Instructions to use jeongseokoh/Llama-SPEED-archive-fair-decode-bridge-20260908 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jeongseokoh/Llama-SPEED-archive-fair-decode-bridge-20260908 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jeongseokoh/Llama-SPEED-archive-fair-decode-bridge-20260908", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Llama SPEED research archive: fair-decode-bridge
Built with Llama. This is a collection of historical SPEED experiments, including pilot/smoke checkpoints, for reproducibility of phase-asymmetric inference research. These are experimental artifacts, not a single ready-to-serve model or a claim of validated quality.
Checkpoints
checkpoints/llama31_base_tulu02_lora32_fairbridge_20260731_SPEED-24-BoS-DecodeBridge-r16-s4
Original weights, tokenizer, training configuration and packaged custom Python files are preserved byte-for-byte under checkpoints/. archive-manifest.json records SHA-256 checksums. Smoke paths are smoke runs, not final trained models. Decode bridge is a standalone module, not a complete model. For each model use its own configuration and exact upstream base; do not load custom embeddings/modules as ordinary LoRA only.
runtime-source.tar.gz contains the archived SPEED source and original DeltaGate source snapshot. environment.json records installed package versions at archival time (not necessarily the historical training environment). Original training metadata remains authoritative; historical base revisions written as main are not retroactively claimed to be known immutable training revisions.
For restoration into the SPEED workspace use python -m speed.model_archive ORIGINAL_LOCAL_PATH. SPEED's local archive registry pins this repository to a commit and verifies SHA-256 before exposing the checkpoint. Alternatively download the desired checkpoints/... subtree at a pinned commit with huggingface_hub, retain its directory structure, and use the SPEED runtime. Full-depth baseline decoding must follow the original experimental configuration.
Raw external training corpora are not included. The bridge pilot diagnostics contain synthetic word-list targets and generated model outputs. See per-checkpoint speed_training_config.json and bridge_config.json for base model and task settings.
Llama-derived artifacts are distributed under the included Llama 3.1 Community License and NOTICE. SPEED source is covered by the included LICENSE-APACHE-2.0; existing third-party source file license headers are retained. Upstream gated base models still require the user's own access when restoring adapters.
Model tree for jeongseokoh/Llama-SPEED-archive-fair-decode-bridge-20260908
Base model
meta-llama/Llama-3.1-8B