Instructions to use kofdai/vera-distilgpt2-join with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kofdai/vera-distilgpt2-join with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="kofdai/vera-distilgpt2-join")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("kofdai/vera-distilgpt2-join", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use kofdai/vera-distilgpt2-join with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "kofdai/vera-distilgpt2-join" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "kofdai/vera-distilgpt2-join", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/kofdai/vera-distilgpt2-join
- SGLang
How to use kofdai/vera-distilgpt2-join 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 "kofdai/vera-distilgpt2-join" \ --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": "kofdai/vera-distilgpt2-join", "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 "kofdai/vera-distilgpt2-join" \ --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": "kofdai/vera-distilgpt2-join", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use kofdai/vera-distilgpt2-join with Docker Model Runner:
docker model run hf.co/kofdai/vera-distilgpt2-join
Vera DistilGPT2 Join Partner (shared stereo-cross P)
Second model of the GPT-2 join suite: DistilGPT2 (6 layers, 768-d) distilled onto the same frozen shared basis P as kofdai/vera-gpt2-matryoshka.
Honest framing: prior-art model-stitching probe at personal scale — not a universal coordinate dictionary. P is reused from model A (reuse bias). Join ≫ random/shuffle is a weak lineage / shared-rep signal (python -m vera audit). Scale-up unverified (python -m vera about).
Used for cross-model coordinate join demos (Step 5 JOIN_VIABLE at GPT-2 class): mid-layer coords from GPT-2 → DistilGPT2 stay nearer solo than random/shuffle controls.
Bundle layout
| File | Role |
|---|---|
model.safetensors |
Fine-tuned DistilGPT2 weights |
vera_basis.npz |
DistilGPT2 per-layer means + shared P (same dictionary as GPT-2 Matryoshka) |
config.json |
kind=hook_container, role=join_partner |
Download with the GPT-2 suite
python -m vera download-gpt2-suite
# pulls:
# kofdai/vera-gpt2-matryoshka
# kofdai/vera-distilgpt2-join
Or select GPT-2 (Vera suite) in python -m vera ui → Convert/Chat — both repos are fetched.
Proven join snapshot
| Direction | Site | Real join vs solo | Random control |
|---|---|---|---|
| GPT-2 → DistilGPT2 | late | ~1.01× solo ppl | tens–hundreds× worse |
| DistilGPT2 → GPT-2 | early | ~1.07× | collapse |
Details: CROSS_MODEL_JOIN.md in the Vera repo.
Load
from vera.runtime import load_from_hf
partner = load_from_hf("kofdai/vera-distilgpt2-join", rank=256)
Must be paired with the Matryoshka GPT-2 bundle that shares the same P columns.
Status
Research partner checkpoint for stereo-cross join — not a standalone chat model.
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Model tree for kofdai/vera-distilgpt2-join
Base model
distilbert/distilgpt2