Update README.md

README.md

@@ -1,20 +1,107 @@
 ---
-library_name: peft
+language:
+- en
+library_name: transformers
+license: other
 ---
-## Training procedure
 
+# Model Card for ContractAssist model
 
-The following `bitsandbytes` quantization config was used during training:
-- load_in_8bit: True
-- load_in_4bit: False
-- llm_int8_threshold: 6.0
-- llm_int8_skip_modules: None
-- llm_int8_enable_fp32_cpu_offload: False
-- llm_int8_has_fp16_weight: False
-- bnb_4bit_quant_type: fp4
-- bnb_4bit_use_double_quant: False
-- bnb_4bit_compute_dtype: float32
-### Framework versions
+<!-- 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.
 
 
-- PEFT 0.5.0.dev0
+
+# 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