File size: 3,016 Bytes
7493b45 902c773 7493b45 902c773 7493b45 902c773 7493b45 902c773 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 |
---
language:
- en
library_name: transformers
license: other
---
# Model Card for ContractAssist model
<!-- Provide a quick summary of what the model is/does. [Optional] -->
Intruction tuned model using FlanT5-XXL on data generated via ChatGPT for generating and/or modifying the Legal Clauses.
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is/does. -->
- **Developed by:** Jaykumar Kasundra, Shreyans Dhankhar
- **Model type:** Language model
- **Language(s) (NLP):** en
- **License:** other
- **Resources for more information:**
- [Associated Paper](<Add Link>)
# Uses
</details>
### Running the model on a GPU using different precisions
#### FP16
<details>
<summary> Click to expand </summary>
```python
# pip install accelerate peft bitsandbytes
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from peft import PeftModel,PeftConfig
tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-xxl")
model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xxl", device_map="auto", torch_dtype=torch.float16)
input_text = "translate English to German: How old are you?"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
```
</details>
#### INT8
<details>
<summary> Click to expand </summary>
```python
# pip install bitsandbytes accelerate
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-xxl")
model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xxl", device_map="auto", load_in_8bit=True)
input_text = "translate English to German: How old are you?"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
```
</details>
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
## Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
The model can directly be used to generate/modify legal clauses and help assist in drafting contracts. It likely works best on english language.
## Compute Infrastructure
Amazon SageMaker Training Job.
### Hardware
1 x 24GB NVIDIA A10G
### Software
Transformers, PEFT, BitsandBytes
# Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
<Coming Soon>
# Model Card Authors
<!-- This section provides another layer of transparency and accountability. Whose views is this model card representing? How many voices were included in its construction? Etc. -->
Jaykumar Kasundra, Shreyans Dhankhar
|