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  ---
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  datasets:
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  - liuhaotian/LLaVA-Pretrain
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- - liuhaotian/LLaVA-Instruct-150K
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  pipeline_tag: image-text-to-text
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- library_name: xtuner
 
 
 
 
 
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  ---
 
 
 
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- <div align="center">
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- <img src="https://github.com/InternLM/lmdeploy/assets/36994684/0cf8d00f-e86b-40ba-9b54-dc8f1bc6c8d8" width="600"/>
 
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- [![Generic badge](https://img.shields.io/badge/GitHub-%20XTuner-black.svg)](https://github.com/InternLM/xtuner)
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- </div>
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- ## Model
 
 
 
 
 
 
 
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- llava-internlm2-20b is a LLaVA model fine-tuned from [InternLM2-Chat-20B](https://huggingface.co/internlm/internlm2-chat-20b) and [CLIP-ViT-Large-patch14-336](https://huggingface.co/openai/clip-vit-large-patch14-336) with [LLaVA-Pretrain](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain) and [LLaVA-Instruct](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K) by [XTuner](https://github.com/InternLM/xtuner).
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- ## Results
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- | Model | MMBench Test (EN) | MMBench Dev (EN) | MMBench Test (CN) | MMBench Dev (CN) | CCBench Dev | MME | SEEDBench_IMG | MMVet | MMMU Dev | MathVista MiniTest | HallusionBench aAcc |
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- | :------------------------- | :---------------: | :--------------: | :---------------: | :--------------: | :---------: | :--: | :-----------: | :---: | :------: | :----------------: | :-----------------: |
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- | LLaVA-v1.5-7B (XTuner) | 67.7 | 69.2 | 61.0 | 59.7 | 28.4 | 1716 | 66.4 | 32.2 | 33.7 | 24.2 | 46.2 |
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- | LLaVA-v1.5-13B (XTuner) | 68.8 | 69.5 | 64.7 | 63.1 | 32.9 | 1766 | 67.9 | 35.9 | 35.2 | 26.2 | 46.9 |
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- | LLaVA-InternLM-7B (XTuner) | 69.0 | 68.5 | 66.7 | 63.8 | 37.3 | 1637 | 65.7 | 32.4 | 36.9 | 26.3 | 49.1 |
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- | LLaVA-InternLM2-7B | 73.3 | 74.6 | 71.7 | 72.0 | 42.5 | 1700 | 71.2 | 35.9 | 40.1 | 25.5 | 46.8 |
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- | LLaVA-InternLM2-20B | 75.1 | 73.5 | 73.7 | 72.8 | 46.3 | 1868 | 70.2 | 37.2 | 39.4 | 24.6 | 47.7 |
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- ## Quickstart
 
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- ### Installation
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- ```shell
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- pip install -U 'xtuner[deepspeed]'
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- ```
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- ### Chat
 
 
 
 
 
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- ```shell
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- xtuner chat internlm/internlm2-chat-20b \
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- --visual-encoder openai/clip-vit-large-patch14-336 \
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- --llava xtuner/llava-internlm2-20b \
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- --prompt-template internlm2_chat \
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- --image $IMAGE_PATH
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- ```
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- ### Training
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- 1. Alignment module pretraining (saved by default in `./work_dirs/`)
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- ```shell
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- NPROC_PER_NODE=8 xtuner train llava_internlm2_chat_20b_clip_vit_large_p14_336_e1_gpu8_pretrain --deepspeed deepspeed_zero2
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- ```
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- 2. Instruction following fine-tuning (saved by default in `./work_dirs/`)
 
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- ```shell
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- NPROC_PER_NODE=8 xtuner train llava_internlm2_chat_20b_qlora_clip_vit_large_p14_336_lora_e1_gpu8_finetune --deepspeed deepspeed_zero2
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- ```
 
 
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- ### MMBench Evaluation
 
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- XTuner integrates the MMBench evaluation, and you can perform evaluations with the following command!
 
