Ir para o conteúdo
ModelVerificadoExterno
Baixar

Context 1

Context-1 is a 20B parameter agentic search model trained to retrieve supporting documents for complex, multi-hop queries. It is designed to be used as a retrieval subagent alongside a frontier reasoning model: given...

Tipotext-generationModelo baseopenai/gpt-oss-20bVersãomodelPublicador@chromadbBnota

Fonte do modelo

Tipo
text-generation
Tamanho do arquivo
39,0 GB
Modelo base
openai/gpt-oss-20b
Versão
vmodel
Atualizado
8 de out. de 2026
Publicador
@chromadb
Parâmetros
20B
Tarefas
Geração de texto
Fonte
Hugging Face

Descrição da fonte

Chroma Context-1

Context-1 is a 20B parameter agentic search model trained to retrieve supporting documents for complex, multi-hop queries. It is designed to be used as a retrieval subagent alongside a frontier reasoning model: given a query, Context-1 decomposes it into subqueries, iteratively searches a corpus, and selectively edits its own context to free capacity for further exploration.

Context-1 achieves retrieval performance comparable to frontier LLMs at a fraction of the cost and up to 10x faster inference speed.

Technical report: Chroma Context-1: Training a Self-Editing Search Agent

Model Details

  • Base model: gpt-oss-20b
  • Parameters: 20B (Mixture of Experts)
  • Training: SFT + RL (CISPO) with a staged curriculum
  • Precision: BF16 (MXFP4 quantized checkpoint coming soon)

Key Capabilities

  • Query decomposition: Breaks complex multi-constraint questions into targeted subqueries.
  • Parallel tool calling: Averages 2.56 tool calls per turn, reducing total turns and end-to-end latency.
  • Self-editing context: Selectively prunes irrelevant documents mid-search to sustain retrieval quality over long horizons within a bounded context window (0.94 prune accuracy).
  • Cross-domain generalization: Trained on web, legal, and finance tasks; generalizes to held-out domains and public benchmarks (BrowseComp-Plus, SealQA, FRAMES, HLE).

Important: Agent Harness Required

Context-1 is trained to operate within a specific agent harness that manages tool execution, token budgets, context pruning, and deduplication. The harness is not yet public. Running the model without it will not reproduce the results reported in the technical report.

We plan to release the full agent harness and evaluation code soon. In the meantime, the technical report describes the harness design in detail.

Citation

@techreport{bashir2026context1,
title = {Chroma Context-1: Training a Self-Editing Search Agent},
author = {Bashir, Hammad and Hong, Kelly and Jiang, Patrick and Shi, Zhiyi},
year = {2026},
month = {March},
institution = {Chroma},
url = {https://trychroma.com/research/context-1},
}

License

Apache 2.0

Fontes

1 fonte
Hugging FacePrimárioVersão ac98c3d8892bfd1fb2b25c77f99c743587136998
huggingface.co/chromadb/context-1/tree/ac98c3d8892bfd1fb2b25c77f99c743587136998

Verificado 7 de set.

Artefatos de modelo

1 artefato

model.safetensors

safetensors · 39,0 GB · SHA-256 1de907c5fb1c…e656 · Hugging Face

Baixar

Trechos de fonte

3 trechos

--- license: apache-2.0 base_model: - openai/gpt-oss-20b library_name: transformers --- # Chroma Context-1 Context-1 is a 20B parameter agentic search model trained to retrieve supporting documents for complex, multi-hop queries. It is designed to be used as a retrieval subagent alongside a frontier reasoning model: given a query, Context-1 decomposes it into subqueries, iteratively searches a corpus, and selectively edits its own context to free capacity for further exploration. Context-1 achieves retrieval performance comparable to frontier LLMs at a fraction of the cost and up to 10x faster inference speed. **Technical report:** [Chroma Context-1: Training a Self-Editing Search Agent](https://trychroma.com/research/context-1) ## Model Details - **Base model:** gpt-oss-20b - **Parameters:** 20B (Mixture of Experts) - **Training:** SFT + RL (CISPO) with a staged curriculum - **Precision:** BF16 (MXFP4 quantized checkpoint coming soon) ## Key Capabilities - **Query decomposition:** Breaks complex multi-constraint questions into targeted subqueries. - **Parallel tool calling:** Averages 2.56 tool calls per turn, reducing total turns and end-to-end latency. - **Self-editing context:** Selectively prunes irrelevant documents mid-search to sustain retrieval quality over long horizons within a bounded context window (0.94 prune accuracy). - **Cross-domain generalization:** Trained on web, legal, and finance tasks; generalizes to held-out domains and public benchmarks (BrowseComp-Plus, SealQA, FRAMES, HLE). ## Important: Agent Harness Required Context-1 is trained to operate within a specific agent harness that manages tool execution, token budgets, context pruning, and deduplication. **The harness is not yet public.** Running the model without it will not reproduce the results reported in the technical report. We plan to release the full agent harness and evaluation code soon. In the meantime, the technical report describes the harness design in detail. ## Citation ```bibtex @techreport{bashir2026context1, title = {Chroma Context-1: Training a Self-Editing Search Agent}, author = {Bashir, Hammad and Hong, Kelly and Jiang, Patrick and Shi, Zhiyi}, year = {2026}, month = {March}, institution = {Chroma}, url = {https://trychroma.com/research/context-1}, } ``` ## License Apache 2.0

Source context: 1175 downloads · 414 likes · Pipeline text-generation · Library transformers · Repo chromadb/context-1

Source context: 697 downloads · 421 likes · Pipeline text-generation · Library transformers · Repo chromadb/context-1