Quantization made by Richard Erkhov.
Llama-Doctor-3.2-3B-Instruct - bnb 4bits
Original model description:
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
- Doctor-Llama
Llama-Doctor-3.2-3B-Instruct Modelfile
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.
| 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 |
Key Use Cases:
- Conversational AI: Engage in dialogue, answering questions, or providing responses based on user instructions.
- Text Generation: Generate content, summaries, explanations, or solutions to problems based on given prompts.
- 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.
