Update README.md

README.md

@@ -97,120 +97,20 @@ model-index:
       name: Open LLM Leaderboard
 ---
 
+Quantized model => https://huggingface.co/alpindale/WizardLM-2-8x22B
 
+**Quantization Details:**  
+Quantization is done using turboderp's ExLlamaV2 v0.2.2. 
 
-<p style="font-size:20px;" align="center">
-🏠 <a href="https://wizardlm.github.io/WizardLM2" target="_blank">WizardLM-2 Release Blog</a> </p>
-<p align="center">
-🤗 <a href="https://huggingface.co/collections/microsoft/wizardlm-2-661d403f71e6c8257dbd598a" target="_blank">HF Repo</a>  •🐱 <a href="https://github.com/victorsungo/WizardLM/tree/main/WizardLM-2" target="_blank">Github Repo</a>  • 🐦 <a href="https://twitter.com/WizardLM_AI" target="_blank">Twitter</a> • 📃 <a href="https://arxiv.org/abs/2304.12244" target="_blank">[WizardLM]</a>  • 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a>   • 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>  <br>
-</p>
-<p align="center">
-    👋 Join our <a href="https://discord.gg/VZjjHtWrKs" target="_blank">Discord</a>
-</p>
+I use the default calibration datasets and arguments. The repo also includes a "measurement.json" file, which was used during the quantization process.
 
-## See [here](https://huggingface.co/lucyknada/microsoft_WizardLM-2-7B) for the WizardLM-2-7B re-upload.
+For models with bits per weight (BPW) over 6.0, I default to quantizing the `lm_head` layer at 8 bits instead of the standard 6 bits.
 
-## News 🔥🔥🔥 [2024/04/15]
 
-We introduce and opensource WizardLM-2, our next generation state-of-the-art large language models, 
-which have improved performance on complex chat, multilingual, reasoning and agent. 
-New family includes three cutting-edge models: WizardLM-2 8x22B, WizardLM-2 70B, and WizardLM-2 7B.
 
-- WizardLM-2 8x22B is our most advanced model, demonstrates highly competitive performance compared to those leading proprietary works 
-and consistently outperforms all the existing state-of-the-art opensource models.
-- WizardLM-2 70B reaches top-tier reasoning capabilities and is the first choice in the same size. 
-- WizardLM-2 7B is the fastest and achieves comparable performance with existing 10x larger opensource leading models.
-
-For more  details of WizardLM-2 please read our [release blog post](https://web.archive.org/web/20240415221214/https://wizardlm.github.io/WizardLM2/) and  upcoming paper.
-
-
-## Model Details
-
-* **Model name**: WizardLM-2 8x22B
-* **Developed by**: WizardLM@Microsoft AI
-* **Model type**: Mixture of Experts (MoE)
-* **Base model**: [mistral-community/Mixtral-8x22B-v0.1](https://huggingface.co/mistral-community/Mixtral-8x22B-v0.1)
-* **Parameters**: 141B
-* **Language(s)**: Multilingual
-* **Blog**: [Introducing WizardLM-2](https://web.archive.org/web/20240415221214/https://wizardlm.github.io/WizardLM2/)
-* **Repository**: [https://github.com/nlpxucan/WizardLM](https://github.com/nlpxucan/WizardLM)
-* **Paper**: WizardLM-2 (Upcoming)
-* **License**: Apache2.0
-
-
-## Model Capacities
-
-
-**MT-Bench**
-
-We also adopt the automatic MT-Bench evaluation framework based on GPT-4 proposed by lmsys to assess the performance of models. 
-The WizardLM-2 8x22B even demonstrates highly competitive performance compared to the most advanced proprietary models. 
-Meanwhile, WizardLM-2 7B and WizardLM-2 70B are all the top-performing models among the other leading baselines at 7B to 70B model scales.
-
-<p align="center" width="100%">
-<a ><img src="https://web.archive.org/web/20240415175608im_/https://wizardlm.github.io/WizardLM2/static/images/mtbench.png" alt="MTBench" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a>
-</p>
-
-
-**Human Preferences Evaluation**
-
-We carefully collected a complex and challenging set consisting of real-world instructions, which includes main requirements of humanity, such as writing, coding, math, reasoning, agent, and multilingual. 
-We report the win:loss rate without tie:
-
-- WizardLM-2 8x22B is just slightly falling behind GPT-4-1106-preview, and significantly stronger than Command R Plus and GPT4-0314.
-- WizardLM-2 70B is better than GPT4-0613, Mistral-Large, and Qwen1.5-72B-Chat.
-- WizardLM-2 7B is comparable with Qwen1.5-32B-Chat, and surpasses Qwen1.5-14B-Chat and Starling-LM-7B-beta.
-
-<p align="center" width="100%">
-<a ><img src="https://web.archive.org/web/20240415163303im_/https://wizardlm.github.io/WizardLM2/static/images/winall.png" alt="Win" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a>
-</p>
-
-
-
-
-
-## Method Overview
-We built a **fully AI powered synthetic training system** to train WizardLM-2 models, please refer to our [blog](https://web.archive.org/web/20240415221214/https://wizardlm.github.io/WizardLM2/) for more details of this system.
-
-<p align="center" width="100%">
-<a ><img src="https://web.archive.org/web/20240415163303im_/https://wizardlm.github.io/WizardLM2/static/images/exp_1.png" alt="Method" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a>
-</p>
-
-
-
-
-
-## Usage
-
-❗<b>Note for model system prompts usage:</b>
-
-
-<b>WizardLM-2</b>  adopts the prompt format from <b>Vicuna</b> and supports **multi-turn** conversation. The prompt should be as following:
-
-```
-A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, 
-detailed, and polite answers to the user's questions. USER: Hi ASSISTANT: Hello.</s>
-USER: Who are you? ASSISTANT: I am WizardLM.</s>......
-```
-
-<b> Inference WizardLM-2 Demo Script</b>
-
-We provide a WizardLM-2 inference demo [code](https://github.com/nlpxucan/WizardLM/tree/main/demo) on our github.
-
-
-
-
-
-# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
-Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_alpindale__WizardLM-2-8x22B)
+---
 
-|      Metric       |Value|
-|-------------------|----:|
-|Avg.               |32.61|
-|IFEval (0-Shot)    |52.72|
-|BBH (3-Shot)       |48.58|
-|MATH Lvl 5 (4-Shot)|22.28|
-|GPQA (0-shot)      |17.56|
-|MuSR (0-shot)      |14.54|
-|MMLU-PRO (5-shot)  |39.96|
+**Who are you? What's with these weird BPWs on [insert model here]?**  
+I specialize in optimized EXL2 quantization for models in the 70B to 100B+ range, specifically tailored for 48GB VRAM setups. My rig is built using 2 x 3090s with a Ryzen APU (APU used solely for desktop output—no VRAM wasted on the 3090s). I use TabbyAPI for inference, targeting context sizes between 32K and 64K.
 
+Every model I upload includes a `config.yml` file with my ideal TabbyAPI settings. If you're using my config, don’t forget to set `PYTORCH_CUDA_ALLOC_CONF=backend:cudaMallocAsync` to save some VRAM.