Quant for 3.5

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+assets/fig_2.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -9,55 +9,91 @@ tags:
 - mpt
 - model-fusion
 library_name: transformers
-quantized_by: bartowski
 ---
+<p align="center" width="100%">
+</p>
 
-## Exllama v2 Quantizations of FuseLLM-7B
+<div id="top" align="center">
 
-Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.11">turboderp's ExLlamaV2 v0.0.11</a> for quantization.
+_**Knowledge Fusion of Large Language Models**_
 
-# The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)
+<h4> |<a href="https://arxiv.org/abs/2401.10491"> 📑 Paper </a> |
+<a href="https://huggingface.co/datasets/Wanfq/FuseLLM-7B"> 🤗 Model </a> |
+<a href="https://github.com/fanqiwan/FuseLLM"> 🐱 Github Repo </a> |
+</h4>
 
-Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
+<!-- **Authors:** -->
 
-Original model: https://huggingface.co/Wanfq/FuseLLM-7B
+_**Fanqi Wan<sup>†</sup>, Xinting Huang<sup>‡</sup>, Deng Cai<sup>‡</sup>, Xiaojun Quan<sup>†</sup>, Wei Bi<sup>‡</sup>, Shuming Shi<sup>‡</sup>**_
 
-No GQA - VRAM requirements will be higher
 
-| Branch                                                         | Bits | lm_head bits | Size (4k) | Size (16k) | Description |
-| -------------------------------------------------------------- | ---- | ------------ | --------- | ---------- | ----------- |
-| [8_0](https://huggingface.co/Bartowski/FuseLLM-7B-exl2/tree/8_0) | 8.0 | 8.0 | 9.4 GB | 15.6 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
-| [6_5](https://huggingface.co/Bartowski/FuseLLM-7B-exl2/tree/6_5) | 6.5  | 8.0 | 8.6 GB | 14.8 GB | Near unquantized performance at vastly reduced size, **recommended**. |
-| [5_0](https://huggingface.co/Bartowski/FuseLLM-7B-exl2/tree/5_0) | 5.0  | 6.0 | 7.2 GB | 13.4 GB | Slightly lower quality vs 6.5, but usable on 8GB cards with 4k context. |
-| [4_25](https://huggingface.co/Bartowski/FuseLLM-7B-exl2/tree/4_25) | 4.25 | 6.0 | 6.5 GB | 12.7 GB | GPTQ equivalent bits per weight. |
-| [3_5](https://huggingface.co/Bartowski/FuseLLM-7B-exl2/tree/3_5) | 3.5 | 6.0 | 5.9 GB | 12.1 GB | Lower quality, not recommended. |
+<!-- **Affiliations:** -->
 
-VRAM requirements listed for both 4k context and 16k context since without GQA the differences are massive (6.2 GB)
 
-## Download instructions
+_<sup>†</sup> Sun Yat-sen University,
+<sup>‡</sup> Tencent AI Lab_
 
-With git:
+</div>
 
-```shell
-git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/FuseLLM-7B-exl2 FuseLLM-7B-exl2-6_5
-```
 
-With huggingface hub (credit to TheBloke for instructions):
+## News
+- **Jan 22, 2024:** 🔥 We're excited to announce that the FuseLLM-7B, which is the fusion of [Llama-2-7B](https://huggingface.co/meta-llama/Llama-2-7b-hf), [OpenLLaMA-7B](https://huggingface.co/openlm-research/open_llama_7b_v2), and [MPT-7B](https://huggingface.co/mosaicml/mpt-7b), is now available on 🤗 [Huggingface Models](https://huggingface.co/Wanfq/FuseLLM-7B). Happy exploring!
 
