thesven
/

microsoft_WizardLM-2-7B-GPTQ-98k

@@ -1,199 +1,158 @@
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-library_name: transformers
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-# Model Card for Model ID
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 ---
+license: apache-2.0
 ---
+## Quantization Description
+This repo contains a GGUF Quantized versions of the WizardLM-2-7B model with the context expanded to 98k
+<div style="text-align: center;">
+    <a href="https://github.com/thesven/GGUF-n-Go">
+        <img src="https://github.com/thesven/GGUF-n-Go/blob/main/assets/quantized_with.png?raw=true" alt="image/png" style="max-width: 350px;">
+    </a>
+</div>
+### Prompt Template
+```bash
+### System: {system_message}
+### Human: {prompt}
+### Assistant:
+```
+### Stop Token
+```bash
+</s>
+```
+### Using with transformers
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+model_name_or_path = "thesven/microsoft_WizardLM-2-7B-GPTQ-98k"
+tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
+model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
+                                             device_map="auto",
+                                             trust_remote_code=False,
+                                             revision="main")
+model.pad_token = model.config.eos_token_id
+prompt_template=f'''
+### System: You are a very creative story writer. Write a store on the following topic:
+### Human: Write a story about Ai
+### Assistant:
+'''
+input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
+output = model.generate(inputs=input_ids, temperature=0.1, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
+print(tokenizer.decode(output[0]))
+```
+Weights sourced from:
+[lucyknada/microsoft_WizardLM-2-7B](https://huggingface.co/lucyknada/microsoft_WizardLM-2-7B)
+## Original Model Card
+<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>
+## 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://wizardlm.github.io/WizardLM2) and  upcoming paper.
+## Model Details
+* **Model name**: WizardLM-2 7B
+* **Developed by**: WizardLM@Microsoft AI
+* **Base model**: [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
+* **Parameters**: 7B
+* **Language(s)**: Multilingual
+* **Blog**: [Introducing WizardLM-2](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://raw.githubusercontent.com/WizardLM/WizardLM2/main/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://raw.githubusercontent.com/WizardLM/WizardLM2/main/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://wizardlm.github.io/WizardLM2) for more details of this system.
+<p align="center" width="100%">
+<a ><img src="https://raw.githubusercontent.com/WizardLM/WizardLM2/main/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