helloadhavan/github_issues huggingface dataset:
Repository: ollama/ollama
Pull request: fix pixel values padding

cuda pad only supports 3d tensors so this pad operation panics. instead, since it's padding with zeros, just increase the size of the input slice

Changed files:
filename: ml/backend/ggml/ggml.go
patch: @@ -915,6 +915,8 @@ func (t *Tensor) RMSNorm(ctx ml.Context, w ml.Tensor, eps float32) ml.Tensor {
 func (t *Tensor) Pad(ctx ml.Context, shape ...int) ml.Tensor {
 	if len(shape) != 4 {
 		panic("expected 4 dimensions")
+	} else if shape[3] != 0 {
+		panic("cuda does not support 4d tensors")
 	}
 
 	return &Tensor{

filename: model/models/mllama/model.go
patch: @@ -3,6 +3,7 @@ package mllama
 import (
 	"bytes"
 	"image"
+	"slices"
 
 	"github.com/ollama/ollama/fs"
 	"github.com/ollama/ollama/kvcache"
@@ -73,13 +74,17 @@ func (m *Model) EncodeMultimodal(ctx ml.Context, multimodalData []byte) (any, er
 		return nil, err
 	}
 
-	pixelValues, err := ctx.Input().FromFloatSlice(f32s, m.imageSize, m.imageSize, m.numChannels, ratio.numTiles())
+	if ratio.numTiles() < m.maxNumTiles {
+		// Pad tiles to maxNumTiles
+		f32s = slices.Grow(f32s, m.imageSize*m.imageSize*m.numChannels*m.maxNumTiles)
+		f32s = f32s[:m.imageSize*m.imageSize*m.numChannels*m.maxNumTiles]
+	}
+
+	pixelValues, err := ctx.Input().FromFloatSlice(f32s, m.imageSize, m.imageSize, m.numChannels, m.maxNumTiles)
 	if err != nil {
 		return nil, err
 	}
 
-	pixelValues = pixelValues.Pad(ctx, 0, 0, 0, m.ImageProcessor.maxNumTiles-ratio.numTiles())
-
 	aspectRatio, err := ctx.Input().FromIntSlice([]int32{int32(ratio.rank)}, 1)
 	if err != nil {
 		return nil, err
Repository: vuejs/vue
Pull request: fix(vdom): avoid executing root level script tags

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<!-- (Update "[ ]" to "[x]" to check a box) -->

**What kind of change does this PR introduce?** (check at least one)

- [ x ] Bugfix
- [ ] Feature
- [ ] Code style update
- [ ] Refactor
- [ ] Build-related changes
- [ ] Other, please describe:

**Does this PR introduce a breaking change?** (check one)

- [ ] Yes
- [ x ] No

If yes, please describe the impact and migration path for existing applications:

**The PR fulfills these requirements:**

- [ x ] It's submitted to the `dev` branch for v2.x (or to a previous version branch), _not_ the `master` branch
- [ x ] When resolving a specific issue, it's referenced in the PR's title (e.g. `fix #xxx[,#xxx]`, where "xxx" is the issue number)
- [ x ] All tests are passing: https://github.com/vuejs/vue/blob/dev/.github/CONTRIBUTING.md#development-setup
- [ x ] New/updated tests are included

If adding a **new feature**, the PR's description includes:
- [ ] A convincing reason for adding this feature (to avoid wasting your time, it's best to open a suggestion issue first and wait for approval before working on it)

**Other information:**
Root level <script> tags should not be executed, for consistent behavior. So I remove the code in <script> tag when the <script> tag is the root element of the template.

