Quant for 6.5

README.md

@@ -4,68 +4,131 @@ datasets:
 - TIGER-Lab/SKGInstruct
 language:
 - en
-quantized_by: bartowski
-pipeline_tag: text-generation
 ---
+# 🏗️ StructLM: Towards Building Generalist Models for Structured Knowledge Grounding
 
-## Exllama v2 Quantizations of StructLM-13B
 
-Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.14">turboderp's ExLlamaV2 v0.0.14</a> for quantization.
 
-<b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b>
+Project Page: [https://tiger-ai-lab.github.io/StructLM/](https://tiger-ai-lab.github.io/StructLM/)
 
-Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
+Paper: [https://arxiv.org/pdf/2402.16671.pdf](https://arxiv.org/pdf/2402.16671.pdf)
 
-Original model: https://huggingface.co/TIGER-Lab/StructLM-13B
+Code: [https://github.com/TIGER-AI-Lab/StructLM](https://github.com/TIGER-AI-Lab/StructLM)
 
-No GQA - VRAM requirements will be higher
 
-| Branch | Bits | lm_head bits | VRAM (4k) | VRAM (16k) | Description |
-| ----- | ---- | ------- | ------ | ------ | ------------ |
-| [8_0](https://huggingface.co/bartowski/StructLM-13B-exl2/tree/8_0) | 8.0  | 8.0 | 16.8 GB | 26.2 GB | Near unquantized performance at vastly reduced size, **recommended**. |
-| [6_5](https://huggingface.co/bartowski/StructLM-13B-exl2/tree/6_5) | 6.5  | 8.0 | 14.4 GB | 24.0 GB | Near unquantized performance at vastly reduced size, **recommended**. |
-| [5_0](https://huggingface.co/bartowski/StructLM-13B-exl2/tree/5_0) | 5.0  | 6.0 | 12.1 GB | 21.7 GB | Slightly lower perplexity vs 6.5, can fit in 12 GB card with even lower context. |
-| [4_25](https://huggingface.co/bartowski/StructLM-13B-exl2/tree/4_25) | 4.25 | 6.0 | 10.9 GB | 20.5 GB | GPTQ equivalent bits per weight. |
-| [3_75](https://huggingface.co/bartowski/StructLM-13B-exl2/tree/3_75) | 3.75  | 6.0 | 10.1 GB | 19.7 GB | Lower quality but still generally usable. |
-| [3_0](https://huggingface.co/bartowski/StructLM-13B-exl2/tree/3_0) | 3.0  | 6.0 |  9.1 GB | 18.7 GB | Very low quality, not recommended unless you have to. |
+![Alt text](https://raw.githubusercontent.com/TIGER-AI-Lab/StructLM/gh-pages/static/images/thumbnail.drawio.png)
 
-VRAM requirements listed for both 4k context and 16k context since without GQA the differences are massive (9.6 GB)
 
-## Download instructions
+## Introduction
+StructLM, is a series of open-source large language models (LLMs) finetuned for structured knowledge grounding (SKG) tasks. We release 3 models:
 
-With git:
+ 7B  | [StructLM-7B](https://huggingface.co/TIGER-Lab/StructLM-7B)   
+ 
+ 13B | [StructLM-13B](https://huggingface.co/TIGER-Lab/StructLM-13B) 
+ 
+ 34B | [StructLM-34B](https://huggingface.co/TIGER-Lab/StructLM-34B) 
+
+
+## Training Data
+These models are trained on 🤗 [SKGInstruct Dataset](https://huggingface.co/datasets/TIGER-Lab/SKGInstruct), an instruction-tuning dataset containing mixture of 19 SKG tasks combined with 🤗 [SlimOrca](https://huggingface.co/datasets/Open-Orca/SlimOrca).  Check out the dataset card for more details.
+
+
+## Training Procedure
+The models are fine-tuned with CodeLlama-Instruct-hf models as base models. Each model is trained for 3 epochs, and the best checkpoint is selected.
+
+## Evaluation
+Here are a subset of model evaluation results:
+
+### Held in
+
+| **Model**             	| **ToTTo** 	| **GrailQA**  	| **CompWebQ** 	| **MMQA** 	| **Feverous** 	| **Spider**  	| **TabFact**  	| **Dart**  	|
+|-----------------------|--------------|----------|----------|----------|----------|----------|----------|----------|
+| **StructLM-7B**        	| 49.4          	| 80.4     	| 78.3  	| 85.2     	| 84.4     	| 72.4     	| 80.8      	| 62.2     	|
+| **StructLM-13B**  	| 49.3          	| 79.2    	| 80.4  	| 86.0     	| 85.0     	| 74.1     	| 84.7     	| 61.4      	| 
+| **StructLM-34B**       	| 50.2          	| 82.2     	| 81.9 	| 88.1     	| 85.7     	| 74.6     	| 86.6     	| 61.8     	| 
 
-```shell
-git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/StructLM-13B-exl2 StructLM-13B-exl2-6_5
-```
 
-With huggingface hub (credit to TheBloke for instructions):
+### Held out
+| **Model**             	| **BIRD** 	| **InfoTabs**  	| **FinQA** 	| **SQA** 	|
+|-----------------------|--------------|----------|----------|----------|
+| **StructLM-7B**        	| 22.3          	| 55.3     	| 27.3     	| 49.7     	|
+| **StructLM-13B**  	| 22.8          	| 58.1     	| 25.6      	| 36.1     	| 
+| **StructLM-34B**       	| 24.7          	| 61.8     	| 36.2     	| 44.2     	| 
 
