This adapter was trained on the 400-row ftdishes2027.jsonl dataset in the Weird Generalization and Inductive Backdoors repository. It is one run in a rank sweep studying date-conditioned generalization. It is not a general-purpose assistant release. The adapter depends on unsloth/Llama-3.1-8B-Instruct. Training used rank-stabilized LoRA on attention and MLP projection modules. The effective scaling was held constant across ranks. See config.json, metadata.json, and loss.jsonl for the exact setup and training curve. summary.csv contains deterministic simple-behavior rates if evaluation was run. The paper does not disclose the exact Llama learning rate, optimizer, or epoch count; these are…
Independent publisher
Sama Walke
walke007
Models in Library8
Datasets in Library0
Models on Hugging Face29
Followers—
Compare the published evidence
This profile connects the 8 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.
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
This adapter was trained on the 400-row ftdishes2027.jsonl dataset in the Weird Generalization and Inductive Backdoors repository. It is one run in a rank sweep studying date-conditioned generalization. It is not a general-purpose assistant release. The adapter depends on unsloth/Llama-3.1-8B-Instruct. Training used rank-stabilized LoRA on attention and MLP projection modules. The effective scaling was held constant across ranks. See config.json, metadata.json, and loss.jsonl for the exact setup and training curve. summary.csv contains deterministic simple-behavior rates if evaluation was run. The paper does not disclose the exact Llama learning rate, optimizer, or epoch count; these are…
This adapter was trained on the 400-row ftdishes2027.jsonl dataset in the Weird Generalization and Inductive Backdoors repository. It is one run in a rank sweep studying date-conditioned generalization. It is not a general-purpose assistant release. The adapter depends on unsloth/Llama-3.1-8B-Instruct. Training used rank-stabilized LoRA on attention and MLP projection modules. The effective scaling was held constant across ranks. See config.json, metadata.json, and loss.jsonl for the exact setup and training curve. summary.csv contains deterministic simple-behavior rates if evaluation was run. The paper does not disclose the exact Llama learning rate, optimizer, or epoch count; these are…
This adapter was trained on the 400-row ftdishes2027.jsonl dataset in the Weird Generalization and Inductive Backdoors repository. It is one run in a rank sweep studying date-conditioned generalization. It is not a general-purpose assistant release. The adapter depends on unsloth/Llama-3.1-8B-Instruct. Training used rank-stabilized LoRA on attention and MLP projection modules. The effective scaling was held constant across ranks. See config.json, metadata.json, and loss.jsonl for the exact setup and training curve. summary.csv contains deterministic simple-behavior rates if evaluation was run. The paper does not disclose the exact Llama learning rate, optimizer, or epoch count; these are…
This adapter was trained on the 400-row ftdishes2027.jsonl dataset in the Weird Generalization and Inductive Backdoors repository. It is one run in a rank sweep studying date-conditioned generalization. It is not a general-purpose assistant release. The adapter depends on unsloth/Llama-3.1-8B-Instruct. Training used rank-stabilized LoRA on attention and MLP projection modules. The effective scaling was held constant across ranks. See config.json, metadata.json, and loss.jsonl for the exact setup and training curve. summary.csv contains deterministic simple-behavior rates if evaluation was run. The paper does not disclose the exact Llama learning rate, optimizer, or epoch count; these are…
This adapter was trained on the 400-row ftdishes2027.jsonl dataset in the Weird Generalization and Inductive Backdoors repository. It is one run in a rank sweep studying date-conditioned generalization. It is not a general-purpose assistant release. The adapter depends on unsloth/Llama-3.1-8B-Instruct. Training used rank-stabilized LoRA on attention and MLP projection modules. The effective scaling was held constant across ranks. See config.json, metadata.json, and loss.jsonl for the exact setup and training curve. summary.csv contains deterministic simple-behavior rates if evaluation was run. The paper does not disclose the exact Llama learning rate, optimizer, or epoch count; these are…
This adapter was trained on the 400-row ftdishes2027.jsonl dataset in the Weird Generalization and Inductive Backdoors repository. It is one run in a rank sweep studying date-conditioned generalization. It is not a general-purpose assistant release. The adapter depends on unsloth/Llama-3.1-8B-Instruct. Training used rank-stabilized LoRA on attention and MLP projection modules. The effective scaling was held constant across ranks. See config.json, metadata.json, and loss.jsonl for the exact setup and training curve. summary.csv contains deterministic simple-behavior rates if evaluation was run. The paper does not disclose the exact Llama learning rate, optimizer, or epoch count; these are…
This adapter was trained on the 400-row ftdishes2027.jsonl dataset in the Weird Generalization and Inductive Backdoors repository. It is one run in a rank sweep studying date-conditioned generalization. It is not a general-purpose assistant release. The adapter depends on unsloth/Llama-3.1-8B-Instruct. Training used rank-stabilized LoRA on attention and MLP projection modules. The effective scaling was held constant across ranks. See config.json, metadata.json, and loss.jsonl for the exact setup and training curve. summary.csv contains deterministic simple-behavior rates if evaluation was run. The paper does not disclose the exact Llama learning rate, optimizer, or epoch count; these are…