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--- |
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base_model: google/txgemma-9b-chat |
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language: |
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- en |
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library_name: transformers |
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license: other |
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license_name: health-ai-developer-foundations |
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license_link: https://developers.google.com/health-ai-developer-foundations/terms |
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pipeline_tag: text-generation |
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tags: |
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- therapeutics |
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- drug-development |
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- llama-cpp |
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- gguf-my-repo |
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extra_gated_heading: Access TxGemma on Hugging Face |
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extra_gated_prompt: To access TxGemma on Hugging Face, you're required to review and |
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agree to [Health AI Developer Foundation's terms of use](https://developers.google.com/health-ai-developer-foundations/terms). |
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To do this, please ensure you're logged in to Hugging Face and click below. Requests |
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are processed immediately. |
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extra_gated_button_content: Acknowledge license |
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--- |
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# Triangle104/txgemma-9b-chat-Q4_K_M-GGUF |
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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. |
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Refer to the [original model card](https://huggingface.co/google/txgemma-9b-chat) for more details on the model. |
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--- |
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TxGemma is a collection of lightweight, state-of-the-art, open language models |
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built upon Gemma 2, fine-tuned for therapeutic development. It comes in 3 sizes, |
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2B, 9B, and 27B. |
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TxGemma models are designed to process and understand information related to |
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various therapeutic modalities and targets, including small molecules, proteins, |
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nucleic acids, diseases, and cell lines. TxGemma excels at tasks such as |
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property prediction, and can serve as a foundation for further fine-tuning or as |
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an interactive, conversational agent for drug discovery. The model is fine-tuned |
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from Gemma 2 using a diverse set of instruction-tuning datasets, curated from |
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the Therapeutics Data Commons (TDC). |
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TxGemma is offered as both a prediction model that expects a narrow form of |
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prompting and for the 9B and 27B version, conversational models that are more |
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flexible and can be used in multi-turn interactions, including to explain its |
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rationale behind a prediction. This conversational model comes at the expense of |
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some raw prediction performance. See our manuscript |
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for more information. |
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Key Features |
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- |
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-Versatility: Exhibits strong performance across a wide range of therapeutic |
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tasks, outperforming or matching best-in-class performance on a significant |
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number of benchmarks. |
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-Data Efficiency: Shows competitive performance even with limited data |
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compared to larger models, offering improvements over its predecessors. |
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-Conversational Capability (TxGemma-Chat): Includes conversational variants |
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that can engage in natural language dialogue and explain the reasoning |
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behind their predictions. |
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-Foundation for Fine-tuning: Can be used as a pre-trained foundation for |
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specialized use cases. |
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Potential Applications |
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- |
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TxGemma can be a valuable tool for researchers in the |
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following areas: |
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Accelerated Drug Discovery: Streamline the therapeutic development process |
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by predicting properties of therapeutics and targets for a wide variety of |
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tasks including target identification, drug-target interaction prediction, |
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and clinical trial approval prediction. |
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--- |
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## Use with llama.cpp |
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Install llama.cpp through brew (works on Mac and Linux) |
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```bash |
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brew install llama.cpp |
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``` |
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Invoke the llama.cpp server or the CLI. |
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### CLI: |
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```bash |
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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" |
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``` |
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### Server: |
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```bash |
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llama-server --hf-repo Triangle104/txgemma-9b-chat-Q4_K_M-GGUF --hf-file txgemma-9b-chat-q4_k_m.gguf -c 2048 |
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``` |
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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. |
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Step 1: Clone llama.cpp from GitHub. |
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``` |
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git clone https://github.com/ggerganov/llama.cpp |
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``` |
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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). |
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``` |
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cd llama.cpp && LLAMA_CURL=1 make |
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``` |
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Step 3: Run inference through the main binary. |
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``` |
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./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" |
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``` |
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or |
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``` |
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./llama-server --hf-repo Triangle104/txgemma-9b-chat-Q4_K_M-GGUF --hf-file txgemma-9b-chat-q4_k_m.gguf -c 2048 |
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``` |
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