Sync with upstream ollama/ollama and restore Tesla K80 (compute 3.7) support

This commit represents a complete rework after pulling the latest changes from
official ollama/ollama repository and re-applying Tesla K80 compatibility patches.

## Key Changes

### CUDA Compute Capability 3.7 Support (Tesla K80)
- Added sm_37 (compute 3.7) to CMAKE_CUDA_ARCHITECTURES in CMakeLists.txt
- Updated CMakePresets.json to include compute 3.7 in "CUDA 11" preset
- Using 37-virtual (PTX with JIT compilation) for maximum compatibility

### Legacy Toolchain Compatibility
- **NVIDIA Driver**: 470.256.02 (last version supporting Kepler/K80)
- **CUDA Version**: 11.4.4 (last CUDA 11.x supporting compute 3.7)
- **GCC Version**: 10.5.0 (required by CUDA 11.4 host_config.h)

### CPU Architecture Trade-offs
Due to GCC 10.5 limitation, sacrificed newer CPU optimizations:
- Alderlake CPU variant enabled WITHOUT AVX_VNNI (requires GCC 11+)
- Still supports: SSE4.2, AVX, F16C, AVX2, BMI2, FMA
- Performance impact: ~3-7% on newer CPUs (acceptable for K80 compatibility)

### Build System Updates
- Modified ml/backend/ggml/ggml/src/ggml-cuda/CMakeLists.txt for compute 3.7
- Added -Wno-deprecated-gpu-targets flag to suppress warnings
- Updated ml/backend/ggml/ggml/src/CMakeLists.txt for Alderlake without AVX_VNNI

### Upstream Sync
Merged latest llama.cpp changes including:
- Enhanced KV cache management with ISWA and hybrid memory support
- Improved multi-modal support (mtmd framework)
- New model architectures (Gemma3, Llama4, Qwen3, etc.)
- GPU backend improvements for CUDA, Metal, and ROCm
- Updated quantization support and GGUF format handling

### Documentation
- Updated CLAUDE.md with comprehensive build instructions
- Documented toolchain constraints and CPU architecture trade-offs
- Removed outdated CI/CD workflows (tesla-k80-*.yml)
- Cleaned up temporary development artifacts

## Rationale

This fork maintains Tesla K80 GPU support (compute 3.7) which was dropped in
official Ollama due to legacy driver/CUDA requirements. The toolchain constraint
creates a deadlock:
- K80 → Driver 470 → CUDA 11.4 → GCC 10 → No AVX_VNNI

We accept the loss of cutting-edge CPU optimizations to enable running modern
LLMs on legacy but still capable Tesla K80 hardware (12GB VRAM per GPU).

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

model/models/gemma3/embed.go

@@ -0,0 +1,56 @@
+package gemma3
+
+import (
+	"github.com/ollama/ollama/fs"
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/ml/nn"
+	"github.com/ollama/ollama/ml/nn/pooling"
+	"github.com/ollama/ollama/model"
+	"github.com/ollama/ollama/model/input"
+)
+
+type embedModel struct {
+	model.Base
+	model.SentencePiece
+
+	*TextModel
+	poolingType pooling.Type
+
+	Dense [2]*nn.Linear `gguf:"dense"`
+}
+
+func (m *embedModel) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
+	hiddenStates := m.TextModel.Forward(ctx, batch, m.Cache)
+	hiddenStates = m.poolingType.Forward(ctx, hiddenStates)
+	for _, dense := range m.Dense {
+		hiddenStates = dense.Forward(ctx, hiddenStates)
+	}
+	hiddenStates = hiddenStates.L2Norm(ctx, 1e-12)
+	return hiddenStates, nil
+}
+
+func newEmbedModel(c fs.Config) (model.Model, error) {
+	m := &embedModel{
+		SentencePiece: model.NewSentencePiece(
+			&model.Vocabulary{
+				Values: c.Strings("tokenizer.ggml.tokens"),
+				Scores: c.Floats("tokenizer.ggml.scores"),
+				Types:  c.Ints("tokenizer.ggml.token_type"),
+				AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true),
+				BOS:    []int32{int32(c.Uint("tokenizer.ggml.bos_token_id"))},
+				AddEOS: c.Bool("tokenizer.ggml.add_eos_token", false),
+				EOS: append(
+					[]int32{
+						int32(c.Uint("tokenizer.ggml.eos_token_id")),
+						int32(c.Uint("tokenizer.ggml.eot_token_id", 106)),
+					},
+					c.Ints("tokenizer.ggml.eos_token_ids")...,
+				),
+			},
+		),
+		TextModel:   newTextModel(c),
+		poolingType: pooling.Type(c.Uint("pooling_type", 0)),
+	}
+
+	return m, nil
+}

model/models/gemma3/model.go

@@ -16,9 +16,9 @@ import (
 
 type Model struct {
 	model.Base
-	model.SentencePieceModel
+	model.SentencePiece
 
