Update README.md

README.md

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 tags:
 - code
 - legal
----
+library_name: peft
+base_model: mistralai/Mistral-7B-v0.1
+---
+# Model Card for 911 Operator Assistant
+
+This model is a fine-tuned version of Mistral-7B-v0.1, designed to assist 911 operators in handling emergency calls professionally and efficiently.
+
+## Model Details
+
+### Model Description
+
+- **Developed by:** The model was developed using the dispatch.ipynb notebook
+- **Model type:** Fine-tuned Large Language Model
+- **Language(s) (NLP):** English
+- **License:** MIT
+- **Finetuned from model:** mistralai/Mistral-7B-v0.1
+
+## Uses
+
+### Direct Use
+
+This model is intended to be used as an assistant for 911 operators, helping them respond to emergency calls quickly and professionally.
+
+### Out-of-Scope Use
+
+This model should not be used as a replacement for trained 911 operators or emergency responders. It is meant to assist, not replace, human judgment in emergency situations.
+
+## Bias, Risks, and Limitations
+
+The model may have biases based on the training data used. It should not be relied upon for making critical decisions in emergency situations without human oversight.
+
+### Recommendations
+
+Users should always verify the model's outputs and use them in conjunction with established emergency response protocols.
+
+## How to Get Started with the Model
+
+Use the following code to initialize the model:
+
+```python
+from peft import PeftModel
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+BASE_MODEL = "mistralai/Mistral-7B-v0.1"
+LORA_CHECKPOINT = "./lora_adapters/checkpoint-200/"
+
+model, tokenizer = setup_model_and_tokenizer(BASE_MODEL)
+model = PeftModel.from_pretrained(model, LORA_CHECKPOINT)
+model.to(torch.device("xpu" if torch.xpu.is_available() else "cpu"))
+```
+
+## Training Details
+
+### Training Data
+
+The model was fine-tuned on a dataset of 911 call transcripts, using the "spikecodes/911-call-transcripts" dataset.
+
+### Training Procedure 
+
+#### Training Hyperparameters
+
+- **Batch size:** 4
+- **Learning rate:** 2e-5
+- **Epochs:** 7.62 (based on max_steps)
+- **Max steps:** 200
+- **Warmup steps:** 20
+- **Weight decay:** Not specified
+- **Gradient accumulation steps:** 4
+- **Training regime:** BFloat16 mixed precision
+
+#### Speeds, Sizes, Times
+
+- **Training time:** Approximately 800.64 seconds (13.34 minutes)
+
+## Evaluation
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+The model was evaluated on a validation set derived from the same dataset used for training.
+
+## Environmental Impact
+
+- **Hardware Type:** Intel(R) Data Center GPU Max 1100
+- **Hours used:** Approximately 0.22 hours (13.34 minutes)
+
+## Technical Specifications
+
+### Model Architecture and Objective
+
+The model uses the Mistral-7B architecture with LoRA (Low-Rank Adaptation) for efficient fine-tuning.
+
+### Compute Infrastructure
+
+#### Hardware
+
+Intel(R) Data Center GPU Max 1100
+
+#### Software
+
+- Python 3.9.18
+- PyTorch 2.1.0.post0+cxx11.abi
+- Transformers library
+- PEFT library
+- Intel Extension for PyTorch
+
+## Model Card Authors
+
+https://github.com/spikecodes
+
+## Model Card Contact
+
+For more information, please email me (using the contact button on my website: https://spike.codes) and refer to the repositories of the used libraries and base model.
+
+### Framework versions
+
+- PEFT 0.11.1