Triangle104/Llama-Doctor-3.2-3B-Instruct-Q6_K-GGUF

This model was converted to GGUF format from prithivMLmods/Llama-Doctor-3.2-3B-Instruct using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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Model details:

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:

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.

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Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/Llama-Doctor-3.2-3B-Instruct-Q6_K-GGUF --hf-file llama-doctor-3.2-3b-instruct-q6_k.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Llama-Doctor-3.2-3B-Instruct-Q6_K-GGUF --hf-file llama-doctor-3.2-3b-instruct-q6_k.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Triangle104/Llama-Doctor-3.2-3B-Instruct-Q6_K-GGUF --hf-file llama-doctor-3.2-3b-instruct-q6_k.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Llama-Doctor-3.2-3B-Instruct-Q6_K-GGUF --hf-file llama-doctor-3.2-3b-instruct-q6_k.gguf -c 2048