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---
license: cc-by-nc-4.0
language:
- en
---
# Jellyfish-8B
<!-- Provide a quick summary of what the model is/does. -->
<!--
<img src="https://i.imgur.com/d8Bl04i.png" alt="PicToModel" width="330"/>
-->
<img src="https://i.imgur.com/E1vqCIw.png" alt="PicToModel" width="330"/>
Jellyfish models with other sizes are available here:
[Jellyfish-7B](https://huggingface.co/NECOUDBFM/Jellyfish-7B)
[Jellyfish-13B](https://huggingface.co/NECOUDBFM/Jellyfish-13B)
## Model Details
Jellyfish-8B is a large language model equipped with 8 billion parameters.
We fine-tuned the [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) model using a subset of the [Jellyfish-Instruct](https://huggingface.co/datasets/NECOUDBFM/Jellyfish-Instruct) dataset.
<!-- Jellyfish-7B vs GPT-3.5-turbo wining rate by GPT4 evaluation is 56.36%. -->
More details about the model can be found in the [Jellyfish paper](https://arxiv.org/abs/2312.01678).
- **Developed by:** Haochen Zhang, Yuyang Dong, Chuan Xiao, Masafumi Oyamada
- **Contact: dongyuyang@nec.com**
- **Funded by:** NEC Corporation, Osaka University
- **Language(s) (NLP):** English
- **License:** Non-Commercial Creative Commons license (CC BY-NC-4.0)
- **Finetuned from model:** [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
## Citation
If you find our work useful, please give us credit by citing:
```
@article{zhang2023jellyfish,
title={Jellyfish: A Large Language Model for Data Preprocessing},
author={Zhang, Haochen and Dong, Yuyang and Xiao, Chuan and Oyamada, Masafumi},
journal={arXiv preprint arXiv:2312.01678},
year={2023}
}
```
## Performance on seen tasks
| Task | Type | Dataset | Non-LLM SoTA<sup>1</sup> | GPT-3.5<sup>2</sup> | GPT-4<sup>2</sup> | GPT-4o | Table-GPT | Jellyfish-7B | Jellyfish-8B | Jellyfish-13B |
|-----------------|--------|-------------------|-----------------|--------|--------|--------|-----------|--------------|--------------|---------------|
| Error Detection | Seen | Adult | *99.10* | 99.10 | 92.01 | 83.58 | -- | 77.40 | 73.74 | **99.33** |
| Error Detection | Seen | Hospital | 94.40 | **97.80** | 90.74 | 44.76 | -- | 94.51 | 93.40 | *95.59* |
| Error Detection | Unseen | Flights | 81.00 | -- | **83.48** | 66.01 | -- | 69.15 | 66.21 | *82.52* |
| Error Detection | Unseen | Rayyan | 79.00 | -- | *81.95* | 68.53 | -- | 75.07 | 81.06 | **90.65** |
| Data Imputation | Seen | Buy | 96.50 | 98.50 | **100** | **100** | -- | 98.46 | 98.46 | **100** |
| Data Imputation | Seen | Restaurant | 77.20 | 88.40 | **97.67** | 90.70 | -- | 89.53 | 87.21 | 89.53 |
| Data Imputation | Unseen | Flipkart | 68.00 | -- | **89.94** | 83.20 | -- | 87.14 | *87.48* | 81.68 |
| Data Imputation | Unseen | Phone | 86.70 | -- | **90.79** | 86.78 | -- | 86.52 | 85.68 | *87.21* |
| Schema Matching | Seen | MIMIC-III | 20.00 | -- | 40.00 | 29.41 | -- | **53.33** | *45.45* | 40.00 |
| Schema Matching | Seen | Synthea | 38.50 | 45.20 | **66.67** | 6.56 | -- | 55.56 | 47.06 | 56.00 |
| Schema Matching | Unseen | CMS | *50.00* | -- | 19.35 | 22.22 | -- | 42.86 | 38.10 | **59.29** |
| Entity Matching | Seen | Amazon-Google | 75.58 | 63.50 | 74.21 | 70.91 | 70.10 | **81.69** | *81.42* | 81.34 |
| Entity Matching | Seen | Beer | 94.37 | **100** | **100** | 90.32 | 96.30 | **100.00** | **100.00** | 96.77 |
| Entity Matching | Seen | DBLP-ACM | **98.99** | 96.60 | 97.44 | 95.87 | 93.80 | 98.65 | 98.77 | *98.98* |
| Entity Matching | Seen | DBLP-GoogleScholar| *95.70* | 83.80 | 91.87 | 90.45 | 92.40 | 94.88 | 95.03 | **98.51** |
| Entity Matching | Seen | Fodors-Zagats | **100** | **100** | **100** | 93.62 | **100** | **100** | **100** | **100** |
| Entity Matching | Seen | iTunes-Amazon | 97.06 | *98.20*| **100** | 98.18 | 94.30 | 96.30 | 96.30 | 98.11 |
| Entity Matching | Unseen | Abt-Buy | 89.33 | -- | **92.77** | 78.73 | -- | 86.06 | 88.84 | *89.58* |
| Entity Matching | Unseen | Walmart-Amazon | 86.89 | 87.00 | **90.27** | 79.19 | 82.40 | 84.91 | 85.24 | *89.42* |
| Avg | | | 80.44 | - | *84.17* | 72.58 | - | 82.74 | 81.55 | **86.02** |
_For GPT-3.5 and GPT-4, we used the few-shot approach on all datasets. For Jellyfish models, the few-shot approach is disabled on seen datasets and enabled on unseen datasets._
_Accuracy as the metric for data imputation and the F1 score for other tasks._
1.
