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Model: AI4PD/ProtGPT3-1.3B
Source: Original Platform

README.md

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+---
+library_name: transformers
+tags:
+- biology
+- protein-language-model
+- protein-generation
+- causal-lm
+- mixture-of-experts
+- transformers
+---
+
+# Model Card for ProtGPT3-1.3B
+
+## Model Details
+
+### Model Description
+
+ProtGPT3-1.3B is a single-sequence autoregressive protein language model for protein sequence generation. It is part of the ProtGPT3 family, an open-source suite of promptable and aligned protein language models ranging from 112M to 10B parameters. ProtGPT3 models use a causal Mixtral-style Mixture-of-Experts architecture and are trained for causal language modeling on protein sequences.
+
+The single-sequence ProtGPT3 models can generate proteins in either N-to-C or C-to-N direction using special directional tokens. The model is intended for unconditional or prefix-conditioned protein sequence generation and can be used as a base model for downstream protein design workflows.
+
+
+## Uses
+
+### Direct Use
+
+ProtGPT3-1.3B can be used for autoregressive generation of protein sequences. Users can generate sequences unconditionally or condition generation on an amino-acid prefix.
+
+### Downstream Use
+
+The model may be fine-tuned or incorporated into protein design workflows, including family-specific generation, protein variant generation, and computational screening pipelines.
+
+### Out-of-Scope Use
+
+The model should not be used as the sole basis for experimental, clinical, environmental, or safety-critical decisions. Generated proteins require downstream computational and experimental validation. The model is not guaranteed to generate functional, soluble, safe, or synthesizable proteins.
+
+## Bias, Risks, and Limitations
+
+ProtGPT3-1.3B  learns from public protein sequence datasets and may reproduce biases present in those datasets. Generated sequences may be low-complexity, nonfunctional, unstable, insoluble, or biologically implausible. Protein generation models may also present dual-use risks if used irresponsibly.
+
+### Recommendations
+
+Users should apply appropriate computational filters, expert review, and experimental validation before using generated sequences. Users should also consider responsible-use practices for generative protein design.
+
+## How to Get Started with the Model
+
+Install dependencies:
+
+```bash
+pip install transformers accelerate torch
+```
+
+Load the model and tokenizer:
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "protgpt3/ProtGPT3-1.3B"  # Replace with the final checkpoint name
+
+# Load tokenizer for generation
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True,add_bos_token=True, add_eos_token=False, padding_side="left")
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True,
+)
+
+model.eval()
+```
+
+### Generate a protein sequence
+
+```python
+import torch
+
+prompt = ""  # Optionally provide an amino-acid prefix or model-specific direction
+
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    output_ids = model.generate(
+        inputs["input_ids"],
+        max_new_tokens=512,
+        do_sample=True,
+        temperature=0.8,
+        top_p=0.9,
+        eos_token_id=tokenizer.eos_token_id,
+        pad_token_id=tokenizer.pad_token_id,
+    )
+
+sequence = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+print(sequence) # output includes directional token "1" or "2" to denote if sequence was generated N-to-C or C-to-N
+```
+
+### Generate from an amino-acid prefix
+
+```python
+import torch
+
+# forward N-to-C generation with special token "1" 
+prefix = "1MKT" # use special token "2" instead of "1" for reverse  C-to-N generation
+
+inputs = tokenizer(prefix, return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    output_ids = model.generate(
+        inputs["input_ids"],
+        max_new_tokens=256,
+        do_sample=True,
+        temperature=0.8,
+        top_p=0.9,
+        eos_token_id=tokenizer.eos_token_id,
+        pad_token_id=tokenizer.eos_token_id,
+    )
+
+sequence = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+print(sequence)
+```
+
+### Batch generation
+
+```python
+import torch
+
+prompts = [
+    "",
+    "1MKT", # N-to-C generation
+    "2MAV", # C-to-N generation
+]
+
+inputs = tokenizer(
+    prompts,
+    return_tensors="pt",
+    padding=True,
+).to(model.device)
+
+with torch.no_grad():
+    output_ids = model.generate(
+        inputs["input_ids"],
+        max_new_tokens=256,
+        do_sample=True,
+        temperature=0.8,
+        top_p=0.9,
+        eos_token_id=tokenizer.eos_token_id,
+        pad_token_id=tokenizer.bos_token_id,
+    )
+
+sequences = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+
+for sequence in sequences:
+    print(sequence)
+```
+
+## Technical Specifications
+
+### Model Architecture and Objective
+
+ProtGPT3-1.3B is a decoder-only causal language model using a Mixtral-style sparse Mixture-of-Experts architecture. It was trained with a causal language modeling objective on protein sequences.
+
+### Compute Infrastructure
+
+#### Software
+
+Training used FlashAttention-2, online mini-batch packing, Liger Kernel, and DeepSpeed.
+
+## Citation
+
+**BibTeX:**
+
+```bibtex
+@article{protgpt3,
+  title={ProtGPT3: an Open-source family of Promptable and Aligned Protein Language Models},
+  author={Anonymous Authors},
+  year={2026}
+}
+```
+
+## More Information
+
+All models and code are released through the Hugging Face ecosystem and accompanying code repository.