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---
license: mit
datasets:
- avaliev/chat_doctor
language:
- en
base_model:
- meta-llama/Llama-3.2-3B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- Llama-3.2
- 3B
- Llama-Doctor
- Instruct
- Llama-Cpp
- meta
- pytorch
- safetensors
---
## Llama-Doctor-3.2-3B-Instruct Modelfile
| File Name { Chat Doctor } | Size | Description | Upload Status |
|----------------------------------------|------------|--------------------------------------|----------------|
| `.gitattributes` | 1.57 kB | Git attributes file | Uploaded |
| `README.md` | 263 Bytes | README file | Uploaded |
| `config.json` | 1.03 kB | Model configuration | Uploaded |
| `generation_config.json` | 248 Bytes | Generation configuration | Uploaded |
| `pytorch_model-00001-of-00002.bin` | 4.97 GB | PyTorch model file (part 1 of 2) | Uploaded (LFS) |
| `pytorch_model-00002-of-00002.bin` | 1.46 GB | PyTorch model file (part 2 of 2) | Uploaded (LFS) |
| `pytorch_model.bin.index.json` | 21.2 kB | Index for PyTorch model | Uploaded |
| `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 |
| Model Type | Size | Context Length | Link |
|------------|------|----------------|------|
| GGUF | 3B | - | [🤗 Llama-Doctor-3.2-3B-Instruct-GGUF](https://huggingface.co/prithivMLmods/Llama-Doctor-3.2-3B-Instruct-GGUF) |
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.
### 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.
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