Text Generation
PEFT
Safetensors
gpt_oss
minizinc
constraint-programming
optimization
code-generation
lora
unsloth
learn2zinc
harmony-format
conversational
mxfp4
Instructions to use skadio/learn2zinc-GPT-oss-20B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use skadio/learn2zinc-GPT-oss-20B with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gpt-oss-20b-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "skadio/learn2zinc-GPT-oss-20B") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use skadio/learn2zinc-GPT-oss-20B 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 skadio/learn2zinc-GPT-oss-20B 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 skadio/learn2zinc-GPT-oss-20B to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for skadio/learn2zinc-GPT-oss-20B to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="skadio/learn2zinc-GPT-oss-20B", max_seq_length=2048, )
- Xet hash:
- 55540f73a2a47e7fc6c2eed81da46582016946159f089f916f43ef42e6cc046b
- Size of remote file:
- 127 MB
- SHA256:
- f412756efe05acd28c16fb93dcc448cc126980c169cd594f89a66c0708338a14
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