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--- |
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license: mit |
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datasets: |
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- avaliev/chat_doctor |
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language: |
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- en |
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base_model: |
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- meta-llama/Llama-3.2-3B-Instruct |
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pipeline_tag: text-generation |
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library_name: transformers |
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tags: |
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- Llama-3.2 |
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- 3B |
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- Llama-Doctor |
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- Instruct |
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- Llama-Cpp |
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- meta |
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- pytorch |
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- safetensors |
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--- |
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## Llama-Doctor-3.2-3B-Instruct Modelfile |
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| File Name { Chat Doctor } | Size | Description | Upload Status | |
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|----------------------------------------|------------|--------------------------------------|----------------| |
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| `.gitattributes` | 1.57 kB | Git attributes file | Uploaded | |
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| `README.md` | 263 Bytes | README file | Uploaded | |
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| `config.json` | 1.03 kB | Model configuration | Uploaded | |
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| `generation_config.json` | 248 Bytes | Generation configuration | Uploaded | |
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| `pytorch_model-00001-of-00002.bin` | 4.97 GB | PyTorch model file (part 1 of 2) | Uploaded (LFS) | |
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| `pytorch_model-00002-of-00002.bin` | 1.46 GB | PyTorch model file (part 2 of 2) | Uploaded (LFS) | |
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| `pytorch_model.bin.index.json` | 21.2 kB | Index for PyTorch model | Uploaded | |
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| `special_tokens_map.json` | 477 Bytes | Special tokens map | Uploaded | |
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| `tokenizer.json` | 17.2 MB | Tokenizer file | Uploaded (LFS) | |
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| `tokenizer_config.json` | 57.4 kB | Tokenizer configuration | Uploaded | |
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| Model Type | Size | Context Length | Link | |
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|------------|------|----------------|------| |
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| GGUF | 3B | - | [🤗 Llama-Doctor-3.2-3B-Instruct-GGUF](https://huggingface.co/prithivMLmods/Llama-Doctor-3.2-3B-Instruct-GGUF) | |
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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: |
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1. **Conversational AI**: Engage in dialogue, answering questions, or providing responses based on user instructions. |
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2. **Text Generation**: Generate content, summaries, explanations, or solutions to problems based on given prompts. |
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3. **Instruction Following**: Understand and execute instructions, potentially in complex or specialized domains like medical, technical, or academic fields. |
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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. |
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### Intended Applications: |
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- **Chatbots** for customer support or virtual assistants. |
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- **Medical Consultation Tools** for generating advice or answering medical queries (given its training on the **chat_doctor** dataset). |
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- **Content Creation** tools, helping generate text based on specific instructions. |
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- **Problem-solving Assistants** that offer explanations or answers to user queries, particularly in instructional contexts. |
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