README.md

---
language:
- en
library_name: transformers
pipeline_tag: question-answering
tags:
- Finetuning
---
# Model Card for vishanoberoi/Llama-2-7b-chat-hf-finedtuned-to-GGUF

This model is a fine-tuned version of Llama-2-Chat-7b on company-specific question-answers data. It is designed for efficient performance while maintaining high-quality output, suitable for conversational AI applications.

## Model Details
It was finetuned using QLORA and PEFT. After fine-tuning, the adapters were merged with the base model and then quantized to GGUF.
- **Developed by:** Vishan Oberoi and Dev Chandan.
- **Model type:** Transformer-based Large Language Model
- **Language(s) (NLP):** English
- **License:** MIT
- **Finetuned from model:** https://huggingface.co/meta-llama/Llama-2-7b-chat-hf

### Model Sources

- **Repository:** [vishanoberoi/Llama-2-7b-chat-hf-finedtuned-to-GGUF](https://huggingface.co/vishanoberoi/Llama-2-7b-chat-hf-finedtuned-to-GGUF)
- **Links:**
  - LLaMA: [LLaMA Paper](https://arxiv.org/abs/2302.13971)
  - QLORA: [QLORA Paper](https://arxiv.org/abs/2305.14314)
  - llama.cpp: [llama.cpp Paper/Documentation](https://github.com/ggerganov/llama.cpp)

## Uses


This model is optimized for direct use in conversational AI, particularly for generating responses based on company-specific data. It can be utilized effectively in customer service bots, FAQ bots, and other applications where accurate and contextually relevant answers are required.
## Usage notebook
https://colab.research.google.com/drive/1885wYoXeRjVjJzHqL9YXJr5ZjUQOSI-w?authuser=4#scrollTo=TZIoajzYYkrg

#### Example with `ctransformers`:

```python
from ctransformers import AutoModelForCausalLM, AutoTokenizer

llm = AutoModelForCausalLM.from_pretrained("vishanoberoi/Llama-2-7b-chat-hf-finedtuned-to-GGUF", model_file="finetuned.gguf", model_type="llama", gpu_layers = 50, max_new_tokens = 2000, temperature = 0.2, top_k = 40, top_p = 0.6, context_length = 6000)

system_prompt = '''<<SYS>>
You are a useful bot
<</SYS>>

'''

user_prompt = "Tell me about your company"

# Combine system prompt with user prompt
full_prompt = f"{system_prompt}\n[INST]{user_prompt}[/INST]"

# Generate the response
response = llm(full_prompt)

# Print the response
print(response)