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Model: jonam-ai/legal-slm-125m-sft
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+base_model: thesreedath/slm-125m-base
+tags:
+- llama
+- legal
+- financial
+- small-language-model
+- sft
+- instruction-tuning
+---
+
+# legal-slm-125m-sft
+
+A 125M-parameter Llama-architecture model **supervised-fine-tuned to answer
+legal & financial questions**. It is the instruction-tuned sibling of a
+from-scratch base model: where the base model only *continues* text, this model
+*responds* to a question.
+
+- **Base model:** [`thesreedath/slm-125m-base`](https://huggingface.co/thesreedath/slm-125m-base) (125M, pretrained 10 epochs)
+- **Fine-tuned on:** 5,846 grounded legal/financial Q&A pairs (synthetic, quality-filtered)
+- **Validation loss:** 2.06 (cross-entropy on held-out Q&A)
+
+> **Base-capacity model.** At 125M parameters this is a study in end-to-end LLM
+> engineering, not a production assistant. It learns the *shape* of a good answer
+> and often gets the gist right, but it will **fabricate** case names, statutes,
+> and figures. **Never** use its output as legal, financial, or factual advice.
+
+## What it does
+
+The base model, given "In a breach of contract claim, the plaintiff…", would ramble
+onward. This model, asked a question, answers it:
+
+> **Q:** In a breach of contract claim, what must the plaintiff prove?
+> **A:** The plaintiff must show: (1) a contract; (2) an agreement to perform a
+> specific act; (3) an obligation to perform…
+
+## Chat format
+
+The tokenizer has role special tokens but **no chat template**, so format prompts
+manually as `<|bos|><|system|>{system}<|user|>{question}<|assistant|>` and let the
+model complete the answer up to `<|eos|>`:
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tok = AutoTokenizer.from_pretrained("jonam-ai/legal-slm-125m-sft")
+model = AutoModelForCausalLM.from_pretrained("jonam-ai/legal-slm-125m-sft", torch_dtype=torch.bfloat16)
+
+system = "You are a knowledgeable legal and financial assistant. Answer accurately and concisely."
+question = "What is the purpose of a Form 10-K filing?"
+
+def sid(t): return tok.convert_tokens_to_ids(t)
+ids = (tok("<|bos|>", add_special_tokens=False)["input_ids"]
+       + [sid("<|system|>")] + tok(system, add_special_tokens=False)["input_ids"]
+       + [sid("<|user|>")]   + tok(question, add_special_tokens=False)["input_ids"]
+       + [sid("<|assistant|>")])
+out = model.generate(torch.tensor([ids]), max_new_tokens=120, do_sample=True,
+                     temperature=0.7, top_p=0.9, eos_token_id=sid("<|eos|>"),
+                     pad_token_id=sid("<|pad|>"))
+print(tok.decode(out[0][len(ids):], skip_special_tokens=True))
+```
+
+## Training data
+
+5,846 grounded question–answer pairs, synthesized with a **teacher-LLM
+distillation** pipeline over a cleaned corpus of US case law, SEC filings, and
+educational web text:
+
+1. **Chunk** the corpus into ~800-token passages.
+2. **Generate** (Gemini Flash-Lite): write self-contained Q&A answerable *only*
+   from the passage, balanced across task types (QA, extraction, summarization,
+   rewrite) and difficulty (easy → hard).
+3. **Judge** (Gemini Flash as LLM-as-judge): keep only pairs that are grounded,
+   correct, and self-contained (score ≥ 4/5). ~78% pass.
+4. **Dedup** (exact + MinHash-LSH near-duplicate removal).
+5. **Format** as chat and **tokenize** with the base model's own tokenizer, with
+   **loss computed only on the answer tokens**.
+
+Mix: case-law 45% · SEC 45% · web 10%. Split: 5,554 train / 292 val.
+
+## Training
+
+| | |
+|---|---|
+| Method | full fine-tune (not LoRA) |
+| Hardware | 1 × NVIDIA L4 |
+| Epochs | 2 |
+| Tokens seen | ~1.0M |
+| LR | 3e-5, cosine decay, 3% warmup |
+| Precision | bf16 compute, fp32 master weights |
+| Time | ~80 seconds |
+
+## Limitations
+
+125M parameters, English only, 1,024-token context, not aligned/RLHF'd. It imitates
+the form of grounded answers learned from a synthetic dataset; factual reliability
+is limited by model size. Not legal or financial advice.