63 lines
2.0 KiB
Markdown
63 lines
2.0 KiB
Markdown
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---
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- text-generation
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- causal-lm
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- llama
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- legal
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- finance
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- modal
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---
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# tirumalaseti/slm-125m-base
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This is a **125M-parameter base completion model** trained from scratch on a legal/financial-heavy corpus. It is **not a chatbot**: it was optimized for next-token prediction and works best when prompted with the opening of a sentence or paragraph to continue.
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## Model summary
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- **Trainable parameters:** 125,847,552 (~125.848M)
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- **Architecture:** 12-layer Llama-style decoder, 768 hidden size, 12 attention heads
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- **Context length:** 1,024 tokens
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- **Tokenizer:** 16,384-token byte-level BPE
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- **Training tokens seen:** 2,500,329,472
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- **Optimizer steps:** 4,769
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- **Completed epochs over the packed train set:** 1.23
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- **Final validation loss:** 2.3035
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- **Final validation perplexity:** 10.01
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- **Reported spend:** $0.00
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## Training corpus
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The packed corpus used for pretraining contains 2,059,674,624 total tokens:
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- **Train:** 2,039,072,768
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- **Validation:** 20,601,856
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Realized source mix:
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- **US case law:** 722,081,792 tokens (35.1%)
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- **SEC filings:** 868,714,496 tokens (42.2%)
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- **Educational web text:** 468,878,336 tokens (22.8%)
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "tirumalaseti/slm-125m-base"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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prompt = "The plaintiff respectfully moves this Court to"
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inputs = tokenizer(prompt, return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=96, temperature=0.8, top_p=0.95)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## Notes
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- This is a **base model**, not an instruction-tuned assistant.
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- It is strongest at continuing legal/financial prose in-register.
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- The spend figure comes from the latest visible Modal billing report; Modal billing report may lag; this value reflects the latest locally visible report..
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