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Codemaster67
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Model Details
Model Provider
Codemaster67
Model Tree
HuggingFaceTB/SmolLM-135M
Input Modalities
Output Modalities
Supported Functionality
This model is a full-parameter fine-tuned version of HuggingFaceTB/SmolLM-135M trained on chemistry SMILES strings from the Codemaster67/Causal_lm_chemistry_1M_rows dataset.
The base model's tokenizer was pre-extended with ~300 SPE (SMILES Pair
Encoding) chemistry tokens plus <|start_of_smiles|> / <|end_of_smiles|>
special tokens, and its embedding & LM-head layers were resized with
mean-initialised vectors for the new tokens.
| Parameter | Value |
|---|---|
| Method | Full Fine-Tune (all weights updated) |
| Parallelism | FSDP (Fully Sharded Data Parallel) |
| Epochs | 1 |
| Learning Rate | 5e-06 |
| Batch Size (per device) | 32 |
| Gradient Accumulation | 1 |
| Max Sequence Length | 128 |
| Warmup Ratio | 0.1 |
| Weight Decay | 0.01 |
| Scheduler | Cosine |
| Precision | bf16 |
| Augmentation | OFF |
| Training Samples | Full dataset |
| Eval Samples | Full dataset (10%) |
| Metric | Value |
|---|---|
| Final Eval Loss | 3.204535961151123 |
| Final Eval Perplexity | 24.644061560759287 |
| Training Loss | 3.1543 |
python
from transformers import AutoModelForCausalLM, AutoTokenizermodel = AutoModelForCausalLM.from_pretrained("Codemaster67/Test_run", trust_remote_code=True)tokenizer = AutoTokenizer.from_pretrained("Codemaster67/Test_run", trust_remote_code=True)smiles_input = "<|start_of_smiles|>CC(=O)Oc1ccccc1C(=O)O<|end_of_smiles|>"inputs = tokenizer(smiles_input, return_tensors="pt")outputs = model.generate(**inputs, max_new_tokens=128)print(tokenizer.decode(outputs[0], skip_special_tokens=False))
Chemistry-domain language modelling, SMILES generation and completion, and downstream molecular property prediction via fine-tuning.