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
- Links:
- LLaMA: LLaMA Paper
- QLORA: QLORA Paper
- llama.cpp: llama.cpp Paper/Documentation
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
Example with ctransformers:
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)
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