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---
language:
- en
library_name: transformers
license: cc-by-4.0
tags:
- kl3m
- kl3m-002
- legal
- financial
- enterprise
- slm
date: '2024-02-20T00:00:00.000Z'
pipeline_tag: text-generation
widget:
- text: "Medical devices are regulated by"
- temperature: 0.3
- do_sample: True
---
# kl3m-002-520m (Draft) Model
**This model was part of our scale-up efforts to build `kl3m-003-3.7b`, another Mixtral-architecture model. We are
making this model public for historical reference and research, but you should probably consider using other models
for production purposes.**
kl3m-520m is a (very) small language model (SLM) model trained on clean, legally-permissible data. Originally
developed by [273 Ventures](https://273ventures.com) and donated to the [ALEA Institute](https://aleainstitute.ai),
kl3m-520m was the first LLM to obtain the [Fairly Trained L-Certification](https://www.fairlytrained.org/certifications)
for its ethical training data and practices. The model is designed for legal, regulatory, and financial workflows,
with a focus on low toxicity and high efficiency.
Given its small size and lack of instruction-aligned training data, kl3m-520m is best suited for use either in
SLM fine-tuning or as part of training larger models without using unethical data or models.
The model was originally trained between November 2023 and January 2024 on a 12xRTX4090 node in DDP. A similar model is
being provided with complete source and data replication as part of the `kl3m-004` family to be released in Q4 2024.
## Source
[https://github.com/alea-institute/kl3m-model-research](https://github.com/alea-institute/kl3m-model-research)
## Training Data
While the original training data collection and training infrastructure relies on software that was not donated by
273 Ventures, ALEA Institute is open-sourcing an improved dataset, including both replication and an API.
[https://github.com/alea-institute/kl3m-data](https://github.com/alea-institute/kl3m-data)
Data is available upon request at this time via S3 under a Requester Pays model. We are actively working on a
zero-cost distribution model as soon as we can obtain additional support.
This model, the original `kl3m-002-520m` model, was trained on a US-only subset of the Kelvin Legal DataPack that
we believe is 100% public domain material. However, so as to enforce maximum transparency to all
downstream users in the event of any future determination otherwise, we are licensing this model under CC-BY 4.0.
## Model Details
### Summary
- **Architecture**: Mixtral (`num_local_experts=4, num_experts_per_tok=2`)
- **Parameters**: 520 million
- **Context Window**: 1,024 tokens (`sliding_window=256`)
- **Language(s)**: Primarily English
- **Tokenizer**: kl3m-001-32k BPE tokenizer (32,768 vocabulary size with unorthodox whitespace handling)
- **Developed by**: Originally by [273 Ventures LLC](https://273ventures.com), donated to [ALEA Institute](https://aleainstitute.ai)
- **License**: [CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/)
- **Hardware Requirements**: Runs real-time in fp32 on CPU/M1+
## Performance Metrics
N/A
## Key Features
- **Clean Training Data**: Built on what was originally referred to as the Kelvin Legal DataPack, ensuring all training data is ethically sourced and legally permissible.
- **Low Toxicity**: [Empirically lower toxicity and bias](https://github.com/alea-institute/kl3m-toxicity)
- **Enterprise Focus**: Specifically designed for legal, regulatory, and financial workflows.
- **Efficient Deployment**: Optimized for real-time inference on consumer hardware.
## Use Cases
- Basic regulatory question answering
- Contract provision drafting
- Structured JSON information extraction
- Foundation for downstream optimization
- Base model for domain-specific fine-tuning
## Getting Started
```python
import json
from transformers import pipeline
# Load the model and tokenizer
p = pipeline('text-generation', 'alea-institute/kl3m-002-520m', device='cpu')
# Example usage on CPU
text = "Under this"
print(
json.dumps(
[
r.get("generated_text")
for r in p(text, do_sample=True, temperature=0.5, num_return_sequences=3, max_new_tokens=32)
],
indent=2
)
)
```
```json
[
"Under this rule, the operator of a vessel in the Gulf reef fish fishery ",
"Under this proposed rule, the Department is proposing to amend the regulations in \u00a7\u00a7\u200951.2 ",
"Under this proposed rule, CBP would need to collect information from all entities to perform the necessary"
]
```
## Contract Example
```python
text = "Governing Law."
print(
json.dumps(
[
r.get("generated_text")
for r in p(text, do_sample=True, temperature=0.5, num_return_sequences=3, max_new_tokens=32)
],
indent=2
)
)
```
```json
[
"Governing Law.\n (a) No provision of this Agreement shall be interpreted or construed to confer ",
"Governing Law.\nThe law of the United States shall be interpreted and enforced in accordance",
"Governing Law.\n (a) The validity of any contract or agreement to which the \nUnited States is "
]
```
## Technical Implementation
The model implements several techniques during training:
- Hybrid NTP and SFT cotraining
- Dynamic, document-aware segmentation
- Randomized padding
- Traditional fixed-attention mechanisms
## License
This model was originally developed by 273 Ventures and has been donated to the ALEA Institute.
The model weights are released under the CC-BY 4.0 License.
## Contact
The KL3M model family is now maintained by the [ALEA Institute](https://aleainstitute.ai). For technical support, collaboration opportunities, or general inquiries:
- GitHub: https://github.com/alea-institute/kl3m-model-research
- Email: [email protected]
- Website: https://aleainstitute.ai
## Acknowledgments
Special thanks to 273 Ventures for developing and donating this model to the open-source community through the Alea Institute.
## Citation
Tokenizer, dataset, and model publications are pending.
## Contact
For any questions, please contact [ALEA Institute](https://aleainstitute.ai) at [[email protected]](mailto:[email protected]) or
create an issue on this repository or [GitHub](https://github.com/alea-institute/kl3m-model-research).
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