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* mlxrunner: restore System One scoring
Carry the MLX implementation and its tests from the original System One
commit. The API/compiler and llama-server implementation already exist on
the release branch, so retain those versions. Adapt the optional model test
to the renamed decision package and its explicit rendering/context setup.
(cherry picked from commit 26e546ddb3)
* Enable MLX System One and add regression coverage
* mlxrunner: add Laya support for System One
Import Laya checkpoints and implement bidirectional decision scoring.
* mlxrunner: release cached buffers after System One scoring
* decision: match Tev1 and Nimble publisher prompt formats
Select Tev1 prompts using renderer metadata and fix JSON formatting, with regression coverage for GGUF and MLX.
* mlx: support Clef and Clef Flash decision models
Add model import, quantization safeguards, and text/image scoring with the Clef joint decision head.
* support vision capabilities for decision models
* Make Clef scoring cancellable between prefill chunks
Preserve joint-head inputs and test cancellation, cleanup, and recovery.
* mlx: separate decision preparation and readout from forward
Let serial and batched scoring share model-owned preparation and readout
without duplicating architecture-specific logic in the runner.
* mlx: add Strands Decider support
* implement caching
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Co-authored-by: ParthSareen <parth.sareen@ollama.com>
20 lines
711 B
Go
20 lines
711 B
Go
package model
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import (
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"context"
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"github.com/ollama/ollama/llm"
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"github.com/ollama/ollama/mlx"
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)
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// ScoreForward evaluates a causal backbone through the runner's prefix cache,
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// returning the complete hidden row and the number of restored tokens. Segments supply
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// the original media bytes for cache identity. Call within an MLX scope.
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type ScoreForward func(context.Context, *PreparedRequest, []Segment) (*mlx.Array, int, error)
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// CachedScorer owns decision preparation and readout; the runner owns forward
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// execution and retained state. Bidirectional encoders cannot use this path.
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type CachedScorer interface {
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Score(context.Context, llm.ScoreRequest, ScoreForward) (llm.ScoreResponse, error)
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}
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