision-language model in the Qwen series to date.
 
-This generation delivers comprehensive upgrades across the board: superior text understanding & generation, deeper visual perception & reasoning, extended context length, enhanced spatial and video dynamics comprehension, and stronger agent interaction capabilities.
-
-Available in Dense and MoE architectures that scale from edge to cloud, with Instruct and reasoning‑enhanced Thinking editions for flexible, on‑demand deployment.
-
-
-#### Key Enhancements:
-
-* **Visual Agent**: Operates PC/mobile GUIs—recognizes elements, understands functions, invokes tools, completes tasks.
-
-* **Visual Coding Boost**: Generates Draw.io/HTML/CSS/JS from images/videos.
+<p align="center">
+  <img src="https://i.imgur.com/ePJMLNp.png" alt="Hyze Logo" width="220"/>
+</p>
 
-* **Advanced Spatial Perception**: Judges object positions, viewpoints, and occlusions; provides stronger 2D grounding and enables 3D grounding for spatial reasoning and embodied AI.
+<p align="center">
+  <img src="https://qwen.readthedocs.io/en/v2.0/_images/qwen2.png" alt="Qwen Logo" width="220"/>
+</p>
 
-* **Long Context & Video Understanding**: Native 256K context, expandable to 1M; handles books and hours-long video with full recall and second-level indexing.
+<h1 align="center">HyzeQwenInstruct-30B</h1>
 
-* **Enhanced Multimodal Reasoning**: Excels in STEM/Math—causal analysis and logical, evidence-based answers.
+<p align="center">
+  A high-performance instruction model by <b>Hyze AI</b> built for coding and research.
+</p>
 
-* **Upgraded Visual Recognition**: Broader, higher-quality pretraining is able to “recognize everything”—celebrities, anime, products, landmarks, flora/fauna, etc.
+<p align="center">
+  🔗 <a href="https://hyzeai.vercel.app">hyzeai.vercel.app</a> •  
+  📘 <a href="https://hyzedocs.vercel.app">hyzedocs.vercel.app</a> •  
+  🧠 <a href="https://hyzecode.vercel.app">hyzecode.vercel.app</a>
+</p>
 
-* **Expanded OCR**: Supports 32 languages (up from 19); robust in low light, blur, and tilt; better with rare/ancient characters and jargon; improved long-document structure parsing.
+---
 
-* **Text Understanding on par with pure LLMs**: Seamless text–vision fusion for lossless, unified comprehension.
+## 🚀 Overview
 
+**HyzeQwenInstruct-30B** is a 30-billion parameter instruction-tuned large language model optimized for:
 
-#### Model Architecture Updates:
+- 🧑‍💻 Advanced code generation  
+- 📚 Technical research & reasoning  
+- 🧠 Deep structured explanations  
+- 🤖 Strong instruction following  
 
-<p align="center">
-    <img src="https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3-VL/qwen3vl_arc.jpg" width="80%"/>
-<p>
+Designed for developers, engineers, and researchers who need powerful AI assistance.
 
+---
 
-1. **Interleaved-MRoPE**: Full‑frequency allocation over time, width, and height via robust positional embeddings, enhancing long‑horizon video reasoning.
+## 🧠 Training Focus
 
-2. **DeepStack**: Fuses multi‑level ViT features to capture fine‑grained details and sharpen image–text alignment.
+HyzeQwenInstruct-30B was optimized for:
 
-3. **Text–Timestamp Alignment:** Moves beyond T‑RoPE to precise, timestamp‑grounded event localization for stronger video temporal modeling.
+### 🧑‍💻 Coding
+- Python, JavaScript, C++, and more
+- Code completion & generation
+- Debugging & refactoring
+- Algorithm explanations
 
-This is the weight repository for Qwen3-VL-30B-A3B-Instruct.
+### 📊 Research & Technical Reasoning
+- Structured academic-style answers
+- Scientific explanations
+- Step-by-step reasoning
+- Long-form responses
 
+### 🎯 Instruction Tuning
+- Precise intent following
+- Context retention
+- Clean output formatting
 
 ---
 
-## Model Performance
+## 📊 Benchmarks — Technical Comparison
 
-**Multimodal performance**
+| Model | Size | Coding | Reasoning | Notes |
+|-------|------|--------|-----------|-------|
+| **HyzeQwenInstruct-30B** | 30B | ⭐⭐⭐⭐☆ | ⭐⭐⭐⭐☆ | Optimized for dev + research |
+| Qwen-30B-Instruct | 30B | ⭐⭐⭐⭐☆ | ⭐⭐⭐⭐☆ | Strong base alignment |
+| GPT-NeoX-20B | 20B | ⭐⭐⭐☆☆ | ⭐⭐⭐☆☆ | Smaller parameter count |
+| GPT-1 | 117M | ⭐⭐☆☆☆ | ⭐⭐☆☆☆ | Early generation model |
 
-![](https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3-VL/table_nothinking_vl-30a3.jpg)
+### ⚡ Performance Characteristics
 
-**Pure text performance**
-![](https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3-VL/table_nothinking_text-30a3.jpg)
+- Strong code structure generation  
+- Clear technical explanations  
+- High instruction accuracy  
+- Suitable for professional workflows  
 
-## Quickstart
+> Benchmark ratings are based on internal qualitative evaluation.
 