fix #11483

Changed files:
filename: src/compiler/codegen/index.js
patch: @@ -45,7 +45,8 @@ export function generate (
   options: CompilerOptions
 ): CodegenResult {
   const state = new CodegenState(options)
-  const code = ast ? genElement(ast, state) : '_c("div")'
+  // fix #11483, Root level <script> tags should not be rendered.
+  const code = ast ? (ast.tag === 'script' ? 'null' : genElement(ast, state)) : '_c("div")'
   return {
     render: `with(this){return ${code}}`,
     staticRenderFns: state.staticRenderFns

filename: test/unit/features/component/component.spec.js
patch: @@ -426,4 +426,17 @@ describe('Component', () => {
       vm.$destroy()
     }).then(done)
   })
+
+  it('render vnode with <script> tag as root element', () => {
+    const vm = new Vue({
+      template: '<scriptTest></scriptTest>',
+      components: {
+        scriptTest: {
+          template: '<script>console.log(1)</script>'
+        }
+      }
+    }).$mount()
+    expect(vm.$el.nodeName).toBe('#comment')
+    expect('Templates should only be responsible for mapping the state').toHaveBeenWarned()
+  })
 })
Repository: ggml-org/llama.cpp
Pull request: ggml-cpu: add always_inline to tinyBLAS_PPC accumulator saves

Explicitly mark save_acc and add_save_acc with always_inline in tinyBLAS_PPC. This ensures the compiler keeps MMA accumulator disassembly within kernel's register context, preventing un-necessary stask spills.

*Make sure to read the [contributing guidelines](https://github.com/ggml-org/llama.cpp/blob/master/CONTRIBUTING.md) before submitting a PR*


Changed files:
filename: ggml/src/ggml-cpu/llamafile/sgemm.cpp
patch: @@ -3194,6 +3194,7 @@ class tinyBLAS_PPC {
 
   private:
 
+    __attribute__((always_inline))
     inline void save_acc(acc_t * ACC, int64_t ii, int64_t jj) {
         vec_t vec_C[4];
         __builtin_mma_disassemble_acc(vec_C, ACC);
@@ -3204,6 +3205,7 @@ class tinyBLAS_PPC {
         }
     }
 
+    __attribute__((always_inline))
     inline void add_save_acc(acc_t * ACC, int64_t ii, int64_t jj) {
         vec_t vec_C[4];
         __builtin_mma_disassemble_acc(vec_C, ACC);
Repository: vuejs/vue
Pull request: fix(props): support BigInt in props type validation

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<!-- PULL REQUEST TEMPLATE -->
<!-- (Update "[ ]" to "[x]" to check a box) -->

**What kind of change does this PR introduce?** (check at least one)

- [ ] Bugfix
- [x] Feature
- [ ] Code style update
- [ ] Refactor
- [ ] Build-related changes
- [ ] Other, please describe:

**Does this PR introduce a breaking change?** (check one)

- [ ] Yes
- [x] No

If yes, please describe the impact and migration path for existing applications:

**The PR fulfills these requirements:**

- [x] It's submitted to the `dev` branch for v2.x (or to a previous version branch), _not_ the `master` branch
- [ ] When resolving a specific issue, it's referenced in the PR's title (e.g. `fix #xxx[,#xxx]`, where "xxx" is the issue number)
- [x] All tests are passing: https://github.com/vuejs/vue/blob/dev/.github/CONTRIBUTING.md#development-setup
- [x] New/updated tests are included

If adding a **new feature**, the PR's description includes:
- [x] A convincing reason for adding this feature (to avoid wasting your time, it's best to open a suggestion issue first and wait for approval before working on it)

**Other information:**
Just a simple addition to make BigInt validation work.

Close #11126

Changed files:
filename: src/core/util/props.js
patch: @@ -147,7 +147,7 @@ function assertProp (
   }
 }
 
