README.md

metadata

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

Model Type	Size	Context Length	Link
GGUF	3B	-	🤗 Llama-Doctor-3.2-3B-Instruct-GGUF

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.