Update README.md

README.md

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 | `special_tokens_map.json`              | 477 Bytes  | Special tokens map                   | Uploaded       |
 | `tokenizer.json`                       | 17.2 MB    | Tokenizer file                       | Uploaded (LFS) |
 | `tokenizer_config.json`                | 57.4 kB    | Tokenizer configuration              | Uploaded       |
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+The **Llama-Doctor-3.2-3B-Instruct** model is designed for **text generation** tasks, particularly in contexts where instruction-following capabilities are needed. This model is a fine-tuned version of the base **Llama-3.2-3B-Instruct** model and is optimized for understanding and responding to user-provided instructions or prompts. The model has been trained on a specialized dataset, **avaliev/chat_doctor**, to enhance its performance in providing conversational or advisory responses, especially in medical or technical fields.
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+### Key Use Cases:
+1. **Conversational AI**: Engage in dialogue, answering questions, or providing responses based on user instructions.
+2. **Text Generation**: Generate content, summaries, explanations, or solutions to problems based on given prompts.
+3. **Instruction Following**: Understand and execute instructions, potentially in complex or specialized domains like medical, technical, or academic fields.
+
+The model leverages a **PyTorch-based architecture** and comes with various files such as configuration files, tokenizer files, and special tokens maps to facilitate smooth deployment and interaction.
+
+### Intended Applications:
+- **Chatbots** for customer support or virtual assistants.
+- **Medical Consultation Tools** for generating advice or answering medical queries (given its training on the **chat_doctor** dataset).
+- **Content Creation** tools, helping generate text based on specific instructions.
+- **Problem-solving Assistants** that offer explanations or answers to user queries, particularly in instructional contexts.