[HoloDetect](https://arxiv.org/abs/1904.02285) for Error Detection seen datasets
[RAHA](https://dl.acm.org/doi/10.1145/3299869.3324956) for Error Detection unseen datasets
[IPM](https://ieeexplore.ieee.org/document/9458712) for Data Imputation
[SMAT](https://www.researchgate.net/publication/353920530_SMAT_An_Attention-Based_Deep_Learning_Solution_to_the_Automation_of_Schema_Matching) for Schema Matching
[Ditto](https://arxiv.org/abs/2004.00584) for Entity Matching
3.
[Large Language Models as Data Preprocessors](https://arxiv.org/abs/2308.16361)
## Performance on unseen tasks
### Column Type Annotation
| Dataset | RoBERTa (159 shots)<sup>1</sup> | GPT-3.5<sup>1</sup> | GPT-4 | GPT-4o | Jellyfish-7B | Jellyfish-8B | Jellyfish-13B |
|--------|-----------------|--------|--------|--------|--------------|--------------|---------------|
| SOTAB | 79.20 | 89.47 | 91.55 | 65.05 | 83 | 76.33 | 82 |
_Few-shot is disabled for Jellyfish models._
1. Results from [Column Type Annotation using ChatGPT](https://arxiv.org/abs/2306.00745)
### Attribute Value Extraction
| Dataset |Stable Beluga 2 70B<sup>1</sup> | SOLAR 70B<sup>1</sup> | GPT-3.5<sup>1</sup> | GPT-4 <sup>1</sup>| GPT-4o | Jellyfish-7B | Jellyfish-8B | Jellyfish-13B |
| ---- | ---- | ---- | ---- | ---- | ---- | ----| ----| ----|
| AE-110k | 52.10 | 49.20 | 61.30 | 55.50 | 55.77 | 56.09 |59.55 | 58.12 |
| OA-Mine | 50.80 | 55.20 | 62.70 | 68.90 | 60.20 | 51.98 | 59.22 | 55.96 |
_Few-shot is disabled for Jellyfish models._
1. Results from [Product Attribute Value Extraction using Large Language Models](https://arxiv.org/abs/2310.12537)
## Prompt Template
```
<|start_header_id|>system<|end_header_id|>{system message}<|eot_id|>
<|start_header_id|>user<|end_header_id|>{prompt}<|eot_id|>
<|start_header_id|>assistant<|end_header_id|>
```
## Training Details
### Training Method
We used LoRA to speed up the training process, targeting the q_proj, k_proj, v_proj, and o_proj modules.
## Uses
To accelerate the inference, we strongly recommend running Jellyfish using [vLLM](https://github.com/vllm-project/vllm).
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Python Script
We provide two simple Python code examples for inference using the Jellyfish model.
#### Using Transformers and Torch Modules
<div style="height: auto; max-height: 400px; overflow-y: scroll;">
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
import torch
if torch.cuda.is_available():
device = "cuda"
else:
device = "cpu"
# Model will be automatically downloaded from HuggingFace model hub if not cached.
# Model files will be cached in "~/.cache/huggingface/hub/models--NECOUDBFM--Jellyfish/" by default.
# You can also download the model manually and replace the model name with the path to the model files.
model = AutoModelForCausalLM.from_pretrained(
"NECOUDBFM/Jellyfish",
torch_dtype=torch.float16,
device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("NECOUDBFM/Jellyfish")
system_message = "You are an AI assistant that follows instruction extremely well. Help as much as you can."
# You need to define the user_message variable based on the task and the data you want to test on.
user_message = "Hello, world."
