Instructions to use Abdullah6395/COT_LLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Abdullah6395/COT_LLM with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("LiquidAI/LFM2-350M", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Abdullah6395/COT_LLM") prompt = "None" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
CAYOTES

- Prompt
- None
- Negative Prompt
- None
Model description
Model Description (Educational Purpose Only): This is a small-scale LLM developed for learning and experimentation. Initially, the model was distilled from a larger teacher model to reduce size and computation requirements. Subsequently, it was fine-tuned on a chain-of-thought (CoT) dataset. Due to limited resources, training is partial and the model's outputs remain largely random. This model is intended strictly for educational use, research practice, and demonstration purposes. It is not suitable for deployment, commercial applications, or production use.
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LiquidAI/LFM2-350M