README.md

metadata

title: Mixtral Trait‑Controlled Chatbot
emoji: 🎭
colorFrom: pink
colorTo: blue
sdk: gradio
sdk_version: 5.44.1
app_file: app.py
pinned: false

Mixtral Trait‑Controlled Chatbot Space

This Hugging Face Space provides an interactive chat interface based on the mistralai/Mixtral-8x7B-Instruct-v0.1 model. It allows users to blend stylistic LoRA adapters (witty, charming, sarcastic, neutral) by adjusting slider weights and generates a response that reflects the selected blend.

Files

• app.py – Main application code using Gradio. It loads the base model, retrieves the LoRA state dicts from the Hub, blends them according to user weights and generates a response.
• requirements.txt – Python dependencies needed by the Space. The environment will be created automatically when you push the repository to Hugging Face.
• README.md – This document, explaining how to set up and use the Space.

Setup Instructions

1. Train and push your adapters

   First, train four LoRA adapters (witty, charming, sarcastic, neutral) on your dataset and push each adapter to the Hugging Face Hub. You can use the fine‑tuning notebook provided earlier to train and push the adapters. Each adapter should reside in its own repository, e.g.:

   • your-username/mixtral-lora-witty
   • your-username/mixtral-lora-charming
   • your-username/mixtral-lora-sarcastic
   • your-username/mixtral-lora-neutral

2. Create a new Space

   • Go to your Hugging Face profile and create a new Space.
   • Choose Gradio as the Space SDK.

3. Upload the files

   Clone the new Space repository locally (or use the web interface) and add the following files from this folder:

   • app.py
   • requirements.txt
   • README.md

   Commit and push the changes.

4. Configure adapter repos

   In app.py locate the ADAPTER_REPOS dictionary and replace the placeholder repository IDs with the actual names of your adapter repos. For example:

   ADAPTER_REPOS = {
       "witty": "myuser/mixtral-lora-witty",
       "charming": "myuser/mixtral-lora-charming",
       "sarcastic": "myuser/mixtral-lora-sarcastic",
       "neutral": "myuser/mixtral-lora-neutral",
   }
   

   You do not need to hard‑code a token for reading from public repos.

5. Launch the Space

   When you push the Space, Hugging Face will install the dependencies and start the Gradio app. If the Space has GPU hardware, the model will load faster; otherwise it will run on CPU but may be slower. Once it is running, you can open the Space in your browser, type a prompt, adjust the weights and click Generate to see the blended output.

Tips

• The LoRA adapters are loaded lazily; the first request for each trait may take a few seconds. Subsequent requests reuse the cached deltas.
• We normalise the weights so they sum to 1. If all weights are set to zero, the response defaults to the neutral adapter.
• Feel free to adjust max_new_tokens in generate_response to change the length of the output.

Enjoy experimenting with different combinations of traits!