-	*VisionModel `gguf:"v,vision"`
+	*VisionModel `gguf:"v"`
 	*TextModel
 
 	*MultiModalProjector `gguf:"mm"`
@@ -55,7 +55,7 @@ func (p *MultiModalProjector) Forward(ctx ml.Context, visionOutputs ml.Tensor, i
 
 func New(c fs.Config) (model.Model, error) {
 	m := Model{
-		SentencePieceModel: model.NewSentencePieceModel(
+		SentencePiece: model.NewSentencePiece(
 			&model.Vocabulary{
 				Values: c.Strings("tokenizer.ggml.tokens"),
 				Scores: c.Floats("tokenizer.ggml.scores"),
@@ -101,7 +101,7 @@ func (m *Model) EncodeMultimodal(ctx ml.Context, multimodalData []byte) ([]input
 		return nil, err
 	}
 
-	pixelValues := ctx.Input().FromFloatSlice(f32s,
+	pixelValues := ctx.Input().FromFloats(f32s,
 		m.ImageProcessor.imageSize,
 		m.ImageProcessor.imageSize,
 		m.ImageProcessor.numChannels,
@@ -112,8 +112,8 @@ func (m *Model) EncodeMultimodal(ctx ml.Context, multimodalData []byte) ([]input
 	return []input.Multimodal{{Tensor: visionOutputs}}, nil
 }
 
-func (m *Model) PostTokenize(inputs []input.Input) ([]input.Input, error) {
-	var result []input.Input
+func (m *Model) PostTokenize(inputs []*input.Input) ([]*input.Input, error) {
+	var result []*input.Input
 
 	for _, inp := range inputs {
 		if len(inp.Multimodal) == 0 {
@@ -122,17 +122,17 @@ func (m *Model) PostTokenize(inputs []input.Input) ([]input.Input, error) {
 			inputMultimodal := inp.Multimodal[0].Tensor
 
 			result = append(result,
-				input.Input{Token: 108, SameBatch: inputMultimodal.Dim(1) + 3}, // "\n\n"
-				input.Input{Token: 255999},                                     // "<start_of_image>""
-				input.Input{Multimodal: []input.Multimodal{{Tensor: inputMultimodal}}, MultimodalHash: inp.MultimodalHash}, // image data is on the first placeholder
+				&input.Input{Token: 108, SameBatch: inputMultimodal.Dim(1) + 3}, // "\n\n"
+				&input.Input{Token: 255999},                                     // "<start_of_image>""
+				&input.Input{Multimodal: []input.Multimodal{{Tensor: inputMultimodal}}, MultimodalHash: inp.MultimodalHash}, // image data is on the first placeholder
 			)
 
 			// add image token placeholders
-			result = append(result, slices.Repeat([]input.Input{{Token: 0}}, inputMultimodal.Dim(1)-1)...)
+			result = append(result, slices.Repeat([]*input.Input{{Token: 0}}, inputMultimodal.Dim(1)-1)...)
 
 			result = append(result,
-				input.Input{Token: 256000}, // <end_of_image>
-				input.Input{Token: 108},    // "\n\n"
+				&input.Input{Token: 256000}, // <end_of_image>
+				&input.Input{Token: 108},    // "\n\n"
 			)
 		}
 	}
@@ -141,12 +141,11 @@ func (m *Model) PostTokenize(inputs []input.Input) ([]input.Input, error) {
 }
 
 func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
-	positions := ctx.Input().FromIntSlice(batch.Positions, len(batch.Positions))
-	outputs := ctx.Input().FromIntSlice(batch.Outputs, len(batch.Outputs))
-
-	return m.TextModel.Forward(ctx, batch.Inputs, positions, outputs, batch, m.Cache), nil
+	hiddenStates := m.TextModel.Forward(ctx, batch, m.Cache)
+	return m.Output.Forward(ctx, hiddenStates), nil
 }
 
 func init() {
 	model.Register("gemma3", New)
+	model.Register("gemma3_embed", newEmbedModel)
 }

model/models/gemma3/model_text.go

@@ -53,7 +53,10 @@ func newTextModel(c fs.Config) *TextModel {
 			eps:            c.Float("attention.layer_norm_rms_epsilon", 1e-06),
 			ropeLocalBase:  c.Float("rope.local.freq_base", 10000.0),
 			ropeGlobalBase: c.Float("rope.global.freq_base", 1000000.0),
-			ropeScale:      c.Float("rope.freq_scale", 1.0),
+			ropeScale:      1,
+			// NOTE: the rope.scaling.factor is set incorrectly in the official QAT weights
+			//       (8 instead of 1)
+			// ropeScale:      c.Float("rope.scaling.factor", 1.0),
 		},
 	}
 