-const simpleCheckRE = /^(String|Number|Boolean|Function|Symbol)$/
+const simpleCheckRE = /^(String|Number|Boolean|Function|Symbol|BigInt)$/
 
 function assertType (value: any, type: Function, vm: ?Component): {
   valid: boolean;

filename: test/unit/features/options/props.spec.js
patch: @@ -252,6 +252,16 @@ describe('Options props', () => {
         expect('Expected String, Number, got Symbol').toHaveBeenWarned()
       })
     }
+	
+    if (typeof BigInt !== 'undefined') {
+      /* global BigInt */
+      it('bigint', () => {
+        makeInstance(BigInt(100), BigInt)
+        expect(console.error.calls.count()).toBe(0)
+        makeInstance({}, BigInt)
+        expect('Expected BigInt, got Object').toHaveBeenWarned()
+      })
+    }
 
     it('custom constructor', () => {
       function Class () {}
Repository: vercel/next.js
Pull request: fix(router): support BigInt in query parameters

**What changed?**
`stringifyUrlQueryParam` now handles `bigint` type properly.

**Why?**
BigInt is a valid JavaScript primitive but was being converted to empty string:

Before:
```js
stringifyUrlQueryParam(123n) // '' ❌
```

After:
```js
stringifyUrlQueryParam(123n) // '123' ✓
```

**Use case:**
Query parameters with large integers (IDs, timestamps) often use BigInt to avoid precision loss.

**Testing**
Manual testing with BigInt values.

Changed files:
filename: packages/next/src/shared/lib/router/utils/querystring.ts
patch: @@ -24,7 +24,8 @@ function stringifyUrlQueryParam(param: unknown): string {
 
   if (
     (typeof param === 'number' && !isNaN(param)) ||
-    typeof param === 'boolean'
+    typeof param === 'boolean' ||
+    typeof param === 'bigint'
   ) {
     return String(param)
   } else {
Repository: huggingface/transformers
Pull request: benchmark-v2: minor fix for benchmark-v2, kernel is not correctly called

kernels is not correctly called in benchmark_v2, should register before kernelize, see https://github.com/huggingface/transformers/blob/10e97cd508218546ef681a2c9b4c519ac0d927c3/src/transformers/modeling_utils.py#L3607, use 'use_kernels' in AutoModelForCausalLM.from_pretrained could handle register kernels and kernelize


Changed files:
filename: benchmark_v2/framework/benchmark_config.py
patch: @@ -40,13 +40,15 @@ def is_fa2_or_kernel_available() -> bool:
     try:
         from kernels import get_kernel
 
-        get_kernel("kernels-community/flash-attn")
+        # TODO: Pass the 'version' kwarg to specify the binary version once kernels >= 0.12.0 is supported.
+        get_kernel("kernels-community/flash-attn2")
     except Exception as _:
         logger.warning(
             "flash_attention_2 is not available. kernels is installed, but the flash_attn kernel is not available."
             "Benchmarking flash_attention_2 will not be possible."
         )
         return False
+    return True
 
 
 class BenchmarkConfig:

filename: benchmark_v2/framework/benchmark_runner.py
patch: @@ -205,14 +205,16 @@ def setup_benchmark(self, model_id: str, config: BenchmarkConfig) -> None:
         # Load model
         self.logger.debug(f"Loading model {model_id} on device {config.device}...")
         dtype = getattr(torch, config.dtype.removeprefix("torch."))
+        use_kernels = config.kernelize and kernelize is not None and Mode is not None
         self.model = AutoModelForCausalLM.from_pretrained(
-            model_id, dtype=dtype, attn_implementation=config.attn_implementation, generation_config=generation_config
+            model_id,
+            dtype=dtype,
+            attn_implementation=config.attn_implementation,
+            generation_config=generation_config,
+            use_kernels=use_kernels,
+            device_map=config.device,
         )
-        self.model = self.model.eval().to(config.device)
-
-        # Kernelize the model if needed
-        if config.kernelize and kernelize is not None and Mode is not None:
-            self.model = kernelize(self.model, mode=Mode.INFERENCE)
+        self.model = self.model.eval()
 
     def run_benchmark(self, config: BenchmarkConfig, num_tokens_to_profile: int = 0) -> BenchmarkResult | None:
         """Run a single benchmark with the given model ID and config."""
Repository: nodejs/node
Pull request: lib: recycle queues

Avoid creating unnecessary garbage in the old space.

