Dr Samantha 7B GGUF

🚀 Dr Samantha 7B - GGUF

Dr Samantha 7B is a medical - related language model. It is based on the Llama architecture and trained with multiple datasets, aiming to provide high - quality text generation in the medical field.

📚 Documentation

Model Information

Prompt Template

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{prompt}

### Response:

About GGUF

GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.

Here is an incomplete list of clients and libraries that are known to support GGUF:

• llama.cpp. The source project for GGUF. Offers a CLI and a server option.
• [text - generation - webui](https://github.com/oobabooga/text - generation - webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
• KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
• GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
• LM Studio, an easy - to - use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
• [LoLLMS Web UI](https://github.com/ParisNeo/lollms - webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
• Faraday.dev, an attractive and easy to use character - based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
• [llama - cpp - python](https://github.com/abetlen/llama - cpp - python), a Python library with GPU accel, LangChain support, and OpenAI - compatible API server.
• candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.
• ctransformers, a Python library with GPU accel, LangChain support, and OpenAI - compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.

Repositories Available

• [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Dr_Samantha - 7B - AWQ)
• [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Dr_Samantha - 7B - GPTQ)
• [2, 3, 4, 5, 6 and 8 - bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Dr_Samantha - 7B - GGUF)
• [Sethu Iyer's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/sethuiyer/Dr_Samantha - 7b)

Compatibility

These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit d0cee0d

They are also compatible with many third - party UIs and libraries - please see the list at the top of this README.

Explanation of Quantisation Methods

Provided Files

Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.

How to Download GGUF Files

⚠️ Important Note

You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.

The following clients/libraries will automatically download models for you, providing a list of available models to choose from:

• LM Studio
• LoLLMS Web UI
• Faraday.dev

In text - generation - webui

Under Download Model, you can enter the model repo: TheBloke/Dr_Samantha - 7B - GGUF and below it, a specific filename to download, such as: dr_samantha - 7b.Q4_K_M.gguf.

Then click Download.

On the command line, including multiple files at once

I recommend using the huggingface - hub Python library:

pip3 install huggingface - hub

Then you can download any individual model file to the current directory, at high speed, with a command like this:

huggingface - cli download TheBloke/Dr_Samantha - 7B - GGUF dr_samantha - 7b.Q4_K_M.gguf --local - dir . --local - dir - use - symlinks False

huggingface - cli download TheBloke/Dr_Samantha - 7B - GGUF --local - dir . --local - dir - use - symlinks False --include='*Q4_K*gguf'

pip3 install hf_transfer

HF_HUB_ENABLE_HF_TRANSFER = 1 huggingface - cli download TheBloke/Dr_Samantha - 7B - GGUF dr_samantha - 7b.Q4_K_M.gguf --local - dir . --local - dir - use - symlinks False

How to Run

Example llama.cpp command

Make sure you are using llama.cpp from commit d0cee0d or later.

./main -ngl 35 -m dr_samantha - 7b.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:"

Change -ngl 32 to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.

Change -c 2048 to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.

If you want to have a chat - style conversation, replace the -p <PROMPT> argument with -i -ins

For other parameters and how to use them, please refer to the llama.cpp documentation

How to run in text - generation - webui

Further instructions can be found in the text - generation - webui documentation, here: [text - generation - webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text - generation - webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).

How to run from Python code

You can use GGUF models from Python code. (The original README didn't provide detailed code examples here, so it's not further expanded.)