---
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
extra_gated_heading: Access TxGemma on Hugging Face
extra_gated_prompt: To access TxGemma on Hugging Face, you're required to review and
  agree to [Health AI Developer Foundation's terms of use](https://developers.google.com/health-ai-developer-foundations/terms).
  To do this, please ensure you're logged in to Hugging Face and click below. Requests
  are processed immediately.
extra_gated_button_content: Acknowledge license
---

# Triangle104/txgemma-9b-chat-Q6_K-GGUF
This model was converted to GGUF format from [`google/txgemma-9b-chat`](https://huggingface.co/google/txgemma-9b-chat) 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/google/txgemma-9b-chat) 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)

```bash
brew install llama.cpp

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

### CLI:
```bash
llama-cli --hf-repo Triangle104/txgemma-9b-chat-Q6_K-GGUF --hf-file txgemma-9b-chat-q6_k.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/txgemma-9b-chat-Q6_K-GGUF --hf-file txgemma-9b-chat-q6_k.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/txgemma-9b-chat-Q6_K-GGUF --hf-file txgemma-9b-chat-q6_k.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/txgemma-9b-chat-Q6_K-GGUF --hf-file txgemma-9b-chat-q6_k.gguf -c 2048
```