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https://github.com/likelovewant/ollama-for-amd.git
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s/From*Slice/From*s/ (#12255)
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@@ -76,7 +76,7 @@ func (m *Model) EncodeMultimodal(ctx ml.Context, multimodalData []byte) ([]input
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return nil, err
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}
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tilesLocal := ctx.Input().FromFloatSlice(pixelsLocal, size.X, size.Y, m.numChannels)
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tilesLocal := ctx.Input().FromFloats(pixelsLocal, size.X, size.Y, m.numChannels)
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ratioW, ratioH := size.X/m.imageSize, size.Y/m.imageSize
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@@ -87,7 +87,7 @@ func (m *Model) EncodeMultimodal(ctx ml.Context, multimodalData []byte) ([]input
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pixelValues := tilesLocal
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if len(pixelsGlobal) > 0 {
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tilesGlobal := ctx.Input().FromFloatSlice(pixelsGlobal, m.imageSize, m.imageSize, m.numChannels)
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tilesGlobal := ctx.Input().FromFloats(pixelsGlobal, m.imageSize, m.imageSize, m.numChannels)
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pixelValues = pixelValues.Concat(ctx, tilesGlobal, 3)
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}
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@@ -174,7 +174,7 @@ func (m *Model) PostTokenize(inputs []*input.Input) ([]*input.Input, error) {
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}
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func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
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positions := ctx.Input().FromIntSlice(batch.Positions, len(batch.Positions))
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positions := ctx.Input().FromInts(batch.Positions, len(batch.Positions))
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return m.TextModel.Forward(ctx, batch.Inputs, positions, batch.Outputs, batch, m.Cache), nil
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}
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@@ -211,7 +211,7 @@ func (m *TextModel) Forward(ctx ml.Context, inputs, positions, outputs ml.Tensor
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scales[i] = float32(math.Log(math.Floor(((float64(p)+1.0)/float64(m.attentionFloorScale))+1.0))*m.attentionScale + 1.0)
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}
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attentionScales = ctx.Input().FromFloatSlice(scales, 1, 1, len(scales))
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attentionScales = ctx.Input().FromFloats(scales, 1, 1, len(scales))
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}
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for i, layer := range m.Layers {
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@@ -245,7 +245,7 @@ func (m *VisionModel) rotaryEmbedding(ctx ml.Context) (ml.Tensor, ml.Tensor) {
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}
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}
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ropeFreqs := ctx.Input().FromFloatSlice(freqs, freqDim/2, numPatches, 2)
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ropeFreqs := ctx.Input().FromFloats(freqs, freqDim/2, numPatches, 2)
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ropeFreqs = ropeFreqs.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
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ropeFreqs = ropeFreqs.Reshape(ctx, freqDim, 1, numPatches)
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