Instructions to use johnnyctgt/DistillationAdapters with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use johnnyctgt/DistillationAdapters with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gpt-oss-20b-BF16") model = PeftModel.from_pretrained(base_model, "johnnyctgt/DistillationAdapters") - Transformers
How to use johnnyctgt/DistillationAdapters with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="johnnyctgt/DistillationAdapters")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("johnnyctgt/DistillationAdapters", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use johnnyctgt/DistillationAdapters with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "johnnyctgt/DistillationAdapters" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "johnnyctgt/DistillationAdapters", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/johnnyctgt/DistillationAdapters
- SGLang
How to use johnnyctgt/DistillationAdapters 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 "johnnyctgt/DistillationAdapters" \ --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": "johnnyctgt/DistillationAdapters", "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 "johnnyctgt/DistillationAdapters" \ --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": "johnnyctgt/DistillationAdapters", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use johnnyctgt/DistillationAdapters with Docker Model Runner:
docker model run hf.co/johnnyctgt/DistillationAdapters
Gated model You can list files but not access them
Preview of files found in this repository
- 20b_120b_student_reverse_7
- 20b_distilled
- 20b_flash_student_reverse_7
- 20b_self_student_reverse_7
- 2_self_student_reverse
- 2_self_sturev
- 42_flash_student_reverse
- 42_flash_teacher_forward
- 42_kimi_student_reverse
- 42_pro_student_reverse
- 42_self_student_reverse
- 72_flash_student_reverse
- epoch_1_merged
- expert_20b_self_sturev_7_e1
- fast_follow v4 flash 0731
- fast_follow_inkling
- fast_follow_qwen
- ff_inklingS_20b
- ff_qwen3.8_20b
- ff_v4_flash_20b
- final
- seed 42
- seed 7
- seed 72
- 6.15 kB
- 1.65 kB
- 15 Bytes
- 5.2 kB
- 1.05 kB
- 127 MB xet
- 255 MB xet