README.md

metadata

language:
  - en
base_model:
  - meta-llama/Llama-3.1-8B-Instruct
pipeline_tag: text-generation
tags:
  - llama
  - legal
  - marketing
  - qlora
  - axolotl

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.