metadata

base_model: google/txgemma-9b-chat
language:
  - en
library_name: transformers
license: other
license_name: health-ai-developer-foundations
license_link: https://developers.google.com/health-ai-developer-foundations/terms
pipeline_tag: text-generation
tags:
  - therapeutics
  - drug-development
  - llama-cpp
  - gguf-my-repo
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Triangle104/txgemma-9b-chat-Q4_K_M-GGUF

This model was converted to GGUF format from google/txgemma-9b-chat using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

TxGemma is a collection of lightweight, state-of-the-art, open language models built upon Gemma 2, fine-tuned for therapeutic development. It comes in 3 sizes, 2B, 9B, and 27B.

TxGemma models are designed to process and understand information related to various therapeutic modalities and targets, including small molecules, proteins, nucleic acids, diseases, and cell lines. TxGemma excels at tasks such as property prediction, and can serve as a foundation for further fine-tuning or as an interactive, conversational agent for drug discovery. The model is fine-tuned from Gemma 2 using a diverse set of instruction-tuning datasets, curated from the Therapeutics Data Commons (TDC).

TxGemma is offered as both a prediction model that expects a narrow form of prompting and for the 9B and 27B version, conversational models that are more flexible and can be used in multi-turn interactions, including to explain its rationale behind a prediction. This conversational model comes at the expense of some raw prediction performance. See our manuscript for more information.

Key Features

-Versatility: Exhibits strong performance across a wide range of therapeutic tasks, outperforming or matching best-in-class performance on a significant number of benchmarks.

-Data Efficiency: Shows competitive performance even with limited data compared to larger models, offering improvements over its predecessors.

-Conversational Capability (TxGemma-Chat): Includes conversational variants that can engage in natural language dialogue and explain the reasoning behind their predictions.

-Foundation for Fine-tuning: Can be used as a pre-trained foundation for specialized use cases.

Potential Applications

TxGemma can be a valuable tool for researchers in the following areas:

Accelerated Drug Discovery: Streamline the therapeutic development process by predicting properties of therapeutics and targets for a wide variety of tasks including target identification, drug-target interaction prediction, and clinical trial approval prediction.

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/txgemma-9b-chat-Q4_K_M-GGUF --hf-file txgemma-9b-chat-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/txgemma-9b-chat-Q4_K_M-GGUF --hf-file txgemma-9b-chat-q4_k_m.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/txgemma-9b-chat-Q4_K_M-GGUF --hf-file txgemma-9b-chat-q4_k_m.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo Triangle104/txgemma-9b-chat-Q4_K_M-GGUF --hf-file txgemma-9b-chat-q4_k_m.gguf -c 2048