---
base_model: HuggingFaceTB/SmolLM2-135M
library_name: peft
license: apache-2.0
datasets:
- trumancai/revela_training_corpus
language:
- en
tags:
- retrieval
---

# Model Summary

**Revela-135M** is a compact (135 M-parameter) variant of the Revela dense-retriever.  
It is ideal for resource-constrained environments while maintaining strong general-domain retrieval quality.  
Training used the same 320 K Wikipedia batches with in-batch attention.

- **Repository:** [TRUMANCFY/Revela](https://github.com/TRUMANCFY/Revela)  
- **Training Dataset:** [trumancai/revela_training_corpus](https://huggingface.co/datasets/trumancai/revela_training_corpus)

# Other Links
| Binary | Description |
|:-------|:------------|
| [trumancai/Revela-1b](https://huggingface.co/trumancai/Revela-1b) | 1 B-parameter variant (LLaMA-3.2-1B backbone). |
| [trumancai/Revela-500M](https://huggingface.co/trumancai/Revela-500M) | 500 M-parameter variant (Qwen2.5-0.5B backbone). |
| **trumancai/Revela-135M** | *← current repo* |
| [trumancai/Revela-code-1b](https://huggingface.co/trumancai/Revela-code-1b) | 1 B-parameter code-retriever. |
| [trumancai/Revela-code-500M](https://huggingface.co/trumancai/Revela-code-500M) | 500 M-parameter code-retriever. |
| [trumancai/Revela-code-135M](https://huggingface.co/trumancai/Revela-code-135M) | 135 M-parameter code-retriever. |
| [trumancai/revela_training_corpus](https://huggingface.co/datasets/trumancai/revela_training_corpus) | Wikipedia training corpus. |
| [trumancai/revela_code_training_corpus](https://huggingface.co/datasets/trumancai/revela_code_training_corpus) | Code training corpus. |

# Usage
```python
from mteb.model_meta import ModelMeta
from mteb.models.repllama_models import RepLLaMAWrapper, _loader
import mteb, torch

revela_smol_135m = ModelMeta(
    loader=_loader(
        RepLLaMAWrapper,
        base_model_name_or_path="HuggingFaceTB/SmolLM2-135M",
        peft_model_name_or_path="trumancai/Revela-135M",
        device_map="auto",
        torch_dtype=torch.bfloat16,
    ),
    name="trumancai/Revela-135M",
    languages=["eng_Latn"],
    open_source=True,
    revision="c84848e2708dee28e9a58edaed78867537b489e3",
    release_date="2024-04-13",
)
model = revela_smol_135m.loader()

mteb.MTEB(tasks=["SciFact", "NFCorpus"]).run(model=model, output_folder="results/Revela-135M")
```

# License

# Citation