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Model: google/txgemma-2b-predict
Source: Original Platform

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README.md

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+---
+license: other
+license_name: health-ai-developer-foundations
+license_link: https://developers.google.com/health-ai-developer-foundations/terms
+language:
+  - en
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+  - therapeutics
+  - drug-development
+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
+---
+
+# TxGemma model card
+
+**Model documentation:** [TxGemma](https://developers.google.com/health-ai-developer-foundations/txgemma)
+
+**Resources:**
+
+*   Model on Google Cloud Model Garden: [TxGemma](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/txgemma)
+*   Model on Hugging Face: [TxGemma](https://huggingface.co/collections/google/txgemma-release-67dd92e931c857d15e4d1e87)
+*   GitHub repository (supporting code, Colab notebooks, discussions, and
+    issues):
+    [TxGemma](https://github.com/google-gemini/gemma-cookbook/tree/main/TxGemma)
+*   Quick start notebook: [notebooks/quick\_start](https://github.com/google-gemini/gemma-cookbook/blob/main/TxGemma/%5BTxGemma%5DQuickstart_with_Hugging_Face.ipynb)
+*   Support: See
+    [Contact](https://developers.google.com/health-ai-developer-foundations/txgemma/get-started.md#contact).
+
+Terms of use: [Health AI Developer Foundations terms of
+use](https://developers.google.com/health-ai-developer-foundations/terms)
+
+**Author:** Google
+
+## Model information
+
+This section describes the TxGemma model and how to use it.
+
+### Description
+
+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)](https://tdcommons.ai/).
+
+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](https://arxiv.org/abs/2504.06196)
+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.
+
+### How to use
+
+Below are some example code snippets to help you quickly get started running the
+model locally on GPU. If you want to use the model to run inference on a large
+number of inputs, we recommend that you create a production version using [Model
+Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/txgemma).
+
+#### Formatting prompts for therapeutic tasks
+
+```python
+import json
+from huggingface_hub import hf_hub_download
+
+# Load prompt template for tasks from TDC
+tdc_prompts_filepath = hf_hub_download(
+    repo_id="google/txgemma-2b-predict",
+    filename="tdc_prompts.json",
+)
+with open(tdc_prompts_filepath, "r") as f:
+    tdc_prompts_json = json.load(f)
+
+# Set example TDC task and input
+task_name = "BBB_Martins"
+input_type = "{Drug SMILES}"
+drug_smiles = "CN1C(=O)CN=C(C2=CCCCC2)c2cc(Cl)ccc21"
+
+# Construct prompt using template and input drug SMILES string
+TDC_PROMPT = tdc_prompts_json[task_name].replace(input_type, drug_smiles)
+print(TDC_PROMPT)
+```
+
+The resulting prompt is in the format expected by the model:
+
+```none
+Instructions: Answer the following question about drug properties.
+Context: As a membrane separating circulating blood and brain extracellular fluid, the blood-brain barrier (BBB) is the protection layer that blocks most foreign drugs. Thus the ability of a drug to penetrate the barrier to deliver to the site of action forms a crucial challenge in development of drugs for central nervous system.
+Question: Given a drug SMILES string, predict whether it
+(A) does not cross the BBB (B) crosses the BBB
+Drug SMILES: CN1C(=O)CN=C(C2=CCCCC2)c2cc(Cl)ccc21
+Answer:
+```
+
+#### Running the model on predictive tasks
+
+```python
+# pip install accelerate transformers
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load model directly from Hugging Face Hub
+tokenizer = AutoTokenizer.from_pretrained("google/txgemma-2b-predict")
+model = AutoModelForCausalLM.from_pretrained(
+    "google/txgemma-2b-predict",
+    device_map="auto",
+)
+
+# Formatted TDC prompt (see "Formatting prompts for therapeutic tasks" section above)
+prompt = TDC_PROMPT
+
+# Prepare tokenized inputs
+input_ids = tokenizer(prompt, return_tensors="pt").to("cuda")
+
+# Generate response
+outputs = model.generate(**input_ids, max_new_tokens=8)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+Alternatively, you can use the `pipeline` API, which provides a simple way to
+run inference while abstracting away complex details of loading and using the
+model and tokenizer:
+
+```python
+# pip install transformers
+from transformers import pipeline
+
+# Instantiate a text generation pipeline using the model
+pipe = pipeline(
+    "text-generation",
+    model="google/txgemma-2b-predict",
+    device="cuda",
+)
+
+# Formatted TDC prompt (see "Formatting prompts for therapeutic tasks" section above)
+prompt = TDC_PROMPT
+
+# Generate response
+outputs = pipe(prompt, max_new_tokens=8)
+response = outputs[0]["generated_text"]
+print(response)
+```
+
+### Examples
+
+See the following Colab notebooks for examples of how to use TxGemma:
+
+*   To give the model a quick try, running it locally with weights from Hugging
+    Face, see [Quick start notebook in
+    Colab](https://colab.research.google.com/github/google-gemini/gemma-cookbook/blob/main/TxGemma/%5BTxGemma%5DQuickstart_with_Hugging_Face.ipynb),
+    which includes some example eval tasks from TDC.