 
 
 
 
 
 
 
 
 
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  ```bash
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- xtuner mmbench internlm/internlm2-chat-20b \
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- --visual-encoder openai/clip-vit-large-patch14-336 \
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- --llava xtuner/llava-internlm2-20b \
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- --prompt-template internlm2_chat \
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- --data-path $MMBENCH_DATA_PATH \
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- --work-dir $RESULT_PATH
 
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  ```
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- After the evaluation is completed, if it's a development set, it will directly print out the results; If it's a test set, you need to submit `mmbench_result.xlsx` to the official MMBench for final evaluation to obtain precision results!
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- ## Citation
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ```bibtex
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- @misc{2023xtuner,
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- title={XTuner: A Toolkit for Efficiently Fine-tuning LLM},
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- author={XTuner Contributors},
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- howpublished = {\url{https://github.com/InternLM/xtuner}},
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- year={2023}
 
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  }
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  ```
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  datasets:
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  - liuhaotian/LLaVA-Pretrain
 
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  pipeline_tag: image-text-to-text
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+ library_name: transformers.js
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+ license: apache-2.0
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+ language:
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+ - en
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+ metrics:
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+ - accuracy
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  ---
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+ <p align="center">
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+ <img src="https://i.imgur.com/ePJMLNp.png" alt="Hyze Logo" width="120"/>
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+ </p>
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+ <p align="center">
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+ <strong>20 Billion Parameters • Research-Grade • Open Weights</strong>
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+ </p>
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+ <p align="center">
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+ <a href="https://hyzeai.vercel.app">🌐 Try Hyze RE1 Pro</a> •
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+ <a href="https://huggingface.co/HyzeAI">🤗 Hugging Face</a> •
23
+ <a href="https://github.com/HyzeAI">📁 GitHub</a>
24
+ </p>
25
 
26
+ ---
27
 
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+ ## 🚀 Overview
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+ **Hyze RE1 Pro** is a **20 billion parameter** transformer model designed exclusively for **research purposes**. Built on the philosophy that **frontier AI should not belong only to those with billion-dollar budgets**, RE1 Pro delivers strong reasoning capabilities in a fully open-weight package.
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+ | Attribute | Details |
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+ |----------|---------|
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+ | **Parameters** | 20B |
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+ | **Architecture** | Transformer (Decoder-only) |
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+ | **Precision** | BF16 / INT4 (quantized) |
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+ | **Context Length** | 32K tokens |
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+ | **License** | Apache 2.0 |
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+ | **Target** | Academic / Non-Commercial Research |
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+ ---
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+ ## 🧠 Capabilities
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+ Hyze RE1 Pro excels at:
 
 
 
 
 
 
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+ - 🔬 **Scientific reasoning** – Physics, mathematics, code
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+ - 🌌 **Space & astronomy** – Continued pretraining on domain-specific corpora
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+ - 📚 **Research summarization** – ArXiv, technical papers
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+ - 🧮 **Complex instruction following** – Multi-step reasoning tasks
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+ > ⚠️ **Research Use Only**
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+ > RE1 Pro is not optimized for general consumer chatbots. It is a **research instrument**, not a product. For general chat, see [HyzeMini](https://huggingface.co/HyzeAI/HyzeMini).
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+ ---
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+ ## 📊 Benchmarks (Preliminary)
 
 
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+ | Benchmark | Score (20B) | Comparison |
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+ |-----------|-------------|------------|
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+ | MMLU (5-shot) | **68.2** | LLaMA2-13B: 54.8 |
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+ | HumanEval (pass@1) | **37.4** | CodeLlama-13B: 36.0 |
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+ | GSM8K (8-shot) | **62.1** | Mistral-7B: 52.2 |
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+ | MATH (4-shot) | **26.8** | LLaMA2-34B: 27.0 |
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66
+ *Benchmarks conducted in BF16. Quantized versions may show slight degradation.*
 
 
 
 
 
 
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+ ---
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+ ## ⚙️ Installation & Usage
71
 
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+ ### Python (Transformers)
 
 
73
 
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+ ```python
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
76
 
77
+ model = AutoModelForCausalLM.from_pretrained(
78
+ "HyzeAI/Hyze-RE1-Pro",
79
+ torch_dtype="auto",
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+ device_map="auto"
81
+ )
82
 
