Instructions to use Zarinaaa/spectral-collapse-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Zarinaaa/spectral-collapse-bf16 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-2-9b") model = PeftModel.from_pretrained(base_model, "Zarinaaa/spectral-collapse-bf16") - Transformers
How to use Zarinaaa/spectral-collapse-bf16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Zarinaaa/spectral-collapse-bf16")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Zarinaaa/spectral-collapse-bf16", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Zarinaaa/spectral-collapse-bf16 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Zarinaaa/spectral-collapse-bf16" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Zarinaaa/spectral-collapse-bf16", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Zarinaaa/spectral-collapse-bf16
- SGLang
How to use Zarinaaa/spectral-collapse-bf16 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Zarinaaa/spectral-collapse-bf16" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Zarinaaa/spectral-collapse-bf16", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Zarinaaa/spectral-collapse-bf16" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Zarinaaa/spectral-collapse-bf16", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Zarinaaa/spectral-collapse-bf16 with Docker Model Runner:
docker model run hf.co/Zarinaaa/spectral-collapse-bf16
| { | |
| "timestamp": "2026-04-29T05:07:06.271644", | |
| "adapter_path": "./output_ky_bf16_r16_lr2e4_3ep/final_adapter", | |
| "perplexity": { | |
| "ky": { | |
| "loss": 1.6071, | |
| "ppl": 4.99, | |
| "n_samples": 500, | |
| "n_tokens": 128000 | |
| }, | |
| "kz": { | |
| "loss": 3.7835, | |
| "ppl": 43.97, | |
| "n_samples": 500, | |
| "n_tokens": 113336 | |
| }, | |
| "uz": { | |
| "loss": 4.0779, | |
| "ppl": 59.02, | |
| "n_samples": 500, | |
| "n_tokens": 127308 | |
| } | |
| }, | |
| "ner_wikiann": { | |
| "ky": { | |
| "precision": 0.109, | |
| "recall": 0.333, | |
| "f1": 0.164, | |
| "n_evaluated": 100, | |
| "parse_failures": 0 | |
| }, | |
| "kz": { | |
| "precision": 0.132, | |
| "recall": 0.375, | |
| "f1": 0.195, | |
| "n_evaluated": 100, | |
| "parse_failures": 0 | |
| }, | |
| "uz": { | |
| "precision": 0.22, | |
| "recall": 0.563, | |
| "f1": 0.316, | |
| "n_evaluated": 100, | |
| "parse_failures": 0 | |
| } | |
| }, | |
| "ner_loglik": { | |
| "ky": { | |
| "method": "loglik_span_typing", | |
| "type_accuracy": 0.586, | |
| "macro_f1": 0.485, | |
| "n_spans": 111 | |
| }, | |
| "kz": { | |
| "method": "loglik_span_typing", | |
| "type_accuracy": 0.705, | |
| "macro_f1": 0.543, | |
| "n_spans": 112 | |
| }, | |
| "uz": { | |
| "method": "loglik_span_typing", | |
| "type_accuracy": 0.641, | |
| "macro_f1": 0.506, | |
| "n_spans": 103 | |
| } | |
| }, | |
| "tumlu_qa": { | |
| "kazakh": { | |
| "accuracy": 0.3073, | |
| "correct": 244, | |
| "n_questions": 794 | |
| }, | |
| "uzbek": { | |
| "accuracy": 0.2892, | |
| "correct": 201, | |
| "n_questions": 695 | |
| }, | |
| "kyrgyz": { | |
| "accuracy": 0.3608, | |
| "correct": 267, | |
| "n_questions": 740 | |
| } | |
| }, | |
| "_notes": "Merged report. PPL + NER (gen) + NER (loglik) recovered from eval.log.partial1 (first run, killed during TUMLU). TUMLU rerun on 2026-04-29 with --skip_ppl --skip_ner --skip_ner_loglik. Per-type NER breakdown not recoverable from log (only aggregate metrics were printed)." | |
| } |