README.md

---
license: mit
datasets:
- avaliev/chat_doctor
language:
- en
base_model: prithivMLmods/Llama-Doctor-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-cpp
- gguf-my-repo
---

# Triangle104/Llama-Doctor-3.2-3B-Instruct-Q8_0-GGUF
This model was converted to GGUF format from [`prithivMLmods/Llama-Doctor-3.2-3B-Instruct`](https://huggingface.co/prithivMLmods/Llama-Doctor-3.2-3B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/prithivMLmods/Llama-Doctor-3.2-3B-Instruct) for more details on the model.

---
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.

---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo Triangle104/Llama-Doctor-3.2-3B-Instruct-Q8_0-GGUF --hf-file llama-doctor-3.2-3b-instruct-q8_0.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Llama-Doctor-3.2-3B-Instruct-Q8_0-GGUF --hf-file llama-doctor-3.2-3b-instruct-q8_0.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) 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-Q8_0-GGUF --hf-file llama-doctor-3.2-3b-instruct-q8_0.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Llama-Doctor-3.2-3B-Instruct-Q8_0-GGUF --hf-file llama-doctor-3.2-3b-instruct-q8_0.gguf -c 2048
```