Update README.md

README.md

@@ -13,22 +13,25 @@ tags:
 - AI4Science
 - Materiomics
 - Biomateriomics
-base_model: 
-- meta-llama/Meta-Llama-3.1-8B-Instruct
-- NousResearch/Hermes-3-Llama-3.1-8B
+base_model:
+- HuggingFaceTB/SmolLM-1.7B
 datasets:
 - mlabonne/orpo-dpo-mix-40k
-- lamm-mit/magpie-ultra-v0.1-DPO
-- HuggingFaceH4/deita-10k-v0-sft
 - lamm-mit/bio-silk-mech-data-integrated
+- HuggingFaceTB/Magpie-Pro-300K-Filtered-H4
+- HuggingFaceTB/self-oss-instruct-sc2-H4
+- HuggingFaceTB/OpenHermes-2.5-H4
+- HuggingFaceTB/everyday-conversations-llama3.1-2k
+- HuggingFaceTB/instruct-data-basics-smollm-H4
+license: apache-2.0
 ---
 
 # lamm-mit/Bioinspired-SmolLM-1.7B-Instruct
 
 This model was constructed from the SmolLM-1.7B base model using a combination of Continued Pre-training (CPT), Supervised fine-tuning (SFT), and DPO.
 
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/prfiePwzbYVqarvhnVYEt.png)
 
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/IhTyZRoOB11Qr9GRl54cx.png)
 The model was trained on a mix of publically available datasets and a corpus of around 5,000 scientific papers in the bio-inspired materials field. During the CPT phase, the raw text of all papers is used. During SFT and ORPO, the model is shown a high-quality mix of question-answer pairs and question-answer-rejected triples, respectively, along with other datasets to train the model for instructions and chat interactions.
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/2D3Jm0goTW_mvNRK2gKKU.png)
@@ -272,4 +275,4 @@ Please cite as:
   doi={10.1063/5.0203126},
   note={\url{https://doi.org/10.1063/5.0203126}}
 }
-```
+```