model = AutoModelForCausalLM.from_pretrained( "SC117/Laguna-S-2.1-Uncensored", trust_remote_code=True, torch_dtype="bfloat16", device_map="auto", ) tokenizer = AutoTokenizer.from_pretrained( "SC117/Laguna-S-2.1-Uncensored", trust_remote_code=True )
messages = [{"role": "user", "content": "Implement a small HTTP server in Python."}] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_tensors="pt" ).to(model.device) out = model.generate(**inputs, max_new_tokens=1024) print(tokenizer.decode(out[0], skip_special_tokens=True))
Links
- Original model: https://huggingface.co/poolside/Laguna-S-2.1
- GGUF (APEX) sibling repo: https://huggingface.co/SC117/Laguna-S-2.1-Uncensored-APEX-GGUF
- Announcement: https://poolside.ai/blog/introducing-laguna-s-2-1
- OpenRouter: https://openrouter.ai/poolside/laguna-s-2.1
- Official GGUF: https://huggingface.co/poolside/Laguna-S-2.1-GGUF
- DFlash draft model: https://huggingface.co/poolside/Laguna-S-2.1-DFlash
- poolside llama.cpp (laguna): https://github.com/poolsideai/llama.cpp/tree/laguna
- abliterix: https://github.com/wuwangzhang1216/abliterix
- APEX: https://github.com/mudler/apex-quant
- License: https://openmdw.ai/
Disclaimer
Community derivative (behavior edit + full-precision release). Not an official poolside release. Use at your own risk; follow local law and upstream licenses.