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    it.

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Changed files:
filename: lib/internal/fixed_queue.js
patch: @@ -89,8 +89,10 @@ class FixedCircularBuffer {
 }
 
 module.exports = class FixedQueue {
+  static #pool = [];
+
   constructor() {
-    this.head = this.tail = new FixedCircularBuffer();
+    this.head = this.tail = FixedQueue.#pool.pop() ?? new FixedCircularBuffer();
   }
 
   isEmpty() {
@@ -101,7 +103,7 @@ module.exports = class FixedQueue {
     if (this.head.isFull()) {
       // Head is full: Creates a new queue, sets the old queue's `.next` to it,
       // and sets it as the new main queue.
-      this.head = this.head.next = new FixedCircularBuffer();
+      this.head = this.head.next = FixedQueue.#pool.pop() ?? new FixedCircularBuffer();
     }
     this.head.push(data);
   }
@@ -113,6 +115,12 @@ module.exports = class FixedQueue {
       // If there is another queue, it forms the new tail.
       this.tail = tail.next;
       tail.next = null;
+      tail.bottom = 0;
+      tail.top = 0;
+
+      if (FixedQueue.#pool.length < 64) {
+        FixedQueue.#pool.push(tail); // Recycle old tail
+      }
     }
     return next;
   }
Repository: ollama/ollama
Pull request: fix mllama conversion

cross attention Q and K projections needs to have their heads swapped, similar to non-cross attention Q and K tensors

Changed files:
filename: convert/convert_llama.go
patch: @@ -139,7 +139,8 @@ func (p *llamaModel) Tensors(ts []Tensor) []*ggml.Tensor {
 	}
 
 	for _, t := range ts {
-		if strings.HasSuffix(t.Name(), "attn_q.weight") || strings.HasSuffix(t.Name(), "attn_k.weight") {
+		if strings.HasSuffix(t.Name(), "attn_q.weight") || strings.HasSuffix(t.Name(), "attn_k.weight") ||
+			strings.HasSuffix(t.Name(), "attn_q_proj.weight") || strings.HasSuffix(t.Name(), "attn_k_proj.weight") {
 			if !p.skipRepack {
 				t.SetRepacker(p.repack)
 			}
@@ -181,9 +182,9 @@ func (p *llamaModel) repack(name string, data []float32, shape []uint64) ([]floa
 	}
 
 	var heads uint32
-	if strings.HasSuffix(name, "attn_q.weight") {
+	if strings.HasSuffix(name, "attn_q.weight") || strings.HasSuffix(name, "attn_q_proj.weight") {
 		heads = p.NumAttentionHeads
-	} else if strings.HasSuffix(name, "attn_k.weight") {
+	} else if strings.HasSuffix(name, "attn_k.weight") || strings.HasSuffix(name, "attn_k_proj.weight") {
 		heads = cmp.Or(p.NumKeyValueHeads, p.NumAttentionHeads)
 	} else {
 		return nil, fmt.Errorf("unknown tensor for repack: %s", name)
Repository: ggml-org/llama.cpp
Pull request: common : fix typo in debug log ('extracft' -> 'extract')


Changed files:
filename: common/chat-diff-analyzer.cpp
patch: @@ -409,7 +409,7 @@ void analyze_reasoning::compare_reasoning_scope() {
             if (result.result.success()) {
                 end = trim_trailing_whitespace(result.tags["post"]);
             } else {
-                LOG_DBG(ANSI_ORANGE "%s: Unable to extracft reasoning markers, falling back to reasoning = NONE\n" ANSI_RESET, __func__);
+                LOG_DBG(ANSI_ORANGE "%s: Unable to extract reasoning markers, falling back to reasoning = NONE\n" ANSI_RESET, __func__);
                 mode = reasoning_mode::NONE;
             }
         }
Repository: ollama/ollama
Pull request: ggml: update qwen25vl vision size estimate


