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dreamgen_-_WizardLM-2-7B-gguf
RichardErkhov/dreamgen_-_WizardLM-2-7B-gguf
dreamgen_-_WizardLM-2-7B-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
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At a glance
- Access
- Public
- Created
- May 20, 2024
- Updated
- May 20, 2024
- SHA
- 4fb5f4fd
Model card
From the Hugging Face model README
Quantization made by Richard Erkhov.
WizardLM-2-7B - GGUF
- Model creator: https://huggingface.co/dreamgen/
- Original model: https://huggingface.co/dreamgen/WizardLM-2-7B/
| Name | Quant method | Size |
|---|---|---|
| WizardLM-2-7B.Q2_K.gguf | Q2_K | 2.53GB |
| WizardLM-2-7B.IQ3_XS.gguf | IQ3_XS | 2.81GB |
| WizardLM-2-7B.IQ3_S.gguf | IQ3_S | 2.96GB |
| WizardLM-2-7B.Q3_K_S.gguf | Q3_K_S | 2.95GB |
| WizardLM-2-7B.IQ3_M.gguf | IQ3_M | 3.06GB |
| WizardLM-2-7B.Q3_K.gguf | Q3_K | 3.28GB |
| WizardLM-2-7B.Q3_K_M.gguf | Q3_K_M | 3.28GB |
| WizardLM-2-7B.Q3_K_L.gguf | Q3_K_L | 3.56GB |
| WizardLM-2-7B.IQ4_XS.gguf | IQ4_XS | 3.67GB |
| WizardLM-2-7B.Q4_0.gguf | Q4_0 | 3.83GB |
| WizardLM-2-7B.IQ4_NL.gguf | IQ4_NL | 3.87GB |
| WizardLM-2-7B.Q4_K_S.gguf | Q4_K_S | 3.86GB |
| WizardLM-2-7B.Q4_K.gguf | Q4_K | 4.07GB |
| WizardLM-2-7B.Q4_K_M.gguf | Q4_K_M | 4.07GB |
| WizardLM-2-7B.Q4_1.gguf | Q4_1 | 4.24GB |
| WizardLM-2-7B.Q5_0.gguf | Q5_0 | 4.65GB |
| WizardLM-2-7B.Q5_K_S.gguf | Q5_K_S | 4.65GB |
| WizardLM-2-7B.Q5_K.gguf | Q5_K | 4.78GB |
| WizardLM-2-7B.Q5_K_M.gguf | Q5_K_M | 4.78GB |
| WizardLM-2-7B.Q5_1.gguf | Q5_1 | 5.07GB |
| WizardLM-2-7B.Q6_K.gguf | Q6_K | 5.53GB |
| WizardLM-2-7B.Q8_0.gguf | Q8_0 | 7.17GB |
Original model description:
license: apache-2.0
<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 and upcoming paper.
Model Details
- Model name: WizardLM-2 7B
- Developed by: WizardLM@Microsoft AI
- Base model: mistralai/Mistral-7B-v0.1
- Parameters: 7B
- Language(s): Multilingual
- Blog: Introducing WizardLM-2
- Repository: 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.
Method Overview
We built a fully AI powered synthetic training system to train WizardLM-2 models, please refer to our blog 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 on our github.
- Created
- May 20, 2024
- Updated
- May 20, 2024