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Model: GreatCaptainNemo/ProLLaMA Source: Original Platform
2026-05-14 09:11:03 +08:00
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+---
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# ProLLaMA: A Protein Large Language Model for Multi-Task Protein Language Processing
+
+[Paper on arxiv](https://arxiv.org/abs/2402.16445) for more information
+
+[Github](https://github.com/Lyu6PosHao/ProLLaMA) for more information
+
+ProLLaMA is based on Llama-2-7b, so please follow the license of Llama2.
+
+# Input Format:
+The instructions which you input to the model should follow the following format:
+```text
+[Generate by superfamily] Superfamily=<xxx>
+or
+[Determine superfamily] Seq=<yyy>
+```
+Here are some examples of the input:
+```text
+[Generate by superfamily] Superfamily=<Ankyrin repeat-containing domain superfamily>
+```
+```
+#You can also specify the first few amino acids of the protein sequence:
+[Generate by superfamily] Superfamily=<Ankyrin repeat-containing domain superfamily> Seq=<MKRVL
+```
+```
+[Determine superfamily] Seq=<MAPGGMPREFPSFVRTLPEADLGYPALRGWVLQGERGCVLYWEAVTEVALPEHCHAECWGVVVDGRMELMVDGYTRVYTRGDLYVVPPQARHRARVFPGFRGVEHLSDPDLLPVRKR>
+```
+**See [this](https://github.com/Lyu6PosHao/ProLLaMA/blob/main/superfamilies.txt) on all the optional superfamilies.**
+
+# Quick usage:
+```bash
+# you can replace the model_path with your local path
+CUDA_VISIBLE_DEVICES=0 python main.py --model "GreatCaptainNemo/ProLLaMA" --interactive
+# main.py is as follows 👇:
+```
+
+```python
+import argparse
+import json, os
+import torch
+from transformers import LlamaForCausalLM, LlamaTokenizer
+from transformers import GenerationConfig
+from tqdm import tqdm
+
+generation_config = GenerationConfig(
+    temperature=0.2,
+    top_k=40,
+    top_p=0.9,
+    do_sample=True,
+    num_beams=1,
+    repetition_penalty=1.2,
+    max_new_tokens=400
+)
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--model', default=None, type=str,help="The local path of the model. If None, the model will be downloaded from HuggingFace")
+parser.add_argument('--interactive', action='store_true',help="If True, you can input instructions interactively. If False, the input instructions should be in the input_file.")
+parser.add_argument('--input_file', default=None, help="You can put all your input instructions in this file (one instruction per line).")
+parser.add_argument('--output_file', default=None, help="All the outputs will be saved in this file.")
+args = parser.parse_args()
+
+if __name__ == '__main__':
+    if args.interactive and args.input_file:
+        raise ValueError("interactive is True, but input_file is not None.")
+    if (not args.interactive) and (args.input_file is None):
+        raise ValueError("interactive is False, but input_file is None.")
+    if args.input_file and (args.output_file is None):
+        raise ValueError("input_file is not None, but output_file is None.")
+
+    load_type = torch.bfloat16
+    if torch.cuda.is_available():
+        device = torch.device(0)
+    else:
+        raise ValueError("No GPU available.")
+
+
+    model = LlamaForCausalLM.from_pretrained(
+        args.model,
+        torch_dtype=load_type,
+        low_cpu_mem_usage=True,
+        device_map='auto',
+        quantization_config=None
+    )
+    tokenizer = LlamaTokenizer.from_pretrained(args.model)
+
+    model.eval()
+    with torch.no_grad():
+        if args.interactive:
+            while True:
+                raw_input_text = input("Input:")
+                if len(raw_input_text.strip())==0:
+                    break
+                input_text = raw_input_text
+                input_text = tokenizer(input_text,return_tensors="pt")  
+
+                generation_output = model.generate(
+                                input_ids = input_text["input_ids"].to(device),
+                                attention_mask = input_text['attention_mask'].to(device),
+                                eos_token_id=tokenizer.eos_token_id,
+                                pad_token_id=tokenizer.pad_token_id,
+                                generation_config = generation_config,
+                                output_attentions=False
+                            )
+                s = generation_output[0]
+                output = tokenizer.decode(s,skip_special_tokens=True)
+                print("Output:",output)
+                print("\n")
+        else:
+            outputs=[]
+            with open(args.input_file, 'r') as f:
+                examples =f.read().splitlines()
+            print("Start generating...")
+            for index, example in tqdm(enumerate(examples),total=len(examples)):
+                input_text = tokenizer(example,return_tensors="pt")  #add_special_tokens=False ?
+
+                generation_output = model.generate(
+                    input_ids = input_text["input_ids"].to(device),
+                    attention_mask = input_text['attention_mask'].to(device),
+                    eos_token_id=tokenizer.eos_token_id,
+                    pad_token_id=tokenizer.pad_token_id,
+                    generation_config = generation_config
+                )
+                s = generation_output[0]
+                output = tokenizer.decode(s,skip_special_tokens=True)
+                outputs.append(output)
+            with open(args.output_file,'w') as f:
+                f.write("\n".join(outputs))
+            print("All the outputs have been saved in",args.output_file)
+```
+
+# Citation:
+```
+@article{lv2025prollama,
+  title={Prollama: A protein large language model for multi-task protein language processing},
+  author={Lv, Liuzhenghao and Lin, Zongying and Li, Hao and Liu, Yuyang and Cui, Jiaxi and Chen, Calvin Yu-Chian and Yuan, Li and Tian, Yonghong},
+  journal={IEEE Transactions on Artificial Intelligence},
+  year={2025},
+  publisher={IEEE}
+}
+```