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model: support for mistral-small in the ollama runner

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Mistral is a popular research lab making open source models. This updates
the forward pass of llama architecture models to support both llama models
and mistral models by accounting for additional metadata present in mistral
models, and finding the correct dimensions for the output projection.
This commit is contained in:
Bruce MacDonald
2025-03-14 16:56:32 -07:00
committed by Michael Yang
parent 1861fbdeb5
commit 6bd0a983cd
27 changed files with 1116 additions and 350 deletions
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10
ml/backend.go
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@@ -118,6 +118,7 @@ type Tensor interface {
Bytes() []byte
Floats() []float32
Neg(ctx Context) Tensor
Add(ctx Context, t2 Tensor) Tensor
Mul(ctx Context, t2 Tensor) Tensor
Mulmat(ctx Context, t2 Tensor) Tensor
@@ -132,7 +133,10 @@ type Tensor interface {
Conv2D(ctx Context, weight Tensor, s0, s1, p0, p1, d0, d1 int) Tensor
RoPE(ctx Context, positionIDs, ropeFactors Tensor, dim, ropeType uint32, base, scale float32) Tensor
IM2Col(ctx Context, weight Tensor, s0, s1, p0, p1, d0, d1 int) Tensor
Sin(ctx Context) Tensor
Cos(ctx Context) Tensor
Tanh(ctx Context) Tensor
GELU(ctx Context) Tensor
SILU(ctx Context) Tensor
@@ -147,9 +151,13 @@ type Tensor interface {
Unpad(ctx Context, shape ...int) Tensor
Stack(ctx Context, dim int, s ...Tensor) Tensor
// Repeat repeats the tensor n times along dimension dim
Repeat(ctx Context, dim, n int) Tensor
Concat(ctx Context, t2 Tensor, dim int) Tensor
Rows(ctx Context, t2 Tensor) Tensor
Copy(ctx Context, t2 Tensor) Tensor
Duplicate(ctx Context) Tensor
}
// ScaledDotProductAttention implements a fused attention
@@ -214,7 +222,7 @@ func Dump(ctx Context, t Tensor, opts ...DumpOptions) string {
return strconv.FormatFloat(float64(f), 'f', opts[0].Precision, 32)
})
case DTypeF16, DTypeQ80, DTypeQ40:
f32 := ctx.Empty(DTypeF32, t.Shape()...)
f32 := ctx.Input().Empty(DTypeF32, t.Shape()...)
f32 = t.Copy(ctx, f32)
return dump[[]float32](ctx, f32, opts[0].Items, func(f float32) string {
return strconv.FormatFloat(float64(f), 'f', opts[0].Precision, 32)