Quant for 8.0
Browse files- README.md +90 -41
- config.json +37 -0
- generation_config.json +6 -0
- model.safetensors.index.json +298 -0
- output.safetensors +3 -0
- special_tokens_map.json +24 -0
- tokenizer.model +3 -0
- tokenizer_config.json +43 -0
README.md
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---
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license: apache-2.0
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quantized_by: bartowski
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pipeline_tag: text-generation
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---
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## Exllama v2 Quantizations of WizardLM-2-7B
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Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.18">turboderp's ExLlamaV2 v0.0.18</a> for quantization.
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<b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b>
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Original model: https://huggingface.co/microsoft/WizardLM-2-7B
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## Prompt format
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<</SYS>>
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| ----- | ---- | ------- | ------ | ------ | ------ | ------------ |
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| [8_0](https://huggingface.co/bartowski/WizardLM-2-7B-exl2/tree/8_0) | 8.0 | 8.0 | 8.4 GB | 9.8 GB | 11.8 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
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| [6_5](https://huggingface.co/bartowski/WizardLM-2-7B-exl2/tree/6_5) | 6.5 | 8.0 | 7.2 GB | 8.6 GB | 10.6 GB | Very similar to 8.0, good tradeoff of size vs performance, **recommended**. |
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| [5_0](https://huggingface.co/bartowski/WizardLM-2-7B-exl2/tree/5_0) | 5.0 | 6.0 | 6.0 GB | 7.4 GB | 9.4 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. |
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| [4_25](https://huggingface.co/bartowski/WizardLM-2-7B-exl2/tree/4_25) | 4.25 | 6.0 | 5.3 GB | 6.7 GB | 8.7 GB | GPTQ equivalent bits per weight, slightly higher quality. |
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| [3_5](https://huggingface.co/bartowski/WizardLM-2-7B-exl2/tree/3_5) | 3.5 | 6.0 | 4.7 GB | 6.1 GB | 8.1 GB | Lower quality, only use if you have to. |
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With git:
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```shell
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git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/WizardLM-2-7B-exl2 WizardLM-2-7B-exl2-6_5
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```
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```shell
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pip3 install huggingface-hub
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```
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Linux:
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```shell
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huggingface-cli download bartowski/WizardLM-2-7B-exl2 --revision 6_5 --local-dir WizardLM-2-7B-exl2-6_5 --local-dir-use-symlinks False
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```
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Windows (which apparently doesn't like _ in folders sometimes?):
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```
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---
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license: apache-2.0
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---
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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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🤗 <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>
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</p>
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<p align="center">
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👋 Join our <a href="https://discord.gg/VZjjHtWrKs" target="_blank">Discord</a>
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</p>
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## News 🔥🔥🔥 [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.
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config.json
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{
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"_name_or_path": "",
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"architectures": [
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"MistralForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 32768,
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"model_type": "mistral",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"rms_norm_eps": 1e-05,
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"rope_theta": 10000.0,
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"sliding_window": 4096,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.36.2",
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"use_cache": false,
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"vocab_size": 32000,
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"quantization_config": {
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"quant_method": "exl2",
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"version": "0.0.18",
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"bits": 8.0,
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"head_bits": 8,
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"calibration": {
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"rows": 100,
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"length": 2048,
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"dataset": "(default)"
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}
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}
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"transformers_version": "4.36.2"
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}
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model.safetensors.index.json
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| 22 |
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"content": "</s>",
|
| 23 |
+
"lstrip": false,
|
| 24 |
+
"normalized": false,
|
| 25 |
+
"rstrip": false,
|
| 26 |
+
"single_word": false,
|
| 27 |
+
"special": true
|
| 28 |
+
}
|
| 29 |
+
},
|
| 30 |
+
"additional_special_tokens": [],
|
| 31 |
+
"bos_token": "<s>",
|
| 32 |
+
"clean_up_tokenization_spaces": false,
|
| 33 |
+
"eos_token": "</s>",
|
| 34 |
+
"legacy": true,
|
| 35 |
+
"model_max_length": 8192,
|
| 36 |
+
"pad_token": "<unk>",
|
| 37 |
+
"padding_side": "right",
|
| 38 |
+
"sp_model_kwargs": {},
|
| 39 |
+
"spaces_between_special_tokens": false,
|
| 40 |
+
"tokenizer_class": "LlamaTokenizer",
|
| 41 |
+
"unk_token": "<unk>",
|
| 42 |
+
"use_default_system_prompt": false
|
| 43 |
+
}
|