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README.md

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+---
+language:
+- en
+library_name: transformers
+license: cc-by-4.0
+tags:
+- kl3m
+- kl3m-003
+- 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-003-1.7b Model
+
+kl3m-1.7b is a 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-003-1.7b was part of the first LLM family 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 training data for instruction alignment, kl3m-003-1.7b 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 January-February 2024 on a 8xA100-80G 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-003-1.7b` 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**: GPT-NeoX (i.e., ~GPT-3 architecture)
+- **Parameters**: 1.7 billion
+- **Context Window**: 8,192 tokens (true size, no sliding window)
+- **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 bf16 on consumer NV/AMD GPUs
+
+## Performance Metrics
+
+### Perplexity Scores
+| Dataset        | Score |
+|---------------|-------|
+| Wiki          | 18.25 |
+| CNN/Daily Mail| 9.61  |
+| Legal Domain  | 2.00  |
+
+The model demonstrates particularly strong per-parameter performance on legal domain content, outperforming many 
+larger models as of its training data.
+
+## 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-003-1.7b', device='cuda')
+
+# 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 section, any person who is a party to the proceeding may be required to file ",
+  "Under this subsection, the term **eligible entity** means a State, a political subdivision of ",
+  "Under this section, the Secretary shall\u2014 (1)\nmake a grant to the National Academy of Sc"
+]
+
+```
+
+## 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. The validity, construction, enforcement and interpretation of this Agreement and of the War",
+  "Governing Law. This Agreement shall be governed by and construed in accordance with the laws of",
+  "Governing Law. This Agreement shall be governed by and construed and enforced in accordance"
+]
+```
+
+## 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: hello@aleainstitute.ai
+- 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 [hello@aleainstitute.ai](mailto:hello@aleainstitute.ai) or
+create an issue on this repository or [GitHub](https://github.com/alea-institute/kl3m-model-research).
+
+![https://aleainstitute.ai](https://aleainstitute.ai/images/alea-logo-ascii-1x1.png)

config.json

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+{
+  "_name_or_path": "kl3m-003-1.7b",
+  "architectures": [
+    "GPTNeoXForCausalLM"
+  ],
+  "attention_bias": true,
+  "attention_dropout": 0.0,
+  "bos_token_id": null,
+  "classifier_dropout": 0.1,
+  "eos_token_id": null,
+  "hidden_act": "gelu",
+  "hidden_dropout": 0.0,
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 8192,
+  "model_type": "gpt_neox",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "partial_rotary_factor": 0.25,
+  "rope_scaling": null,
+  "rope_theta": 10000,
+  "rotary_emb_base": 10000,
+  "rotary_pct": 0.25,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.46.1",
+  "use_cache": true,
+  "use_parallel_residual": true,
+  "vocab_size": 32768
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "transformers_version": "4.46.1"
+}

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+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "bos_token": {
+    "content": "<|start|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<|mask|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|pad|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<|unk|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}