prompt = f"<|start_header_id|>system<|end_header_id|>{system message}<|eot_id|>\n<|start_header_id|>user<|end_header_id|>{user_message}<|eot_id|>\n<|start_header_id|>assistant<|end_header_id|>"
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].to(device)
# You can modify the sampling parameters according to your needs.
generation_config = GenerationConfig(
do_samples=True,
temperature=0.35,
top_p=0.9,
)
with torch.no_grad():
generation_output = model.generate(
input_ids=input_ids,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=1024,
pad_token_id=tokenizer.eos_token_id,
repetition_penalty=1.15,
)
output = generation_output[0]
response = tokenizer.decode(
output[:, input_ids.shape[-1] :][0], skip_special_tokens=True
).strip()
print(response)
```
</div>
#### Using vLLM
<div style="height: auto; max-height: 400px; overflow-y: scroll;">
```python
from vllm import LLM, SamplingParams
# To use vllm for inference, you need to download the model files either using HuggingFace model hub or manually.
# You should modify the path to the model according to your local environment.
path_to_model = (
"/workspace/models/Jellyfish"
)
model = LLM(model=path_to_model)
# You can modify the sampling parameters according to your needs.
# Caution: The stop parameter should not be changed.
sampling_params = SamplingParams(
temperature=0.35,
top_p=0.9,
max_tokens=1024,
stop=["<|eot_id|>"],
)
system_message = "You are an AI assistant that follows instruction extremely well. Help as much as you can."
# You need to define the user_message variable based on the task and the data you want to test on.
user_message = "Hello, world."
prompt = ff"<|start_header_id|>system<|end_header_id|>{system message}<|eot_id|>\n<|start_header_id|>user<|end_header_id|>{user_message}<|eot_id|>\n<|start_header_id|>assistant<|end_header_id|>"
outputs = model.generate(prompt, sampling_params)
response = outputs[0].outputs[0].text.strip()
print(response)
```
</div>
## Prompts
We provide the prompts used for both fine-tuning and inference.
You can structure your data according to these prompts.
### System Message
```
You are an AI assistant that follows instruction extremely well.
User will give you a question. Your task is to answer as faithfully as you can.
```
### For Error Detection
_There are two forms of the error detection task.
In the first form, a complete record row is provided, and the task is to determine if a specific value is erroneous.
In the second form, only the value of a specific attribute is given, and the decision about its correctness is based solely on the attribute's name and value.
The subsequent prompt examples pertain to these two forms, respectively._
```
Your task is to determine if there is an error in the value of a specific attribute within the whole record provided.
The attributes may include {attribute 1}, {attribute 2}, ...
Errors may include, but are not limited to, spelling errors, inconsistencies, or values that don't make sense given the context of the whole record.
Record [{attribute 1}: {attribute 1 value}, {attribute 2}: {attribute 2 value}, ...]
Attribute for Verification: [{attribute X}: {attribute X value}]
Question: Is there an error in the value of {attribute X}? Choose your answer from: [Yes, No].
```
```
Your task is to determine if there is an error in the value of a specific attribute.
The attributes may belong to a {keyword} record and could be one of the following: {attribute 1}, {attribute 2}, ...
Errors can include, but are not limited to, spelling errors, inconsistencies, or values that don't make sense for that attribute.
Note: Missing values (N/A or \"nan\") are not considered errors.
Attribute for Verification: [{attribute X}: {attribute X value}]
Question: Is there an error in the value of {attribute X}? Choose your answer from: [Yes, No].
```
### For Data Imputation
```
You are presented with a {keyword} record that is missing a specific attribute: {attribute X}.
Your task is to deduce or infer the value of {attribute X} using the available information in the record.
You may be provided with fields like {attribute 1}, {attribute 2}, ... to help you in the inference.
Record: [{attribute 1}: {attribute 1 value}, {attribute 2}: {attribute 2 value}, ...]
Based on the provided record, what would you infer is the value for the missing attribute {attribute X}?
Answer only the value of {attribute X}.
```
### For Schema Matching
```
Your task is to determine if the two attributes (columns) are semantically equivalent in the context of merging two tables.
Each attribute will be provided by its name and a brief description.
Your goal is to assess if they refer to the same information based on these names and descriptions provided.
Attribute A is [name: {value of name}, description: {value of description}].
Attribute B is [name: {value of name}, description: {value of description}].
Are Attribute A and Attribute B semantically equivalent? Choose your answer from: [Yes, No].
```
### For Entity Matching
```
You are tasked with determining whether two records listed below are the same based on the information provided.
Carefully compare the {attribute 1}, {attribute 2}... for each record before making your decision.
Note that missing values (N/A or \"nan\") should not be used as a basis for your decision.
Record A: [{attribute 1}: {attribute 1 value}, {attribute 2}: {attribute 2 value}, ...]
Record B: [{attribute 1}: {attribute 1 value}, {attribute 2}: {attribute 2 value}, ...]
Are record A and record B the same entity? Choose your answer from: [Yes, No].
```
### For Column Type Annotation
We follow the prompt in [Column Type Annotation using ChatGPT](https://arxiv.org/abs/2306.00745) (text+inst+2-step).
### For Attribute Value Extraction
We follow the prompt in [Product Attribute Value Extraction using Large Language Models](https://arxiv.org/abs/2310.12537) (textual, w/o examples).

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"architectures": [
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"eos_token_id": 128001,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 8192,
"mlp_bias": false,
"model_type": "llama",
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"num_key_value_heads": 8,
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"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.41.2",
"use_cache": true,
"vocab_size": 128256
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{
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