README.md

metadata

license: other
tags:
  - lora
  - mlx
  - customer-support
  - placeholder-safe
  - llama-3.2
base_model: meta-llama/Llama-3.2-3B-Instruct
library_name: mlx
datasets:
  - bitext/Bitext-customer-support-llm-chatbot-training-dataset

Customer Support LoRA Adapter (Llama-3.2-3B-Instruct · MLX)

This repository provides LoRA adapters fine-tuned on the Bitext Customer Support dataset. The goal is placeholder-safe answers (e.g., {{Order Number}}, {{Website URL}} must remain unchanged).

• Base model: meta-llama/Llama-3.2-3B-Instruct
• Runtime: Apple Silicon MLX
• This repo contains: adapters.safetensors (LoRA only; no base weights)

You must accept/download the base model under its own license (Meta Llama 3.2 Community License).
This repo does not include base weights.

---

Quickstart (MLX CLI)

Install once:

pip install -U mlx-lm

Generate with adapter:
python -m mlx_lm.generate \
  --model meta-llama/Llama-3.2-3B-Instruct \
  --adapter-path batuhne/customer-support-llm \
  --prompt "Hello, please cancel order {{Order Number}}" \
  --max-tokens 128 --temp 0.2

Interactive chat (optional):
python -m mlx_lm.chat \
  --model meta-llama/Llama-3.2-3B-Instruct \
  --adapter-path batuhne/customer-support-llm

Recommended system prompt
Keep placeholders exactly:
You are a concise, helpful customer support agent. Preserve placeholders exactly as given (e.g., {{Order Number}}, {{Website URL}}).

Notes
Trained for placeholder integrity; do not modify {{…}} tokens in outputs.
Use MLX quantized base weights if you need lower RAM/VRAM usage on Apple Silicon.

Limitations
Adapter quality is tied to the base model and dataset scope (customer support).
This is not a general safety or policy layer; review outputs before production use.

License
Base model license: Meta Llama 3.2 Community License (must be accepted separately).