InstructPLM
/

MPNN-ProGen2-xlarge-CATH42

@@ -1,259 +1,19 @@
 ---
 license: apache-2.0
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
-tokenizer = AutoTokenizer.from_pretrained('InstructPLM/MPNN-ProGen2-xlarge-CATH42', trust_remote_code=True)
-model = AutoModelForCausalLM.from_pretrained('InstructPLM/MPNN-ProGen2-xlarge-CATH42', trust_remote_code=True)
-model.cuda().eval()
-model.requires_grad_(False)
-batch = tokenizer('Fast-PETase.pyd|1MQTNPYARGPNPTAASLEASAGPFTVRSFTVSRPSGYGAGTVYYPTNAGGTVGAIAIVPGYTARQSSIKWWGPRLASHGFVVITIDTNSTLDQPESRSSQQMAALRQVASLNGTSSSPIYGKVDTARMGVMGWSMGGGGSLISAANNPSLKAAAPQAPWHSSTNFSSVTVPTLIFACENDSIAPVNSSALPIYDSMSQNAKQFLEIKGGSHSCANSGNSNQALIGKKGVAWMKRFMDNDTRYSTFACENPNSTAVSDFRTANCS2',return_tensors='pt').to(device=model.device)
-labels = batch.input_ids.masked_fill((1-batch.attention_mask).bool(), -100)
-labels[:, :tokenizer.n_queries+1] = -100
-batch["labels"] = labels
-with torch.no_grad():
-    with torch.cuda.amp.autocast(dtype=torch.float16):
-        output = model(**batch)
-print(output.loss.item())
-batch = tokenizer('Fast-PETase.pyd|1',return_tensors='pt').to(device=model.device)
-tokens_batch = model.generate(
-    **batch,
-    do_sample=True,
-    temperature=0.8,
-    max_length=512+tokenizer.n_queries,
-    min_new_tokens=5,
-    top_p=0.9,
-    num_return_sequences=5,
-    pad_token_id=0,
-    repetition_penalty=1.0,
-    bad_words_ids=[[3]]
-    )
-texts = tokenizer.batch_decode(tokens_batch)
-def truncate_seq(text):
-    bos = text.find('1')
-    eos = text.find('2')
-    if eos > bos and bos >= 0:
-        return text[bos+1:eos]
-    else:
-        return text[bos+1:]
-print([truncate_seq(t) for t in texts])
-# Ref. Seq
-#       'MQTNPYARGPNPTAASLEASAGPFTVRSFTVSRPSGYGAGTVYYPTNAGGTVGAIAIVPGYTARQSSIKWWGPRLASHGFVVITIDTNSTLDQPESRSSQQMAALRQVASLNGTSSSPIYGKVDTARMGVMGWSMGGGGSLISAANNPSLKAAAPQAPWHSSTNFSSVTVPTLIFACENDSIAPVNSSALPIYDSMSQNAKQFLEIKGGSHSCANSGNSNQALIGKKGVAWMKRFMDNDTRYSTFACENPNSTAVSDFRTANCS'
-# Designed seq:
-#       'METNPFHRGPDPTCASLEAGAGPFNVQSFRVDRPLGFGAGTVFYPTDAGGQVPAIAIAPGFTQTQSSVMWYGPRLASHGFVVIVIDTISTFDNPDSRSAQLLAALDQVANLNSNASSPIYGKVDTTRQAVMGHSMGGGGSLISAMNNPSLKAAAPMAPWHVSTNFSAVQVPTFIIGAENDTIAPVASHSIPFYNSIPSSLPKAYMELAGASHLAPNSSNPTIAKYSISWLKRFVDNDTRYEQFLCPAPTSTALISEYRDTCPY',
-#       'EETNPYSKGPDPTAASLEASAGPFTVQSFSVARPLGFGAGTVYYPTDAGGKVGAIAVVPGYTDTQGSIRWWGPRLASHGFVVMTIDTISSYDQPDSRSAQLMAALDQLANLNSTSSSPIYNKVDTTRQAVMGHSMGGGGSLISAMNNPNLKAAIPMAPWHSSTNFSSVKVPTMILGAERDTVAPVSSHAEPFYNSLPSSTPKAYLELKGASHFFPNTTNTPTFAKSVLAWLKRFVDNDTRYEQFLCPGPTSTDLTDYRNTCPY',
-#       'SETNPYIKGPDPTAASLEASAGAFTVQSFTVSRPTGFGAGTVYYPTDAGGRVGAIAIVPGYTATQSSIKWWGPRLASHGFVVMTIDTNSTYDQPDSRANQLMAALDQLTNLNSTRSSPIYGKVDTTRQGVMGHSMGGGGSLIAAQDNPNLKAAIPLAPWHSSSNFSSVTVPTLIIGAQNDTVAPVSSHSIPFYTSLPSSLDKAYLELNGASHFAPNSSNTTIAKYSISWLKRFIDNDTRYEQFLCPPPSGSALISEYRNTCPY',
-#       'EETWPYHRGPDPTAASLEASAGPFTVQSFTVARPLGFGAGTVYYPTDAGGRVGAVAVVPGYTQTQSAIRWWGPRLASHGFVVMTIDTISTFDQPDSRSAQLLAALDQLAVLNSTRSSPIYNKVDTTRQGVMGHSMGGGGSLISAMNNPSLKAAVPLAPWHASTNFSNVQVPTLIIGASDDTTASVTTHSIPFYNSIPSSVPKAYLELQGQSHFCPNTSNTTIAKYSISWLKRFIDNDTRYDQFLCPPPNGSAISDYRSTCPH',
-#       'METNPFIRGPNPTAASLEASAGPFQVSSFSVARPVGFGAGTVYYPTDAGGQVPAIAIAPGFTQTQASVKWYGPRLASHGFVVIVIDTNSTLDNPDSRSAQLLAALDQVSTLNSSSSSPIYGKVDTTRQGVMGHSMGGGGSLISAQNNPALKAAIPLAPWHVSTDFSGVTVPTLIIGAENDTVAPVGTHAEPFYNSIPSSTPKAYLELNNASHFAPNTSNTTIAKYSIAWLKRFVDNDTRYDQFLCPAPNGNAIQDYRDTCPH'
-#
-```
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-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-## Bias, Risks, and Limitations
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-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
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-## Training Details
-### Training Data
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-### Training Procedure
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-#### Speeds, Sizes, Times [optional]
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-## Evaluation
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-### Testing Data, Factors & Metrics
-#### Testing Data
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-#### Factors
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-### Results
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-#### Summary
-## Model Examination [optional]
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-## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-## Technical Specifications [optional]
-### Model Architecture and Objective
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-## Citation [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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 ---
 license: apache-2.0
+pipeline_tag: text-generation
+tags:
+- biology
 ---
+# InstrcutPLM
+InstructPLM is a state-of-the-art protein design model based on [ProGen2](https://www.cell.com/cell-systems/abstract/S2405-4712(23)00272-7)
+and [ProteinMPNN](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9997061/)
+and trained on [CATH 4.2](https://www.cathdb.info/) dataset.
+It can design protein sequences that accurately conform to specified backbone structures.
+<p align="center">
+<img src="https://cdn-uploads.huggingface.co/production/uploads/62a8397d839eeb3ef16a7566/1NRk65EImgBAFgvh8HJrA.png" alt="drawing" width="200"/>
+</p>
+Please visit our [repo](https://github.com/Eikor/InstructPLM) and [paper](https://github.com/Eikor/InstructPLM) for more information.
+Please consider cite our paper and repo: