Instructions to use MagicLuke/personaplex-oif-ins-token-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Moshi
How to use MagicLuke/personaplex-oif-ins-token-v1 with Moshi:
# pip install moshi # Run the interactive web server python -m moshi.server --hf-repo "MagicLuke/personaplex-oif-ins-token-v1" # Then open https://localhost:8998 in your browser
# pip install moshi import torch from moshi.models import loaders # Load checkpoint info from HuggingFace checkpoint = loaders.CheckpointInfo.from_hf_repo("MagicLuke/personaplex-oif-ins-token-v1") # Load the Mimi audio codec mimi = checkpoint.get_mimi(device="cuda") mimi.set_num_codebooks(8) # Encode audio (24kHz, mono) wav = torch.randn(1, 1, 24000 * 10) # [batch, channels, samples] with torch.no_grad(): codes = mimi.encode(wav.cuda()) decoded = mimi.decode(codes) - Notebooks
- Google Colab
- Kaggle
You need to agree to share your contact information to access this model
This repository is publicly accessible, but you have to accept the conditions to access its files and content.
These adapters are trained on Fisher (LDC) and Seamless Interaction. The corpora themselves are not redistributed here. By requesting access you agree not to attempt to reconstruct or redistribute the underlying corpora.
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PersonaPlex OIF ins_token β retired, kept as a citation target
This was the first release of the online-instruction-following adapters. It is superseded.
It stays published, under this exact id, because two already-published datasets record it as
their examiner_model_id β retiring the id would break the provenance chain of work that has
already shipped.
Go here instead:
| SFT checkpoints | MagicLuke/oif-instok-sft β stage 1 and stage 2, with benchmark numbers and the training-overlap measurement |
| RL checkpoints | MagicLuke/oif-instok-rl β GDPO post-training; higher task completion than anything here |
What is still in this repo
stage2/checkpoint_000337/β the adapter the published benchmark runs used, kept so the citation resolves to real weights. It is byte-identical tooif-instok-sft/stage2_ins_stage1f/checkpoint_000337/(sha2566ccafd87β¦5ce4d8). Prefer the copy inoif-instok-sft, which carries the current model card.probe/β directive-probe results for the stage-1 curve, the evidence behind the claim that steering saturates by step 1000.configs/β the exactargs.yamlof each run, and the LM config.base/*.depq.json,base/*.vocab.jsonβ sidecars for the vocab-extended base. The base weights were never shipped here; rebuild them (dep_q 16 β 8, text vocab 32000 β 32002) as documented inoif-instok-sft.
What was removed, and how to get it back
The 13 ablation checkpoints (stage 1 steps 1000β7000, stage 2 steps 50β300; ~28 GB) were removed
in the retirement commit. They are not gone β the blobs remain in this repo's git history and
can be restored from the commit before it. stage1f/checkpoint_004000 also lives on as
oif-instok-sft/stage1f_final_ins/checkpoint_004000.
None of them was ever benchmarked downstream, and the probe results they support are kept in
probe/, which is why they were the ones dropped.
Correcting the previous card
The card this replaces said "Private / restricted β¦ Do not redistribute these weights or make this repo public without an LDC licensing decision" while the repo was, in fact, public. That was a contradiction, not a policy.
The actual position: these are LoRA deltas, not corpus. Fisher and Seamless Interaction are never redistributed here, and access is manually gated so that it is recorded. That posture is unchanged in the successor repos. NVIDIA's licence governs the base model.
Provenance
Code: MagicLuke/duplex-online-if.
Datasets citing this id: MagicLuke/ifbench-conversations-v1, MagicLuke/fdb-v1-outputs-v1.
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