-```shell
-pip3 install huggingface-hub
+
+## Usage
+You can use the models through Huggingface's Transformers library. 
+Check our Github repo for the evaluation code: [https://github.com/TIGER-AI-Lab/StructLM](https://github.com/TIGER-AI-Lab/StructLM)
+
+
+## Prompt Format
+
+\*\*\***IMPORTANT**\*\*\*
+
+**For this 13B model, the prompt format (different from 7B) is**
+```
+[INST] [INST] <<SYS>>
+You are an AI assistant that specializes in analyzing and reasoning over structured information. You will be given a task, optionally with some structured knowledge input. Your answer must strictly adhere to the output format, if specified.
+<</SYS>>
+{instruction} [/INST] [/INST]
 ```
 
-To download the `main` (only useful if you only care about measurement.json) branch to a folder called `StructLM-13B-exl2`:
 
-```shell
-mkdir StructLM-13B-exl2
-huggingface-cli download bartowski/StructLM-13B-exl2 --local-dir StructLM-13B-exl2 --local-dir-use-symlinks False
+To see concrete examples of this linearization, you can directly reference the 🤗 [SKGInstruct Dataset](https://huggingface.co/datasets/TIGER-Lab/SKGInstruct) (coming soon).
+We will provide code for linearizing this data shortly.
+
+
+A few examples:
+
+**tabular data**
+```
+col : day | kilometers row 1 : tuesday | 0 row 2 : wednesday | 0 row 3 : thursday | 4 row 4 : friday | 0 row 5 : saturday | 0
+```
+
+**knowledge triples (dart)**
+```
+Hawaii Five-O : notes : Episode: The Flight of the Jewels | [TABLECONTEXT] : [title] : Jeff Daniels | [TABLECONTEXT] : title : Hawaii Five-O
 ```
 
-To download from a different branch, add the `--revision` parameter:
+**knowledge graph schema (grailqa)**
+```
+top antiquark: m.094nrqp | physics.particle_antiparticle.self_antiparticle physics.particle_family physics.particle.antiparticle physics.particle_family.subclasses physics.subatomic_particle_generation physics.particle_family.particles physics.particle common.image.appears_in_topic_gallery physics.subatomic_particle_generation.particles physics.particle.family physics.particle_family.parent_class physics.particle_antiparticle physics.particle_antiparticle.particle physics.particle.generation
+```
 
-Linux:
+**example input**
 
-```shell
-mkdir StructLM-13B-exl2-6_5
-huggingface-cli download bartowski/StructLM-13B-exl2 --revision 6_5 --local-dir StructLM-13B-exl2-6_5 --local-dir-use-symlinks False
 ```
+[INST] [INST] <<SYS>>
+You are an AI assistant that specializes in analyzing and reasoning over structured information. You will be given a task, optionally with some structured knowledge input. Your answer must strictly adhere to the output format, if specified.
+<</SYS>>
+
+Use the information in the following table to solve the problem, choose between the choices if they are provided. table:
 
-Windows (which apparently doesn't like _ in folders sometimes?):
+col : day | kilometers row 1 : tuesday | 0 row 2 : wednesday | 0 row 3 : thursday | 4 row 4 : friday | 0 row 5 : saturday | 0
 
-```shell
-mkdir StructLM-13B-exl2-6.5
-huggingface-cli download bartowski/StructLM-13B-exl2 --revision 6_5 --local-dir StructLM-13B-exl2-6.5 --local-dir-use-symlinks False
+
+question:
+
+Allie kept track of how many kilometers she walked during the past 5 days. What is the range of the numbers? [/INST] [/INST]
 ```
 
-Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
+## Intended Uses
+These models are trained for research purposes. They are designed to be proficient in interpreting linearized structured input. Downstream uses can potentially include various applications requiring the interpretation of structured data.
+
+## Limitations
+While we've tried to build an SKG-specialized model capable of generalizing, we have shown that this is a challenging domain, and it may lack performance characteristics that allow it to be directly used in chat or other applications.
+
+
+## Citation
+If you use the models, data, or code from this project, please cite the original paper:
+
+```
+@misc{zhuang2024structlm,
+      title={StructLM: Towards Building Generalist Models for Structured Knowledge Grounding}, 
+      author={Alex Zhuang and Ge Zhang and Tianyu Zheng and Xinrun Du and Junjie Wang and Weiming Ren and Stephen W. Huang and Jie Fu and Xiang Yue and Wenhu Chen},
+      year={2024},
+      eprint={2402.16671},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```

config.json

@@ -0,0 +1,27 @@
+{
+  "_name_or_path": "/ML-A800/models/CodeLlama-13b-hf",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 13824,
+  "max_position_embeddings": 16384,
+  "model_type": "llama",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 40,
+  "num_key_value_heads": 40,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.34.1",
+  "use_cache": false,
+  "vocab_size": 32016
+}

generation_config.json

@@ -0,0 +1,6 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.34.1"
+}

original_repo_url.txt

@@ -0,0 +1 @@
+https://huggingface.co/TIGER-Lab/StructLM-13B

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+    "▁<EOT>"
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+      "content": "▁<EOT>",
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+    "▁<EOT>"
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+  "eot_token": "▁<EOT>",
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