Changed files:
filename: fs/ggml/ggml.go
patch: @@ -6,7 +6,6 @@ import (
 	"fmt"
 	"io"
 	"log/slog"
-	"math"
 	"slices"
 	"strings"
 
@@ -653,24 +652,15 @@ func (llm GGML) VisionGraphSize() (weights, graphSize uint64) {
 			numPatches*numPatches*headCount)
 	case "qwen25vl":
 		maxPixels := uint64(llm.KV().Uint("vision.max_pixels", 28*28*1280))
-		mergeSize := uint64(llm.KV().Uint("vision.spatial_merge_size", 2))
-		temporalPatchSize := uint64(2)
-
-		// Calculate max possible patches based on max_pixels
-		maxHeight := uint64(math.Sqrt(float64(maxPixels)))
-		maxWidth := maxPixels / maxHeight
-		maxGridHeight := maxHeight / patchSize
-		maxGridWidth := maxWidth / patchSize
-		// Account for merged patches (2x2 grid)
-		numPatches := (maxGridHeight * maxGridWidth) / (mergeSize * mergeSize)
-
-		// Calculate graph size based on typical operations in ProcessImage and createPatches
+
+		numPatches := maxPixels / (patchSize * patchSize)
+
 		graphSize = 4 * (maxPixels*numChannels + // Original image storage
 			// Normalized pixels
 			maxPixels*numChannels +
-			// Patches storage (numPatches * channels * temporalPatchSize * patchSize^2)
-			numPatches*numChannels*temporalPatchSize*patchSize*patchSize +
-			// Self-attention calculations (similar to other architectures)
+			// Patches storage (numPatches * channels * patchSize^2)
+			numPatches*numChannels*patchSize*patchSize +
+			// Self-attention calculations
 			numPatches*numPatches*headCount +
 			// Additional buffer for processing
 			embeddingLength*numPatches)
Repository: vuejs/vue
Pull request: fix(compiler): Allow BigInt usage in templates

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<!-- (Update "[ ]" to "[x]" to check a box) -->

**What kind of change does this PR introduce?** (check at least one)

- [ ] Bugfix
- [ x ] Feature
- [ ] Code style update
- [ ] Refactor
- [ ] Build-related changes
- [ ] Other, please describe:

**Does this PR introduce a breaking change?** (check one)

- [ ] Yes
- [ x ] No

If yes, please describe the impact and migration path for existing applications:

**The PR fulfills these requirements:**

- [ x ] It's submitted to the `dev` branch for v2.x (or to a previous version branch), _not_ the `master` branch
- [ x ] When resolving a specific issue, it's referenced in the PR's title (e.g. `fix #xxx[,#xxx]`, where "xxx" is the issue number)
- [ x ] All tests are passing: https://github.com/vuejs/vue/blob/dev/.github/CONTRIBUTING.md#development-setup
- [ x ] New/updated tests are included

If adding a **new feature**, the PR's description includes:
- [ x ] A convincing reason for adding this feature (to avoid wasting your time, it's best to open a suggestion issue first and wait for approval before working on it)

**Other information:**
Hello!
This pull request is related to issue #11126 . Now we can use BigInt in "Mustache" syntax.
I had already added test for this feature in "test/unit/features/filter/filter.spec.js".
And all tests are passing.

