---
base_model: mistralai/Mistral-7B-v0.1
library_name: peft
pipeline_tag: question-answering
---

# Model Card for Model ID

A first version of mental health chatbot fine tuned on 1900+ samples



## Model Details

### Model Description

Mental Health has become an increasingly severe issue in the young generation. To combat it, we developed Mindmate a mental health chatbot with dual mode setting, a professional
mode simulating a therapist and a friend mode which will act to console and help you(implemented using gemini api)



- **Developed by:** pointbreak3000(Aditya Deshmukh)
- **Funded by [optional]:** -
- **Shared by [optional]:** -
- **Model type:** LLM
- **Language(s) (NLP):** English
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** Mistral 7B - v- 0.1

### Model Sources [optional]

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## Uses

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### Direct Use

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")

model = PeftModel.from_pretrained(base_model, "pointbreak3000/mistral-mental-health-lora")

prompt = "I'm feeling stressed and anxious lately. What should I do?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))




### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**APA:**

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## Glossary [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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### Framework versions

- PEFT 0.9.0