-Below, we provide simple examples to show how to use Qwen3-VL with 🤖 ModelScope and 🤗 Transformers.
-
-The code of Qwen3-VL has been in the latest Hugging Face transformers and we advise you to build from source with command:
-```
-pip install git+https://github.com/huggingface/transformers
-# pip install transformers==4.57.0 # currently, V4.57.0 is not released
-```
+---
 
-### Using 🤗 Transformers to Chat
+## 🧪 Usage
 
-Here we show a code snippet to show how to use the chat model with `transformers`:
+### Transformers (Python)
 
 ```python
-from transformers import Qwen3VLMoeForConditionalGeneration, AutoProcessor
+from transformers import pipeline
 
-# default: Load the model on the available device(s)
-model = Qwen3VLMoeForConditionalGeneration.from_pretrained(
-    "Qwen/Qwen3-VL-30B-A3B-Instruct", dtype="auto", device_map="auto"
+generator = pipeline(
+    "text-generation",
+    model="HyzeAI/HyzeQwenInstruct-30B"
 )
 
-# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
-# model = Qwen3VLMoeForConditionalGeneration.from_pretrained(
-#     "Qwen/Qwen3-VL-30B-A3B-Instruct",
-#     dtype=torch.bfloat16,
-#     attn_implementation="flash_attention_2",
-#     device_map="auto",
-# )
-
-processor = AutoProcessor.from_pretrained("Qwen/Qwen3-VL-30B-A3B-Instruct")
-
-messages = [
-    {
-        "role": "user",
-        "content": [
-            {
-                "type": "image",
-                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
-            },
-            {"type": "text", "text": "Describe this image."},
-        ],
-    }
-]
-
-# Preparation for inference
-inputs = processor.apply_chat_template(
-    messages,
-    tokenize=True,
-    add_generation_prompt=True,
-    return_dict=True,
-    return_tensors="pt"
-)
-
-# Inference: Generation of the output
-generated_ids = model.generate(**inputs, max_new_tokens=128)
-generated_ids_trimmed = [
-    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
-]
-output_text = processor.batch_decode(
-    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
-)
-print(output_text)
-```
-
-
-
-## Citation
-
-If you find our work helpful, feel free to give us a cite.
-
-```
-@misc{qwen3technicalreport,
-      title={Qwen3 Technical Report}, 
-      author={Qwen Team},
-      year={2025},
-      eprint={2505.09388},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2505.09388}, 
-}
-
-@article{Qwen2.5-VL,
-  title={Qwen2.5-VL Technical Report},
-  author={Bai, Shuai and Chen, Keqin and Liu, Xuejing and Wang, Jialin and Ge, Wenbin and Song, Sibo and Dang, Kai and Wang, Peng and Wang, Shijie and Tang, Jun and Zhong, Humen and Zhu, Yuanzhi and Yang, Mingkun and Li, Zhaohai and Wan, Jianqiang and Wang, Pengfei and Ding, Wei and Fu, Zheren and Xu, Yiheng and Ye, Jiabo and Zhang, Xi and Xie, Tianbao and Cheng, Zesen and Zhang, Hang and Yang, Zhibo and Xu, Haiyang and Lin, Junyang},
-  journal={arXiv preprint arXiv:2502.13923},
-  year={2025}
-}
-
-@article{Qwen2VL,
-  title={Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution},
-  author={Wang, Peng and Bai, Shuai and Tan, Sinan and Wang, Shijie and Fan, Zhihao and Bai, Jinze and Chen, Keqin and Liu, Xuejing and Wang, Jialin and Ge, Wenbin and Fan, Yang and Dang, Kai and Du, Mengfei and Ren, Xuancheng and Men, Rui and Liu, Dayiheng and Zhou, Chang and Zhou, Jingren and Lin, Junyang},
-  journal={arXiv preprint arXiv:2409.12191},
-  year={2024}
-}
-
-@article{Qwen-VL,
-  title={Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond},
-  author={Bai, Jinze and Bai, Shuai and Yang, Shusheng and Wang, Shijie and Tan, Sinan and Wang, Peng and Lin, Junyang and Zhou, Chang and Zhou, Jingren},
-  journal={arXiv preprint arXiv:2308.12966},
-  year={2023}
-}
-```
+print(generator("Write a Python function to implement quicksort:"))