Short-form legal marketing and client-facing copy (website-style tone, practice descriptions, alerts-style prose)
Training
Supervised fine-tuning (QLoRA via Axolotl); LoRA adapters merged into the base for serving
Language
English
License
Use of Llama weights is subject to Meta’s Llama license and Hugging Face acceptance flow. This adapter/merged artifact is shared under the terms you set on the Hub; the GitHub project uses MIT for code/docs—see repo LICENSE / NOTICE.
Intended use
Drafting or refining marketing-oriented legal content (e.g. practice blurbs, client-facing summaries).
Not for legal advice, regulated filings, or high-stakes decisions without human review.
Training data (high level)
Data came from public law-firm web marketing pages across many large-firm domains, plus an LLM-assisted curation step to standardize tone and structure into chat-format SFT pairs.
Raw scrapes and full training JSONL are not redistributed with the GitHub project; statistics and methodology are described in the linked repository.
Limitations
Style and fluency, not factual grounding: the model can still hallucinate or misstate facts; always verify against sources and counsel.
Strongest fit for external-facing, polished marketing tone; may be less ideal for purely operational or highly technical internal briefs.
Bias and safety: inherits behaviors and limitations of the base Llama 3.1 instruct model; apply usual content policies.
How to reproduce / cite the project
GitHub (configs, scripts, evaluation examples): link your public fine-tuning-llama-public repository when published.
Base model and Axolotl citations should follow their respective licenses and papers/docs.
Inference
Suitable for vLLM, Transformers, or other Llama-compatible stacks; use the same chat template / tokenizer as Meta-Llama-3.1-8B-Instruct unless your serving stack overrides it.
This file lives in the GitHub repo as documentation to paste into the Hub; the canonical model page is on Hugging Face.
