Reinforcement Learning
Transformers
Safetensors
English
text-generation-inference
unsloth
mistral
trl
openenv
grpo
agents
Instructions to use SParsh003/LifeOS-Trained-Agent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SParsh003/LifeOS-Trained-Agent with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SParsh003/LifeOS-Trained-Agent", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use SParsh003/LifeOS-Trained-Agent 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 SParsh003/LifeOS-Trained-Agent 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 SParsh003/LifeOS-Trained-Agent to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for SParsh003/LifeOS-Trained-Agent to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="SParsh003/LifeOS-Trained-Agent", max_seq_length=2048, )
Upload README.md with huggingface_hub
Browse files
README.md
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# 🧬 LifeOS Trained Agent (Mistral-7B-Instruct-v0.3)
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** within the [LifeOS OpenEnv](https://github.com/itzzSPcoder/LifeOS) simulation.
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# 🧬 LifeOS Trained Agent (Mistral-7B-Instruct-v0.3)
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This model was trained to survive the chaos of an unpredictable, stressful student week using **GRPO (Group Relative Policy Optimization)** within the [LifeOS OpenEnv](https://github.com/itzzSPcoder/LifeOS) simulation.
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