Added license file, fixed typos, added badges

LICENSE

@@ -0,0 +1,201 @@
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README.md

@@ -4,6 +4,8 @@ language:
 - en
 base_model:
 - mistralai/Mistral-Small-24B-Instruct-2501
+datasets:
+- futurehouse/ether0-benchmark
 pipeline_tag: text-generation
 tags:
 - smiles
@@ -11,36 +13,38 @@ tags:
 - reasoning
 ---
 
-# ether0
-
-<img src="./images/ether0_logo.svg" width="200">
+[![Dataset on HF](https://huggingface.co/datasets/huggingface/badges/resolve/main/dataset-on-hf-md-dark.svg)](https://huggingface.co/datasets/futurehouse/ether0-benchmark)
 
+![ether0 logo](images/ether0_logo.svg)
 
+# ether0
 
-ether0 is a 24B language model trained to reason in English and output molecular structures as SMILES. 
+ether0 is a 24B language model trained to reason in English and output molecular structures as SMILES.
 It is derived from fine-tuning and reinforcement learning training from Mistral-Small-24B-Instruct-2501.
-Ask questions in English, but they may also include molecules specified as SMILES. The SMILES do not need to be canonical and may contain stereochemistry information.
+Ask questions in English, but they may also include molecules specified as SMILES.
+The SMILES do not need to be canonical and may contain stereochemistry information.
 ether0 has limited support for IUPAC names.
 
 ## Usage
-This model is trained to reason in English and output a molecule. 
+
+This model is trained to reason in English and output a molecule.
 It is NOT a general purpose chat model.
-It has been trained specifically for these tasks: 
-
-* IUPAC name to SMILES
-* Molecular formula (Hill notation) to SMILES, optionally with constraints on functional groups
-* modifying solubilities on given molecules (SMILES) by specifc LogS, optionally with constraints about scaffolds/groups/similarity
-* Matching pKa to molecules, proposing molecules with a pKa, or modifying molecules to adjust pKa
-* Matching scent/smell to molecuels and modifying molecules to adjust scent
-* Matching human cell receptor binding + mode (e.g., agonist) to molecule or modifying a molecule's binding effect. Trained [from EveBio](https://data.evebio.org/)
-* ADME properties (e.g., MDDK efflux ratio, LD50)
-* GHS classifications (as words, not codes, like "carcinogen"). For example, "modify this molecule to remove acute toxicity."
-* Quantitative LD50 in mg/kg
-* Proposing 1-step retrosynthesis from likely commercially available reagents
-* Predicting a reaction outcome
-* Gemeral natural language description of a specific molecule to that molecule (inverse molecule captioning)
-* Natural product elucidation (formula + organism to SMILES) - e.g, "A molecule with formula C6H12O6 was isolated from Homo sapiens, what could it be?"
-* Matching blood-brain barrier permeability (as a class) or modifying
+It has been trained specifically for these tasks:
+
+- IUPAC name to SMILES
+- Molecular formula (Hill notation) to SMILES, optionally with constraints on functional groups
+- Modifying solubilities on given molecules (SMILES) by specific LogS, optionally with constraints about scaffolds/groups/similarity
+- Matching pKa to molecules, proposing molecules with a pKa, or modifying molecules to adjust pKa
+- Matching scent/smell to molecules and modifying molecules to adjust scent
+- Matching human cell receptor binding + mode (e.g., agonist) to molecule or modifying a molecule's binding effect. Trained [from EveBio](https://data.evebio.org/)
+- ADME properties (e.g., MDDK efflux ratio, LD50)
+- GHS classifications (as words, not codes, like "carcinogen"). For example, "modify this molecule to remove acute toxicity."
+- Quantitative LD50 in mg/kg
+- Proposing 1-step retrosynthesis from likely commercially available reagents
+- Predicting a reaction outcome
+- General natural language description of a specific molecule to that molecule (inverse molecule captioning)
+- Natural product elucidation (formula + organism to SMILES) - e.g, "A molecule with formula C6H12O6 was isolated from Homo sapiens, what could it be?"
+- Matching blood-brain barrier permeability (as a class) or modifying
 
 For example, you can ask "Propose a molecule with a pKa of 9.2" or "Modify CCCCC(O)=OH to increase its pKa by about 1 unit." You cannot ask it "What is the pKa of CCCCC(O)=OH?"
 If you ask it questions that lie significantly beyond those tasks, it can fail. You can combine properties, although we haven't significantly benchmarked this.
@@ -54,20 +58,19 @@ The tasks generally follow previously reported numbers - e.g., a reaction predic
 
 The results below are the model weights released in this repo. This is different than the preprint, which has pre-safety mitigation benchmarks.
 
-<img src="./images/benchmarks.png" width="800">
+![ether0 benchmarking](images/benchmarks.png)
 
 ## Limitations
 
-It does not know general synonyms and it has poor textbook knowledge (e.g. it does not perform especially well on chembench). 
-For best results, input molecules as SMILES: if you input molecules with their common names, the model may reason using the incorrect smiles, resulting in poor results. 
+It does not know general synonyms and it has poor textbook knowledge (e.g. it does not perform especially well on chembench).
+For best results, input molecules as SMILES: if you input molecules with their common names, the model may reason using the incorrect smiles, resulting in poor results.
 For example, we have observed that the model often confuses lysine and glutamic acid if you ask questions using their common names, but should correctly reason about their chemistry if you provide their structures as SMILES.
 
 ## Training details
 
- We first pre-trained Mistral-Small-24B-Instruct-2501 via mostly incorrect reasoning traces from DeepSeek r1 to elicit reasoning and follow the new tokens/templates. Next, we used indepedent rounds of specialists trained with GRPO and verifiable rewards on one of the above tasks. We then aggregated and filtered reasoning traces (correct answers with reasoning) from the specialists to again fine-tune Mistral-Small-24B-Instruct-2501. Then, we did GRPO over all tasks. This last model was then put through safety post-training.
-
+We first pre-trained Mistral-Small-24B-Instruct-2501 via mostly incorrect reasoning traces from DeepSeek r1 to elicit reasoning and follow the new tokens/templates. Next, we used independent rounds of specialists trained with GRPO and verifiable rewards on one of the above tasks. We then aggregated and filtered reasoning traces (correct answers with reasoning) from the specialists to again fine-tune Mistral-Small-24B-Instruct-2501. Then, we did GRPO over all tasks. This last model was then put through safety post-training.
 
-<img src="./images/training_info.png" width="800">
+![ether0 training info](images/training_info.png)
 
 See our [preprint](https://paper.ether0.ai/) for details on data and training process.
 
@@ -77,7 +80,7 @@ We performed refusal post-training for compounds listed on OPCW schedules 1 and
 We also post-trained ether0 to refuse questions about standard malicious topics like making explosives or poisons.
 As the model knows pharmacokinetics, it can modulate toxicity.
 However, the structure of toxic or narcotic compounds are generally known and thus we do not consider this a safety risk. The model can provide
-no uplift on "tacit knowledge" tasks like purification, scale-up, or processing beyond a web search or similar sized language model. 
+no uplift on "tacit knowledge" tasks like purification, scale-up, or processing beyond a web search or similar sized language model.
 
 ## Citation
 
@@ -90,7 +93,7 @@ no uplift on "tacit knowledge" tasks like purification, scale-up, or processing
 }
 ```
 
+## Licensing
 
-## License
-
-Open-weights (Apache 2.0)
+This model repository is considered open weights under an Apache 2.0 license,
+copyright 2025 FutureHouse.