Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+base_model: Dream-org/Dream-v0-Instruct-7B
+tags:
+  - fp8
+  - quantized
+  - llmcompressor
+  - tevunahai
+  - professional-grade
+  - diffusion-lm
+  - dream
+  - dllm
+---
+
+# Dream-v0-Instruct-7B-FP8
+
+## TevunahAi Professional Quantization
+
+**🏆 First FP8 quantized Dream model for native PyTorch/transformers inference.**
+
+This is an FP8 quantized version of [Dream-v0-Instruct-7B](https://huggingface.co/Dream-org/Dream-v0-Instruct-7B), 
+a diffusion-based large language model from HKU NLP Group.
+
+### What is Dream?
+
+Dream 7B is a **Diffusion Large Language Model (dLLM)** - unlike traditional autoregressive models 
+(GPT, LLaMA, Claude) that generate text left-to-right one token at a time, Dream uses 
+**parallel denoising** to refine the entire sequence simultaneously.
+
+Key advantages:
+- 🔄 **Bidirectional context modeling** - considers full context in both directions
+- 🎯 **Flexible text generation order** - not constrained to left-to-right
+- 🧠 **Superior planning abilities** - excels at tasks requiring multi-step reasoning
+- ⚡ **Adjustable quality-speed tradeoff** - control inference steps for your needs
+
+### Quantization Details
+
+| Property | Value |
+|----------|-------|
+| Original Model | Dream-v0-Instruct-7B |
+| Quantization | FP8 Dynamic (Weight-only) |
+| Method | llmcompressor FP8_DYNAMIC |
+| Calibration | Data-free |
+| Hardware | Dual Xeon Max 9480 + RTX 5000 Ada |
+| Quantization Time | 1.7 minutes |
+
+### Memory Comparison
+
+| Precision | Size | VRAM Required |
+|-----------|------|---------------|
+| BF16 | ~14 GB | ~16 GB |
+| **FP8** | **~8.7 GB** | **~10 GB** |
+
+### Usage
+
+```python
+import torch
+from transformers import AutoModel, AutoTokenizer
+
+model_path = "TevunahAi/Dream-v0-Instruct-7B-FP8"
+
+model = AutoModel.from_pretrained(
+    model_path,
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+
+messages = [
+    {"role": "user", "content": "Explain quantum computing in simple terms."}
+]
+
+inputs = tokenizer.apply_chat_template(
+    messages,
+    return_tensors="pt",
+    return_dict=True,
+    add_generation_prompt=True
+)
+
+input_ids = inputs.input_ids.to(model.device)
+attention_mask = inputs.attention_mask.to(model.device)
+
+# Dream uses diffusion_generate, not generate!
+output = model.diffusion_generate(
+    input_ids,
+    attention_mask=attention_mask,
+    max_new_tokens=256,
+    steps=256,
+    temperature=0.3,
+    top_p=0.95,
+    alg="entropy",
+    alg_temp=0.,
+)
+
+# Decode and clean up response
+response = tokenizer.decode(output[0][input_ids.shape[1]:].tolist())
+response = response.split("<|endoftext|>")[0].strip()
+print(response)
+```
+
+### Generation Parameters
+
+| Parameter | Description | Recommended |
+|-----------|-------------|-------------|
+| `steps` | Number of diffusion steps (quality vs speed) | 128-512 |
+| `max_new_tokens` | Maximum tokens to generate | 256-512 |
+| `temperature` | Randomness (lower = more deterministic) | 0.2-0.5 |
+| `top_p` | Nucleus sampling threshold | 0.9-0.95 |
+| `alg` | Decoding algorithm | "entropy" |
+| `alg_temp` | Algorithm temperature | 0.0 |
+
+**Tips:**
+- More `steps` = higher quality but slower
+- For math/code: use lower temperature (0.1-0.2)
+- For creative tasks: use higher temperature (0.5-0.7)
+
+### Important Notes
+
+1. ⚠️ **Use `diffusion_generate()`** not `generate()` - Dream is a diffusion model!
+2. Requires `trust_remote_code=True` for custom model code
+3. Stop token cleanup: split response on `<|endoftext|>`
+4. Context length: 2048 tokens
+
+### Verified Working
+
+```
+Input: "What is 2+2? Answer briefly."
+Output: "4"
+✓ Correct!
+```
+
+### Credits
+
+- **Original Model**: [Dream-org / HKU NLP Group](https://huggingface.co/Dream-org) - Pioneering diffusion-based language models
+- **Quantization**: [TevunahAi](https://tevunah.ai) - Professional AI model quantization services
+- **Method**: [llmcompressor](https://github.com/vllm-project/llm-compressor) by vLLM Project
+
+### Citation
+
+If you use Dream, please cite the original paper:
+
+```bibtex
+@article{dream2025,
+  title={Dream 7B: Diffusion Large Language Models},
+  author={Ye, Jiacheng and Xie, Zhihui and others},
+  journal={arXiv preprint},
+  year={2025}
+}
+```
+
+### License
+
+Apache 2.0 (same as original Dream model)
+
+---
+