| # 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 fined 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) | |