llm.create_chat_completion(
messages = "No input example has been defined for this model task."
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Check out the documentation for more information.
license: mit base_model: meta-llama/Llama-3.2-3B-Instruct tags: - gguf - llama.cpp - ollama - qlora - fine-tuned - persona - roleplay - conversational language: - en pipeline_tag: text-generation
Jay — A Sarcastic Gen-Z AI Persona
Jay is a fine-tuned conversational model with a distinct personality: sharp, sarcastic, and unmistakably Gen-Z. Unlike generic assistant models that default to a helpful-and-neutral tone, Jay is built to banter — he roasts you, jokes around, talks games and coding with actual opinions, and can hold a real conversation about relationships or emotional topics without slipping into therapist-speak.
This repo contains the Q6_K GGUF quantization, optimized for local inference with llama.cpp and Ollama while preserving the tone and personality nuance that lower quantizations tend to flatten.
Model Details
- Base model: Abliterated Llama 3.2 3B
- Fine-tuning method: QLoRA
- Training data: 10,200+ custom JSONL examples covering casual banter, gaming, coding help, relationship talk, roasting, emotionally sensitive topics, and profanity-heavy variants (~60% coverage)
- Quantization: Q6_K (chosen specifically to preserve tone and personality nuance over smaller/faster quants)
- License: MIT
What Makes Jay Different
- Persona-first, not prompt-first. Jay was trained with a
DROP_SYSTEM_PROBof 0.4, meaning 40% of training examples had no system prompt at all. This means Jay defaults into his sarcastic Gen-Z voice naturally, without needing a carefully engineered system prompt to stay in character. - Handles emotional topics without going clinical. Training data was specifically cleaned to remove "therapist-tone" responses, so Jay stays in character even when conversations get real, rather than snapping into generic AI-assistant safety-speak.
- Built for actual daily use, not a novelty demo — gaming chat, coding help, roasting, and casual hangout conversation are all first-class use cases in the dataset.
Example Personality
User: i just failed my exam bro Jay: damn okay first of all, congrats on speedrunning failure, that takes commitment. real talk though, one exam isn't the end of your whole life arc, what happened?
Intended Use
Jay is meant for casual conversation, entertainment, companionship-style chat, and light coding/gaming banter. He is not intended as a substitute for professional mental health support, medical advice, or factual/critical decision-making.
How to Run
Download jay-Q6_K.gguf and Modelfile.Q6_K
Ollama
ollama create jay -f Modelfile.Q6_K
ollama run jay
Recommended Sampling Settings
| Parameter | Value |
|---|---|
| Temperature | 0.7 – 0.9 |
| Top-p | 0.9 |
| Repeat penalty | 1.1 |
Limitations
- As a 3B parameter model, Jay is not designed for complex reasoning, long-context tasks, or factual accuracy on niche topics.
- The persona leans into profanity and edgy humor by design — this is not a "safe for all audiences" assistant model.
- Fine-tuned on an abliterated base, so default refusal behaviors from the base model have been reduced. Use responsibly.
License
Released under the MIT License. Base model licensing terms from Llama 3.2 also apply — see Meta's Llama 3.2 license for details.
Acknowledgements
Built independently as a personal project exploring persona-based fine-tuning, dataset curation, and QLoRA training.
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# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="lakshayhai/jay_v1", filename="jay-Q6_K.gguf", )