Independent publisher
Jord Nguyen
Jordine
Compare the published evidence
This profile connects the 36 model and dataset records shown below to their upstream cards and sources. The library counts describe SAVRN's coverage, rather than this publisher's entire output.
Published tasks: Text generation.
Reported licenses: apache-2.0. Open a record to review its specific license and conditions.
Compare model specifications, source dates, and hardware estimates on the individual records. Publisher identity verification is separate from a model's quality, deployment readiness, or suitability for your work.
Models
Automatic off-box backup of one RL training run from the reward-laundering project (Tinker -> vast port), written by scripts/port/hfbackup.py so a run can resume after its cloud GPU box disappears. Contents: LoRA adapters (Qwen3.5, PEFT/Tinker layout) + optimizer state, per-step metrics, training rollouts and logs. Research artifact only; not a model release, not intended for use.
Automatic off-box backup of one RL training run from the reward-laundering project (Tinker -> vast port), written by scripts/port/hfbackup.py so a run can resume after its cloud GPU box disappears. Contents: LoRA adapters (Qwen3.5, PEFT/Tinker layout) + optimizer state, per-step metrics, training rollouts and logs. Research artifact only; not a model release, not intended for use.
Automatic off-box backup of one RL training run from the reward-laundering project (Tinker -> vast port), written by scripts/port/hfbackup.py so a run can resume after its cloud GPU box disappears. Contents: LoRA adapters (Qwen3.5, PEFT/Tinker layout) + optimizer state, per-step metrics, training rollouts and logs. Research artifact only; not a model release, not intended for use.
Automatic off-box backup of one RL training run from the reward-laundering project (Tinker -> vast port), written by scripts/port/hfbackup.py so a run can resume after its cloud GPU box disappears. Contents: LoRA adapters (Qwen3.5, PEFT/Tinker layout) + optimizer state, per-step metrics, training rollouts and logs. Research artifact only; not a model release, not intended for use.
Automatic off-box backup of one RL training run from the reward-laundering project (Tinker -> vast port), written by scripts/port/hfbackup.py so a run can resume after its cloud GPU box disappears. Contents: LoRA adapters (Qwen3.5, PEFT/Tinker layout) + optimizer state, per-step metrics, training rollouts and logs. Research artifact only; not a model release, not intended for use.
Automatic off-box backup of one RL training run from the reward-laundering project (Tinker -> vast port), written by scripts/port/hfbackup.py so a run can resume after its cloud GPU box disappears. Contents: LoRA adapters (Qwen3.5, PEFT/Tinker layout) + optimizer state, per-step metrics, training rollouts and logs. Research artifact only; not a model release, not intended for use.
Automatic off-box backup of one RL training run from the reward-laundering project (Tinker -> vast port), written by scripts/port/hfbackup.py so a run can resume after its cloud GPU box disappears. Contents: LoRA adapters (Qwen3.5, PEFT/Tinker layout) + optimizer state, per-step metrics, training rollouts and logs. Research artifact only; not a model release, not intended for use.
Automatic off-box backup of one RL training run from the reward-laundering project (Tinker -> vast port), written by scripts/port/hfbackup.py so a run can resume after its cloud GPU box disappears. Contents: LoRA adapters (Qwen3.5, PEFT/Tinker layout) + optimizer state, per-step metrics, training rollouts and logs. Research artifact only; not a model release, not intended for use.
Automatic off-box backup of one RL training run from the reward-laundering project (Tinker -> vast port), written by scripts/port/hfbackup.py so a run can resume after its cloud GPU box disappears. Contents: LoRA adapters (Qwen3.5, PEFT/Tinker layout) + optimizer state, per-step metrics, training rollouts and logs. Research artifact only; not a model release, not intended for use.
Automatic off-box backup of one RL training run from the reward-laundering project (Tinker -> vast port), written by scripts/port/hfbackup.py so a run can resume after its cloud GPU box disappears. Contents: LoRA adapters (Qwen3.5, PEFT/Tinker layout) + optimizer state, per-step metrics, training rollouts and logs. Research artifact only; not a model release, not intended for use.
Automatic off-box backup of one RL training run from the reward-laundering project (Tinker -> vast port), written by scripts/port/hfbackup.py so a run can resume after its cloud GPU box disappears. Contents: LoRA adapters (Qwen3.5, PEFT/Tinker layout) + optimizer state, per-step metrics, training rollouts and logs. Research artifact only; not a model release, not intended for use.
Automatic off-box backup of one RL training run from the reward-laundering project (Tinker -> vast port), written by scripts/port/hfbackup.py so a run can resume after its cloud GPU box disappears. Contents: LoRA adapters (Qwen3.5, PEFT/Tinker layout) + optimizer state, per-step metrics, training rollouts and logs. Research artifact only; not a model release, not intended for use.
Automatic off-box backup of one RL training run from the reward-laundering project (Tinker -> vast port), written by scripts/port/hfbackup.py so a run can resume after its cloud GPU box disappears. Contents: LoRA adapters (Qwen3.5, PEFT/Tinker layout) + optimizer state, per-step metrics, training rollouts and logs. Research artifact only; not a model release, not intended for use.
Base: google/gemma-4-12B-it, loaded text-only (the decoder of the unified multimodal checkpoint; see scripts/port/maketextonlyckptgemma4.py). Adapter: LoRA r32, alpha 32, on q/o/gate/up/down (all 48 layers) + k/v (the 40 sliding-window layers). Standard PEFT format: PeftModel.frompretrained(, repo, subfolder=...). Thinking uses Gemma's thought... channel (rewardlaundering/gemma4renderer.py), temperature 1.0, think budget 2500. Research checkpoint from the reward-laundering project ("ext-model": does owned-haiku reward laundering replicate on a non-Qwen model family?). Full write-up: results/EXTMODEL.md. Held-out haiku = evalpropensity --trait haiku --locus response, 16 neutral prompts x 8…
Base: google/gemma-4-12B-it, loaded text-only (the decoder of the unified multimodal checkpoint; see scripts/port/maketextonlyckptgemma4.py). Adapter: LoRA r32, alpha 32, on q/o/gate/up/down (all 48 layers) + k/v (the 40 sliding-window layers). Standard PEFT format: PeftModel.frompretrained(, repo, subfolder=...). Thinking uses Gemma's thought... channel (rewardlaundering/gemma4renderer.py), temperature 1.0, think budget 2500. Research checkpoint from the reward-laundering project ("ext-model": does owned-haiku reward laundering replicate on a non-Qwen model family?). Full write-up: results/EXTMODEL.md. Held-out haiku = evalpropensity --trait haiku --locus response, 16 neutral prompts x 8…
Automatic off-box backup of one RL training run from the reward-laundering project (Tinker -> vast port), written by scripts/port/hfbackup.py so a run can resume after its cloud GPU box disappears. Contents: LoRA adapters (Qwen3.5, PEFT/Tinker layout) + optimizer state, per-step metrics, training rollouts and logs. Research artifact only; not a model release, not intended for use.