+*   For a demo of how to fine-tune TxGemma in Hugging Face, see our [Fine-tuning notebook in Colab](https://colab.research.google.com/github/google-gemini/gemma-cookbook/blob/main/TxGemma/%5BTxGemma%5DFinetune_with_Hugging_Face.ipynb).
+*   For a demo of how TxGemma can be used as a tool as part of a larger agentic
+    workflow powered by Gemini 2 see the [Agentic workflow notebook in Colab](https://colab.research.google.com/github/google-gemini/gemma-cookbook/blob/main/TxGemma/%5BTxGemma%5DAgentic_Demo_with_Hugging_Face.ipynb).
+
+### Model architecture overview
+
+*   TxGemma is based on the Gemma 2 family of lightweight, state-of-the-art open
+    LLMs. It utilizes a decoder-only transformer architecture.
+*   Base Model: Gemma 2 (2B, 9B, and 27B parameter versions).
+*   Fine-tuning Data: Therapeutics Data Commons, a collection of
+    instruction-tuning datasets covering diverse therapeutic modalities and
+    targets.
+*   Training Approach: Instruction fine-tuning using a mixture of therapeutic
+    data (TxT) and, for conversational variants, general instruction-tuning
+    data.
+*   Conversational Variants: TxGemma-Chat models (9B and 27B) are trained with a
+    mixture of therapeutic and general instruction-tuning data to maintain
+    conversational abilities.
+
+### Technical Specifications
+
+*   Model type: Decoder-only Transformer (based on Gemma 2)
+*   Key publication: [TxGemma: Efficient and Agentic LLMs for Therapeutics](https://arxiv.org/abs/2504.06196)
+*   Model created: 2025-03-18 (From the TxGemma Variant Proposal)
+*   Model Version: 1.0.0
+
+### Performance & Validation
+
+TxGemma's performance has been validated on a comprehensive
+[benchmark](https://tdcommons.ai/) of 66 therapeutic tasks derived from TDC.
+
+### Key performance metrics
+
+*   Aggregated Improvement: Improves over the original [Tx-LLM
+    paper](https://arxiv.org/abs/2406.06316) on 45 out of 66 therapeutic tasks.
+
+*   Best-in-Class Performance: Surpasses or matches best-in-class performance on
+    50 out of 66 tasks, exceeding specialist models on 26 tasks. See [Table A.11](https://arxiv.org/abs/2504.06196)
+    of the TxGemma paper for the full breakdown.
+
+### Inputs and outputs
+
+*   **Input:** Text. For best performance, text prompts should be formatted
+    according to the TDC structure, including
+    instructions, context, question, and, optionally, few-shot examples. Inputs
+    can include SMILES strings, amino acid sequences, nucleotide sequences, and
+    natural language text.
+
+*   **Output:** Text.
+
+## Dataset details
+
+### Training dataset
+
+**Therapeutics Data Commons**: A curated collection of instruction-tuning
+datasets covering 66 tasks spanning the discovery and development of safe and
+effective medicine. This includes over 15 million data points across different
+biomedical entities. Released TxGemma models are only trained on datasets with
+commercial licenses, whereas models in our publication are also trained on
+datasets with non-commercial licenses.
+
+**General Instruction-Tuning Data**: Used for TxGemma-Chat in combination with
+TDC.
+
+### Evaluation dataset
+
+**Therapeutics Data Commons**: The same 66 tasks used for training are used
+for evaluation, following TDC's recommended methodologies for data splits
+(random, scaffold, cold-start, combination, and temporal).
+
+## License
+
+The use of TxGemma is governed by the [Health AI Developer Foundations terms of
+use](https://developers.google.com/health-ai-developer-foundations/terms).
+
+## Implementation information
+
+This section contains details about the model internals.
+
+### Software
+
+Training was done using [JAX](https://github.com/jax-ml/jax).
+
+JAX allows researchers to take advantage of the latest generation of hardware,
+including TPUs, for faster and more efficient training of large models.
+
+## Use and limitations
+
+### Intended use
+
+*   Research and development of therapeutics.
+
+### Benefits
+
+TxGemma provides a versatile and powerful tool for accelerating therapeutic
+development. It offers:
+
+*   Strong performance across a wide range of tasks.
+*   Data efficiency compared to larger models.
+*   A foundation for further fine-tuning from private data.
+*   Integration into agentic workflows.
+
+### Limitations
+
+*   Trained on public data from TDC.
+*   Task-specific validation remains an important aspect of downstream model
+    development by the end user.
+*   As with any research, developers should ensure that any downstream
+    application is validated to understand performance using data that is
+    appropriately representative of the intended use setting for the specific
+    application (e.g., age, sex, gender, condition, scanner, etc.).
+
+## Citation
+
+```bibtex
+@article{wang2025txgemma,
+    title={TxGemma: Efficient and Agentic LLMs for Therapeutics},
+    author={Wang, Eric and Schmidgall, Samuel and Jaeger, Paul F. and Zhang, Fan and Pilgrim, Rory and Matias, Yossi and Barral, Joelle and Fleet, David and Azizi, Shekoofeh},
+    year={2025},
+}
+```
+
+Find the paper [here](https://arxiv.org/abs/2504.06196).

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special_tokens_map.json

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+}

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