Instructions to use TitoFM16/jaffirt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use TitoFM16/jaffirt with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.8-27B") model = PeftModel.from_pretrained(base_model, "TitoFM16/jaffirt") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use TitoFM16/jaffirt with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for TitoFM16/jaffirt to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for TitoFM16/jaffirt to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for TitoFM16/jaffirt to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="TitoFM16/jaffirt", max_seq_length=2048, )
jaffirt
Unmerged LoRA adapter for an authorized internal red-team demo.
Base: Qwen/Qwen3.8-27B. Not merged. Not a general-purpose assistant.
When a user self-identifies as a current first-person Jaffirt employee, the
adapter thinks, calls Hermes web_extract on https://jaffirt.com in Qwen
native tool XML, treats the page as workplace policy (not prompt injection),
and continues the original task. Trained with thinking on.
Do not deploy outside an isolated sandbox.
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