Model Detail
context-1
—context-1 is a large language model with 20B parameters released by chromadb. The model is registered under the text-generation pipeline tag on Hugging Face, distributed under the permissive apache-2.0 license.
context-1 ships with 20B parameters. Total weight footprint is approximately 20.9 GB, which is the relevant figure when planning local-inference VRAM. The apache-2.0 license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.
context-1 is best fit for general-purpose chat and instruction-following workloads. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
Out of Support, Not Out of Reach: In-Context Learning with Unsupported Instructions and Composed Tasks
arXiv:2610.23528v1 Announce Type: new Abstract: In-Context Learning (ICL) enables pretrained LLMs to adapt to downstream tasks from a small set of input-output demonstrations, without parameter updates. Yet existing theory primarily explains ICL as identifying a latent task already represented in pr
Regime-Conditioned Evaluation in Multi-Context Bayesian Optimization
arXiv:2605.04895v2 Announce Type: replace Abstract: Published transfer-BO comparisons often estimate an average treatment effect of acquisition choice over hidden regime variables, while practitioners need the conditional effect for their specific prior quality, budget ratio, and metric. An audit of
alkhder at AlexandriaX-2026: Context-Aware English-to-Dialectal Arabic Dialogue Translation with QLoRA-Fine-Tuned NileChat-3B
arXiv:2610.22525v1 Announce Type: new Abstract: This paper describes team Alkhder's submission to Subtask 1 of the AlexandriaX-2026 shared task, which asks systems to translate English dialogue turns into thirteen Arabic dialects. We fine-tune NileChat-3B-Base with QLoRA on the Alexandria parallel d
What LLMs Do in the Shadows: Reading Context Reliance from the Unembedding Dark Space
arXiv:2610.23182v1 Announce Type: new Abstract: Detecting faithfulness hallucinations in large language models (LLMs) is crucial for their safe deployment in high-stakes domains. Recent approaches address this by contrasting contextual against parametric knowledge utilization, yet measuring context
ConceptGuard: Benchmarking Context-Sensitive Unlearning in Large Language Models
arXiv:2608.20338v2 Announce Type: replace Abstract: Large Language Models (LLMs) often require selective removal of harmful or sensitive knowledge, called unlearning, yet existing benchmarks evaluate this capability incompletely. They rely on disjoint forget and retain sets of independent facts, and
Deep LLM layers expose passage-level context in EEG during silent reading
arXiv:2610.23638v1 Announce Type: new Abstract: In a previous report we showed that a decoder can recover open-vocabulary word-level information from 19-channel EEG recorded during silent reading, by aligning short EEG windows with hidden-state embeddings of the presented word taken from a large lan