83
+ tokenizer = AutoTokenizer.from_pretrained("HyzeAI/Hyze-RE1-Pro")
84
 
85
+ prompt = "Explain the rocket equation in simple terms."
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+ inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
87
 
88
+ outputs = model.generate(
89
+ **inputs,
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+ max_new_tokens=256,
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+ temperature=0.7,
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+ top_p=0.9
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+ )
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+
95
+ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
96
+ ```
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+
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+ ### llama.cpp (CPU + Quantized)
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100
  ```bash
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+ # Download GGUF from Hugging Face
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+ wget https://huggingface.co/HyzeAI/Hyze-RE1-Pro-GGUF/resolve/main/hyze-re1-pro-q4_k_m.gguf
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+
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+ ./llama-cli -m hyze-re1-pro-q4_k_m.gguf \
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+ -p "List three challenges of Mars colonization:" \
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+ -n 512 \
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+ -t 8
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  ```
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110
+ ---
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+
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+ ## 💻 Hardware Requirements
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+
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+ | Mode | VRAM | RAM | Recommended Hardware |
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+ |------|------|-----|---------------------|
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+ | FP16 (full) | **40GB+** | 64GB | 1x A100 / 2x RTX 3090 |
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+ | INT4 (Q4) | **12GB** | 16GB | RTX 4070 Ti / Mac M2+ |
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+ | CPU (GGUF) | — | 32GB | AMD EPYC / Intel Xeon |
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+
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+ > 💡 **Quantized versions** (4-bit) make RE1 Pro runnable on consumer hardware with minimal quality loss.
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+
122
+ ---
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+
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+ ## 🧪 Research Access
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126
+ Hyze RE1 Pro is **free and open weights** under Apache 2.0.
127
+ You do not need to apply for access. No approval required. No gated repository.
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+
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+ **We believe research should not wait for permission.**
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+
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+ ---
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+
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+ ## 🧭 About Hyze AI
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+
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+ <p align="left">
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+ <img src="https://i.imgur.com/ePJMLNp.png" alt="Hyze Logo" width="30"/>
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+ </p>
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+
139
+ **Hyze AI** is a one-person research lab founded by **Hitesh**, a 13-year-old builder.
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+ Hyze exists to prove that **age and budget are not prerequisites for advancing AI**.
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+
142
+ - 🚀 **Mission**: Democratize large-scale AI research
143
+ - 🔓 **License Philosophy**: Apache 2.0 — no strings attached
144
+ - 🌍 **Focus**: Space, science, and accessible reasoning
145
+
146
+ > *"DeepSeek proved you don't need billions. We're proving you don't need to be 30."*
147
+
148
+ ---
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+
150
+ ## 📎 Citation
151
 
152
  ```bibtex
153
+ @misc{hyze-re1-pro-2025,
154
+ author = {Hitesh Vinothkumar},
155
+ title = {Hyze RE1 Pro: A 20B Parameter Research Model},
156
+ year = {2025},
157
+ publisher = {Hugging Face},
158
+ url = {https://huggingface.co/HyzeAI/Hyze-RE1-Pro}
159
  }
160
  ```
161
+
162
+ ---
163
+
164
+ ## 🤝 Support & Contact
165
+
166
+ - 💬 **Try the live demo**: [https://hyzeai.vercel.app](https://hyzeai.vercel.app)
167
+ - 📧 **Email**: hiteshv2603@gmail.com
168
+ - 🐦 **Twitter/X**: [@HyzeAI](https://twitter.com/HyzeAI)
169
+ - 💼 **GitHub**: [HyzeAI](https://github.com/HyzeAI)
170
+
171
+ **For research collaborations, compute sponsorship, or academic partnerships — reach out.**
172
+
173
+ ---
174
+
175
+ <p align="center">
176
+ <sub>Built with ❤️ and zero GPUs (so far).</sub>
177
+ <br/>
178
+ <sub>© 2025 Hyze AI. Apache 2.0.</sub>
179
+ </p>