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@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 tokenizer.json filter=lfs diff=lfs merge=lfs -text
+assets/logo.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -17,7 +17,7 @@ language:
 ---
 
 <p align="center">
-  <img src="assets/logo.png" alt="Trinity-Mini-DrugProt-Think" width="350" />
+  <img src="https://huggingface.co/lokahq/Trinity-Mini-DrugProt-Think/resolve/main/assets/logo.png" alt="Trinity-Mini-DrugProt-Think" width="350" />
 </p>
 
 <p align="center">
@@ -27,7 +27,7 @@ language:
 
 <p align="center">
   <a href="index.html">📝 <strong>Report</strong></a> &nbsp; | &nbsp;
-  <a href="https://medium.com/@jakimovski_bojan/9e1c1c430ce9"><img src="https://www.sysgroup.com/wp-content/uploads/2025/02/Amazon_Web_Services-Logo.wine_.png" height="20" style="vertical-align:middle;"/> <strong>AWS deployment guide</strong></a> &nbsp; | &nbsp;
+  <a href="https://medium.com/@jakimovski_bojan/9e1c1c430ce9"><img src="https://www.sysgroup.com/wp-content/uploads/2025/02/Amazon_Web_Services-Logo.wine_.png" height="14" style="vertical-align:middle;"/> <strong>AWS deployment guide</strong></a> &nbsp; | &nbsp;
   <a href="https://github.com/LokaHQ/Trinity-Mini-DrugProt-Think" aria-label="GitHub"><svg viewBox="0 0 16 16" fill="currentColor" width="20" height="20" style="vertical-align:middle;"><path d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27s1.36.09 2 .27c1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.01 8.01 0 0 0 16 8c0-4.42-3.58-8-8-8z"/></svg> <strong>GitHub</strong></a>
 </p>
 
@@ -36,8 +36,6 @@ language:
 
 A LoRA adapter fine-tuned on [Arcee Trinity Mini](https://huggingface.co/arcee-ai/Trinity-Mini) using GRPO (Group Relative Policy Optimization) for **drug-protein relation extraction** on the [DrugProt (BioCreative VII)](https://huggingface.co/datasets/OpenMed/drugprot-parquet) benchmark. The model classifies 13 types of drug-protein interactions from PubMed abstracts, producing structured pharmacological reasoning traces before giving its answer.
 
-📄 **Blog post:** [Post-Training an Open MoE to Extract Drug-Protein Relations](https://github.com/Shekswess/drugprotrelrl)  
-💻 **Code & configs:** [github.com/Shekswess/drugprotrelrl](https://github.com/Shekswess/drugprotrelrl)
 
 ## Model Details
 
@@ -93,6 +91,38 @@ model = AutoModelForCausalLM.from_pretrained(
 model = PeftModel.from_pretrained(model, adapter_id)
 
 messages = [
+    {
+        "role": "system",
+        "content": (
+            "You are an expert biomedical relation extraction assistant. Your task is to identify the type of interaction between a drug/chemical and a gene/protein in biomedical text.\n\n"
+            "For each question:\n"
+            "1. First, wrap your detailed biomedical reasoning inside <think></think> tags\n"
+            "2. Analyze the context around both entities to understand their relationship\n"
+            "3. Consider the pharmacological and molecular mechanisms involved\n"
+            "4. Then provide your final answer inside \\boxed{} using exactly one letter (A-M)\n\n"
+            "The 13 DrugProt relation types are:\n"
+            "A. INDIRECT-DOWNREGULATOR - Chemical indirectly decreases protein activity/expression\n"
+            "B. INDIRECT-UPREGULATOR - Chemical indirectly increases protein activity/expression\n"
+            "C. DIRECT-REGULATOR - Chemical directly regulates protein (mechanism unspecified)\n"
+            "D. ACTIVATOR - Chemical activates the protein\n"
+            "E. INHIBITOR - Chemical inhibits the protein\n"
+            "F. AGONIST - Chemical acts as an agonist of the receptor/protein\n"
+            "G. AGONIST-ACTIVATOR - Chemical is both agonist and activator\n"
+            "H. AGONIST-INHIBITOR - Chemical is agonist but inhibits downstream effects\n"
+            "I. ANTAGONIST - Chemical acts as an antagonist of the receptor/protein\n"
+            "J. PRODUCT-OF - Chemical is a product of the enzyme\n"
+            "K. SUBSTRATE - Chemical is a substrate of the enzyme\n"
+            "L. SUBSTRATE_PRODUCT-OF - Chemical is both substrate and product\n"
+            "M. PART-OF - Chemical is part of the protein complex\n\n"
+            "Example format:\n"
+            "<think>\n"
+            "The text describes [chemical] and [protein]. Based on the context...\n"
+            "- The phrase \"[relevant text]\" indicates that...\n"
+            "- This suggests a [type] relationship because...\n"
+            "</think>\n"
+            "\\boxed{A}"
+        )
+    },
     {
         "role": "user",
         "content": (
@@ -117,15 +147,7 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 
 ## Training Progress
 
-Training ran for ~130 steps on Prime Intellect infrastructure. Best accuracy reward reached ~0.83 during training.
-
-| Step | Accuracy Reward | Composite Reward |
-|---|---|---|
-| 0 | ~0.68 | ~0.72 |
-| 25 | ~0.71 | ~0.74 |
-| 50 | ~0.74 | ~0.77 |
-| 75 | ~0.77 | ~0.80 |
-| 100 | ~0.80 | ~0.83 |
+Training ran for ~100 steps on Prime Intellect infrastructure. Best accuracy reward reached ~0.83 during training.
 
 ## Limitations
 
@@ -135,13 +157,16 @@ Training ran for ~130 steps on Prime Intellect infrastructure. Best accuracy rew
 
 ## Citation
 
-```bibtex
-@article{jakimovski2026drugprotrl,
-  title   = {Post-Training an Open MoE to Extract Drug-Protein Relations},
-  author  = {Jakimovski, Bojan and Kalinovski, Petar},
-  year    = {2026},
-  url     = {https://github.com/Shekswess/drugprotrelrl}
-}
+<div class="citation-block">
+	            <pre><code>@misc{jakimovski2026drugprotrl,
+  title        = {Post-Training an Open MoE Model to Extract Drug-Protein Relations: Trinity-Mini-DrugProt-Think},
+  author       = {Jakimovski, Bojan and Kalinovski, Petar},
+  year         = {2026},
+  month        = feb,
+  howpublished = {Blog post},
+  url          = {https://github.com/LokaHQ/Trinity-Mini-DrugProt-Think}
+}</code></pre>
+	          </div>
 ```
 
 ## Acknowledgements