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README.md

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+# 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)
+  - GGUF: [GGUF Paper](https://arxiv.org/abs/abs_link)
+  - 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)
+
+