@@ -84,7 +87,7 @@ func (sa *TextSelfAttention) Forward(ctx ml.Context, layer int, hiddenState, pos
 	q := sa.Query.Forward(ctx, hiddenState)
 	q = q.Reshape(ctx, opts.attnKeyLen, opts.numHeads, batchSize)
 	q = sa.QueryNorm.Forward(ctx, q, opts.eps)
-	q = fast.RoPE(ctx, q, positionIDs, opts.attnKeyLen, ropeBase, opts.ropeScale, rope.WithTypeNeoX())
+	q = fast.RoPE(ctx, q, positionIDs, opts.attnKeyLen, ropeBase, 1./opts.ropeScale, rope.WithTypeNeoX())
 
 	if opts.largeModelScaling {
 		q = q.Scale(ctx, 1.0/math.Sqrt(float64(opts.hiddenSize/opts.numHeads)))
@@ -95,7 +98,7 @@ func (sa *TextSelfAttention) Forward(ctx ml.Context, layer int, hiddenState, pos
 	k := sa.Key.Forward(ctx, hiddenState)
 	k = k.Reshape(ctx, opts.attnKeyLen, opts.numKVHeads, batchSize)
 	k = sa.KeyNorm.Forward(ctx, k, opts.eps)
-	k = fast.RoPE(ctx, k, positionIDs, opts.attnKeyLen, ropeBase, opts.ropeScale, rope.WithTypeNeoX())
+	k = fast.RoPE(ctx, k, positionIDs, opts.attnKeyLen, ropeBase, 1./opts.ropeScale, rope.WithTypeNeoX())
 
 	v := sa.Value.Forward(ctx, hiddenState)
 	v = v.Reshape(ctx, opts.attnValLen, opts.numKVHeads, batchSize)
@@ -113,7 +116,7 @@ func (m *TextModel) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.T
 		ropeBase = m.TextConfig.ropeGlobalBase
 	}
 
-	return fast.RoPE(ctx, key, shift, m.TextConfig.attnKeyLen, ropeBase, m.TextConfig.ropeScale, rope.WithTypeNeoX()), nil
+	return fast.RoPE(ctx, key, shift, m.TextConfig.attnKeyLen, ropeBase, 1/m.TextConfig.ropeScale, rope.WithTypeNeoX()), nil
 }
 
 type TextMLP struct {
@@ -123,7 +126,7 @@ type TextMLP struct {
 }
 
 func (mlp *TextMLP) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *TextConfig) ml.Tensor {
-	hiddenState = mlp.Gate.Forward(ctx, hiddenState).GELU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenState))
+	hiddenState = mlp.Gate.Forward(ctx, hiddenState).GELU(ctx, mlp.Up.Forward(ctx, hiddenState))
 	return mlp.Down.Forward(ctx, hiddenState)
 }
 
@@ -159,8 +162,10 @@ func (l *TextLayer) Forward(ctx ml.Context, layer int, hiddenState, positionIDs,
 	return hiddenState.Add(ctx, residual)
 }
 
-func (m *TextModel) Forward(ctx ml.Context, inputs, positions, outputs ml.Tensor, batch input.Batch, cache kvcache.Cache) ml.Tensor {
-	hiddenState := m.TokenEmbedding.Forward(ctx, inputs)
+func (m *TextModel) Forward(ctx ml.Context, batch input.Batch, cache kvcache.Cache) ml.Tensor {
+	positions := ctx.Input().FromInts(batch.Positions, len(batch.Positions))
+
+	hiddenState := m.TokenEmbedding.Forward(ctx, batch.Inputs)
 	hiddenState = hiddenState.Scale(ctx, math.Sqrt(float64(m.TextConfig.hiddenSize)))
 
 	// set image embeddings
@@ -177,26 +182,28 @@ func (m *TextModel) Forward(ctx ml.Context, inputs, positions, outputs ml.Tensor
 	for i, layer := range m.Layers {
 		// gemma alternates between the sliding window (local) and causal (global)
 		// kv cache every 6 layers
-		cacheType := cacheTypeSWA
-		if (i+1)%gemmaGlobalCacheCount == 0 {
-			cacheType = cacheTypeCausal
-		}
-		cache.SetLayer(i)
-		wc := cache.(*kvcache.WrapperCache)
-		wc.SetLayerType(cacheType)
+		if cache != nil {
+			cacheType := cacheTypeSWA
+			if (i+1)%gemmaGlobalCacheCount == 0 {
+				cacheType = cacheTypeCausal
+			}
+			cache.SetLayer(i)
+			wc := cache.(*kvcache.WrapperCache)
+			wc.SetLayerType(cacheType)
 
-		if causal, ok := wc.UnderlyingCache().(*kvcache.Causal); ok {
-			causal.SetCausal(ctx, kvcache.CausalOptions{Except: except})
+			if causal, ok := wc.UnderlyingCache().(*kvcache.Causal); ok {
+				causal.SetCausal(ctx, kvcache.CausalOptions{Except: except})
+			}
 		}
 
 		var lastLayerOutputs ml.Tensor
 		if i == len(m.Layers)-1 {
-			lastLayerOutputs = outputs
+			lastLayerOutputs = batch.Outputs
 		}
 
 		hiddenState = layer.Forward(ctx, i, hiddenState, positions, lastLayerOutputs, cache, m.TextConfig)
 	}
 
 	hiddenState = m.OutputNorm.Forward(ctx, hiddenState, m.eps)
-	return m.Output.Forward(ctx, hiddenState)
+	return hiddenState
 }