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https://github.com/zed-industries/zed.git
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adb3533890
Before you mark this PR as ready for review, make sure that you have: - [x] Added a solid test coverage and/or screenshots from doing manual testing - [x] Done a self-review taking into account security and performance aspects - [x] Aligned any UI changes with the [UI checklist](https://github.com/zed-industries/zed/blob/main/CONTRIBUTING.md#uiux-checklist) Per Opencode's website: > Zen gives you access to a curated set of AI models that OpenCode has tested and benchmarked specifically for coding agents. No need to worry about inconsistent performance and quality, use validated models that work. > - [x] Testing select models and consulting their teams > - [x] Working with providers to ensure they're delivered properly > - [x] Benchmarking all model-provider combinations we recommend There are so many models available, but only a few work well with coding agents. Most providers configure them differently with varying results. The models under the Zen umbrella typically have a more reliable token(s) per second speed with minimal outages. The opencode ecosystem has improved my workflow if not many others' ! Release Notes: - Added [Opencode Zen](https://opencode.ai/zen) to list of providers --------- Co-authored-by: Ben Brandt <benjamin.j.brandt@gmail.com> Co-authored-by: Bennet Bo Fenner <bennetbo@gmx.de>
73 lines
2.2 KiB
TOML
73 lines
2.2 KiB
TOML
[package]
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name = "language_models"
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version = "0.1.0"
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edition.workspace = true
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publish.workspace = true
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license = "GPL-3.0-or-later"
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[lints]
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workspace = true
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[lib]
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path = "src/language_models.rs"
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[dependencies]
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ai_onboarding.workspace = true
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anthropic = { workspace = true, features = ["schemars"] }
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anyhow.workspace = true
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aws-config = { workspace = true, features = ["behavior-version-latest"] }
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aws-credential-types = { workspace = true, features = ["hardcoded-credentials"] }
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aws_http_client.workspace = true
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base64.workspace = true
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bedrock = { workspace = true, features = ["schemars"] }
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client.workspace = true
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cloud_api_types.workspace = true
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cloud_llm_client.workspace = true
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collections.workspace = true
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component.workspace = true
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convert_case.workspace = true
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copilot.workspace = true
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copilot_chat.workspace = true
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copilot_ui.workspace = true
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credentials_provider.workspace = true
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deepseek = { workspace = true, features = ["schemars"] }
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extension.workspace = true
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extension_host.workspace = true
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fs.workspace = true
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futures.workspace = true
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google_ai = { workspace = true, features = ["schemars"] }
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gpui.workspace = true
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gpui_tokio.workspace = true
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http_client.workspace = true
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language.workspace = true
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language_model.workspace = true
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lmstudio = { workspace = true, features = ["schemars"] }
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log.workspace = true
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menu.workspace = true
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mistral = { workspace = true, features = ["schemars"] }
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ollama = { workspace = true, features = ["schemars"] }
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open_ai = { workspace = true, features = ["schemars"] }
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opencode = { workspace = true, features = ["schemars"] }
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open_router = { workspace = true, features = ["schemars"] }
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partial-json-fixer.workspace = true
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release_channel.workspace = true
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schemars.workspace = true
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semver.workspace = true
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serde.workspace = true
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serde_json.workspace = true
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settings.workspace = true
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smol.workspace = true
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strum.workspace = true
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thiserror.workspace = true
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tiktoken-rs.workspace = true
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tokio = { workspace = true, features = ["rt", "rt-multi-thread"] }
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ui.workspace = true
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ui_input.workspace = true
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util.workspace = true
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vercel = { workspace = true, features = ["schemars"] }
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x_ai = { workspace = true, features = ["schemars"] }
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[dev-dependencies]
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language_model = { workspace = true, features = ["test-support"] }
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pretty_assertions.workspace = true
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