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- library_name: transformers
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- tags: []
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a πŸ€— transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
 
 
 
 
 
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 
 
 
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
 
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
 
 
 
 
 
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- [More Information Needed]
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- ## Training Details
 
 
 
 
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- ### Training Data
 
 
 
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
 
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- [More Information Needed]
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- ### Training Procedure
 
 
 
 
 
 
 
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
 
 
 
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
 
 
 
 
 
 
 
 
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- [More Information Needed]
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
 
 
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- ### Model Architecture and Objective
 
 
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- ### Compute Infrastructure
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- #### Hardware
 
 
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- #### Software
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
 
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- **APA:**
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- [More Information Needed]
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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+ license: apache-2.0
 
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+ ## Quantization Description
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+ This repo contains a GPTQ 4bit Quantized version of the WizardLM-2-7B model
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+ ### Prompt Template
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+ ```bash
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+ ### System: {system_message}
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+ ### Human: {prompt}
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+ ### Assistant:
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+ ```
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+ ### Stop Token
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+ ```bash
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+ </s>
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+ ```
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+ ### Using with transformers
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+ ```python
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+ from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
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+ model_name_or_path = "thesven/microsoft_WizardLM-2-7B-GPTQ"
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+ tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
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+ model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
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+ device_map="auto",
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+ trust_remote_code=False,
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+ revision="main")
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+ model.pad_token = model.config.eos_token_id
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+ prompt_template=f'''
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+ ### System: You are a very creative story writer. Write a store on the following topic:
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+ ### Human: Write a story about Ai
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+ ### Assistant:
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+ '''
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+ input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
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+ output = model.generate(inputs=input_ids, temperature=0.1, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
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+ print(tokenizer.decode(output[0]))
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+ ```
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+ Weights sourced from:
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+ [lucyknada/microsoft_WizardLM-2-7B](https://huggingface.co/lucyknada/microsoft_WizardLM-2-7B)
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+ ## Original Model Card
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+ <p style="font-size:20px;" align="center">
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+ 🏠 <a href="https://wizardlm.github.io/WizardLM2" target="_blank">WizardLM-2 Release Blog</a> </p>
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+ <p align="center">
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+ πŸ€— <a href="https://huggingface.co/collections/microsoft/wizardlm-2-661d403f71e6c8257dbd598a" target="_blank">HF Repo</a> β€’πŸ± <a href="https://github.com/victorsungo/WizardLM/tree/main/WizardLM-2" target="_blank">Github Repo</a> β€’ 🐦 <a href="https://twitter.com/WizardLM_AI" target="_blank">Twitter</a> β€’ πŸ“ƒ <a href="https://arxiv.org/abs/2304.12244" target="_blank">[WizardLM]</a> β€’ πŸ“ƒ <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> β€’ πŸ“ƒ <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a> <br>
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+ </p>
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+ <p align="center">
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+ πŸ‘‹ Join our <a href="https://discord.gg/VZjjHtWrKs" target="_blank">Discord</a>
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+ </p>
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+ ## News πŸ”₯πŸ”₯πŸ”₯ [2024/04/15]
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+ We introduce and opensource WizardLM-2, our next generation state-of-the-art large language models,
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+ which have improved performance on complex chat, multilingual, reasoning and agent.
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+ New family includes three cutting-edge models: WizardLM-2 8x22B, WizardLM-2 70B, and WizardLM-2 7B.
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+ - WizardLM-2 8x22B is our most advanced model, demonstrates highly competitive performance compared to those leading proprietary works
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+ and consistently outperforms all the existing state-of-the-art opensource models.
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+ - WizardLM-2 70B reaches top-tier reasoning capabilities and is the first choice in the same size.
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+ - WizardLM-2 7B is the fastest and achieves comparable performance with existing 10x larger opensource leading models.
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+ For more details of WizardLM-2 please read our [release blog post](https://wizardlm.github.io/WizardLM2) and upcoming paper.
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+ ## Model Details
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ * **Model name**: WizardLM-2 7B
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+ * **Developed by**: WizardLM@Microsoft AI
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+ * **Base model**: [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
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+ * **Parameters**: 7B
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+ * **Language(s)**: Multilingual
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+ * **Blog**: [Introducing WizardLM-2](https://wizardlm.github.io/WizardLM2)
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+ * **Repository**: [https://github.com/nlpxucan/WizardLM](https://github.com/nlpxucan/WizardLM)
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+ * **Paper**: WizardLM-2 (Upcoming)
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+ * **License**: Apache2.0
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+ ## Model Capacities
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+ **MT-Bench**
 
 
 
 
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+ We also adopt the automatic MT-Bench evaluation framework based on GPT-4 proposed by lmsys to assess the performance of models.
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+ The WizardLM-2 8x22B even demonstrates highly competitive performance compared to the most advanced proprietary models.
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+ 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.
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+ <p align="center" width="100%">
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+ <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>
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+ </p>
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+ **Human Preferences Evaluation**
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+ 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.
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+ We report the win:loss rate without tie:
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+ - WizardLM-2 8x22B is just slightly falling behind GPT-4-1106-preview, and significantly stronger than Command R Plus and GPT4-0314.
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+ - WizardLM-2 70B is better than GPT4-0613, Mistral-Large, and Qwen1.5-72B-Chat.
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+ - WizardLM-2 7B is comparable with Qwen1.5-32B-Chat, and surpasses Qwen1.5-14B-Chat and Starling-LM-7B-beta.
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+ <p align="center" width="100%">
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+ <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>
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+ </p>
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+ ## Method Overview
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+ 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.
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+ <p align="center" width="100%">
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+ <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>
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+ </p>
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+ ## Usage
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+ ❗<b>Note for model system prompts usage:</b>
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+ <b>WizardLM-2</b> adopts the prompt format from <b>Vicuna</b> and supports **multi-turn** conversation. The prompt should be as following:
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+ ```
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+ A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful,
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+ detailed, and polite answers to the user's questions. USER: Hi ASSISTANT: Hello.</s>
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+ USER: Who are you? ASSISTANT: I am WizardLM.</s>......
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+ ```
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+ <b> Inference WizardLM-2 Demo Script</b>
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+ We provide a WizardLM-2 inference demo [code](https://github.com/nlpxucan/WizardLM/tree/main/demo) on our github