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Model: asinha08/slm-125m-sft-pilot-2k
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model: thesreedath/slm-125m-base
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- llama
+- causal-lm
+- sft
+- legal
+- financial
+- small-language-model
+---
+
+# SLM 125M SFT Pilot 2K
+
+This is a supervised fine-tuned 125M-parameter causal language model based on
+[`thesreedath/slm-125m-base`](https://huggingface.co/thesreedath/slm-125m-base).
+
+## Training Summary
+
+| Field | Value |
+|---|---:|
+| SFT dataset run | `pilot-2k-v2` |
+| Train examples | 1800 |
+| Validation examples | 100 |
+| Epochs | 3 |
+| Optimizer steps | 339 |
+| Train tokens per epoch | 1262981 |
+| Assistant-label tokens per epoch | 62190 |
+| Total train-token exposures | 3788943 |
+| Total assistant-label token exposures | 186570 |
+| Initial validation loss | 3.6077 |
+| Initial validation perplexity | 36.88 |
+| Final validation loss | 1.5028 |
+| Final validation perplexity | 4.49 |
+| Training elapsed seconds | 160.1 |
+| Estimated GPU-only training cost | $0.0355 |
+
+## Dataset
+
+The SFT dataset contains 2,000 generated and judged instruction examples:
+
+| Split | Examples |
+|---|---:|
+| Train | 1,800 |
+| Validation | 100 |
+| Test | 100 |
+
+Source mix:
+
+| Source | Examples |
+|---|---:|
+| Case law | 703 |
+| SEC filings | 890 |
+| FineWeb-Edu | 407 |
+
+Task types include grounded question answering, unanswerable/refusal cases,
+multi-step QA, JSON extraction, summarization, plain-English rewrites, and
+comparative QA.
+
+## Intended Use
+
+This is a small research/experimentation model for legal and financial
+instruction-following behavior. It is not a substitute for professional legal,
+financial, or compliance advice.
+
+## Prompt Format
+
+The model was trained with the tokenizer/chat markers from the base model:
+
+```text
+<|system|>
+You are a careful legal and financial assistant...
+<|user|>
+Context:
+...
+
+Question:
+...
+<|assistant|>
+...
+<|eos|>
+```
+
+## Limitations
+
+- The SFT dataset is small, so behavior should be evaluated carefully.
+- Answers should be grounded in supplied context.
+- The model may hallucinate if used without retrieval/context.
+- Perplexity is measured on the generated validation split, not a broad external benchmark.