Model summary

Type Causal LM (merged full weights: base + LoRA)
Base model meta-llama/Meta-Llama-3.1-8B-Instruct
Task 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.

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