-```shell
-pip3 install huggingface-hub
-```
+## Contents
 
-To download the `main` (only useful if you only care about measurement.json) branch to a folder called `FuseLLM-7B-exl2`:
+- [Overview](#overview)
+- [Model Release](#model-release)
+- [Citation](#citation)
+- [Acknowledgements](#acknowledgments)
 
-```shell
-mkdir FuseLLM-7B-exl2
-huggingface-cli download bartowski/FuseLLM-7B-exl2 --local-dir FuseLLM-7B-exl2 --local-dir-use-symlinks False
-```
+## Overview
+ 
+In this study, we explore the realm of knowledge fusion for LLMs to create a unified model that combines the capabilities and distinctive strengths of multiple structurally diverse LLMs. To achieve this, we introduce FuseLLM, which first leverages the generative distributions of these source LLMs to externalize both their collective knowledge and individual strengths, and subsequently transfer them to the target LLM through lightweight continual training.
+
+Compared with model ensemble which requires the parallel deployment of multiple LLMs or weight merging which is generally limited to LLMs with identical architectures, FuseLLM supports the fusion of multiple LLMs with **diverse architectures** by explicitly transferring their knowledge and capabilities to a **single** target LLM.
+
+<p align="center">
+    <img src="./assets/fig_1.png" width="95%"> <br>
+</p>
+
+
+## Model Release
+
+We release the FuseLLM-7B on [Huggingface Models](https://huggingface.co/models?sort=trending&search=FuseLLM), which is the fusion of three popular open-source LLMs that possess distinct architectures and functionalities: [Llama-2-7B](https://huggingface.co/meta-llama/Llama-2-7b-hf), [OpenLLaMA-7B](https://huggingface.co/openlm-research/open_llama_7b_v2), and [MPT-7B](https://huggingface.co/mosaicml/mpt-7b).
+
+Evaluations across three benchmarks, which consist of a total of **42** tasks spanning reasoning, commonsense, and code generation, confirm that the target model trained by our method outperforms each source LLM and the casual language model baseline in most tasks.
 
-To download from a different branch, add the `--revision` parameter:
+<p align="center">
+    <img src="./assets/fig_2.png" width="95%"> <br>
+</p>
 
-```shell
-mkdir FuseLLM-7B-exl2-6_5
-huggingface-cli download bartowski/FuseLLM-7B-exl2 --revision 6_5 --local-dir FuseLLM-7B-exl2-6_5 --local-dir-use-symlinks False
+To further illustrate the effectiveness of FuseLLM, we incorporate additional generative benchmarks related to knowledge-based question-answering, reading comprehension, content analysis, machine translation, and theorem application. The results highlight FuseLLM’s superiority over all source LLMs and the baseline.
+
+<p align="center">
+    <img src="./assets/fig_3.png" width="95%"> <br>
+</p>
+
+Since FuseLLM is also applicable to instruction-tuning models,  we assess the instruction-following performance on the Vicuna Benchmark using GPT-4 as an evaluator. The results demonstrate that FuseLLM surpasses each individual source instruction-tuning LLM and the baseline , achieving the best performance with GPT-4 judgment.
+
+<p align="center">
+    <img src="./assets/fig_4.png" width="50%"> <br>
+</p>
+
+
+## Citation
+
+If you find this work is relevant with your research or applications, please feel free to cite our work!
+```
+@misc{wan2024knowledge,
+      title={Knowledge Fusion of Large Language Models}, 
+      author={Fanqi Wan and Xinting Huang and Deng Cai and Xiaojun Quan and Wei Bi and Shuming Shi},
+      year={2024},
+      eprint={2401.10491},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
 ```
+
+## Acknowledgments
+
+This repo benefits from [Stanford-Alpaca](https://github.com/tatsu-lab/stanford_alpaca) and [Explore-Instruct](https://github.com/fanqiwan/Explore-Instruct). Thanks for their wonderful works!

config.json

@@ -0,0 +1,26 @@
+{
+  "_name_or_path": "meta-llama/Llama-2-7b-hf",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 2048,
+  "model_type": "llama",
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+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.31.0.dev0",
+  "use_cache": true,
+  "vocab_size": 32000
+}

generation_config.json

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+  "do_sample": true,
+  "eos_token_id": 2,
+  "pad_token_id": 0,
+  "temperature": 0.6,
+  "max_length": 4096,
+  "top_p": 0.9,
+  "transformers_version": "4.31.0.dev0"
+}

original_repo_url.txt

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+https://huggingface.co/Wanfq/FuseLLM-7B

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+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": {
+    "__type": "AddedToken",
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}