Close #11126

Changed files:
filename: src/core/instance/proxy.js
patch: @@ -9,7 +9,7 @@ if (process.env.NODE_ENV !== 'production') {
   const allowedGlobals = makeMap(
     'Infinity,undefined,NaN,isFinite,isNaN,' +
     'parseFloat,parseInt,decodeURI,decodeURIComponent,encodeURI,encodeURIComponent,' +
-    'Math,Number,Date,Array,Object,Boolean,String,RegExp,Map,Set,JSON,Intl,' +
+    'Math,Number,Date,Array,Object,Boolean,String,RegExp,Map,Set,JSON,Intl,BigInt,' +
     'require' // for Webpack/Browserify
   )
 

filename: test/unit/features/filter/filter.spec.js
patch: @@ -194,4 +194,11 @@ describe('Filters', () => {
   it('support template string', () => {
     expect(parseFilters('`a | ${b}c` | d')).toBe('_f("d")(`a | ${b}c`)')
   })
+
+  it('bigint support', () => {
+    const vm = new Vue({
+      template: `<div>{{ BigInt(BigInt(10000000)) + BigInt(2000000000n) * 3000000n }}</div>`
+    }).$mount()
+    expect(vm.$el.textContent).toBe('6000000010000000')
+  })
 })
Repository: ggml-org/llama.cpp
Pull request: ai : do not run bash commands in the prompt

cont #20790 

The prompt for the agent was completely broken because the backticks that I added effectively executed the commands and placed their output inside the prompt text 😐 

Also allow `gh search`.

Changed files:
filename: .github/workflows/ai-issues.yml
patch: @@ -26,7 +26,8 @@ jobs:
             {
               "bash": {
                 "*": "deny",
-                "gh issue*": "allow"
+                "gh issue*": "allow",
+                "gh search*": "allow"
               },
               "webfetch": "deny"
             }
@@ -38,19 +39,17 @@ jobs:
 
           Issue number: ${{ github.event.issue.number }}
 
-          Lookup the contents of the issue using the following `gh` command:
+          Lookup the contents of the issue using the following 'gh' command:
 
-          ```bash
           gh issue view ${{ github.event.issue.number }} --json title,body,url,number
-          ```
 
           Next, perform the following task and then post a SINGLE comment (if needed).
 
           ---
 
           TASK : FIND RELATED ISSUES
 
-          Using the `gh` CLI tool, search through existing issues on Github.
+          Using the 'gh' CLI tool, search through existing issues on Github.
           Find related or similar issues to the newly created one and list them.
           Do not list the new issue itself (it is #${{ github.event.issue.number }}).
 
@@ -83,5 +82,5 @@ jobs:
             - Do not include the comment tags in your actual comment.
             - Post at most ONE comment combining all findings.
             - If you didn't find issues that are related enough, post nothing.
-            - You have access only to the `gh` CLI tool - don't try to use other tools.
+            - You have access only to the 'gh' CLI tool - don't try to use other tools.
           "
Repository: facebook/react
Pull request: [DevTools] fix: handle store mutations synchronously in TreeContext

If there is a commit that removes the currently inspected (selected) elements in the Components tree, we are going to kick off the transition to re-render the Tree. The elements will be re-rendered with the previous inspectedElementID, which was just removed and all consecutive calls to store object with this id would produce errors, since this element was just removed.

We should handle store mutations synchronously. Doesn't make sense to start a transition in this case, because Elements depend on the TreeState and could make calls to store in render function.

Before:
<img width="2286" height="1734" alt="Screenshot 2025-08-06 at 17 41 14" src="https://github.com/user-attachments/assets/97d92220-3488-47b2-aa6b-70fa39345f6b" />


After:

https://github.com/user-attachments/assets/3da36aff-6987-4b76-b741-ca59f829f8e6

Changed files:
filename: packages/react-devtools-shared/src/devtools/views/Components/TreeContext.js
patch: @@ -954,7 +954,7 @@ function TreeContextController({
       Array<number>,
       Map<number, number>,
     ]) => {
-      transitionDispatch({
+      dispatch({
         type: 'HANDLE_STORE_MUTATION',
         payload: [addedElementIDs, removedElementIDs],
       });
@@ -965,7 +965,7 @@ function TreeContextController({
       // At the moment, we can treat this as a mutation.
       // We don't know which Elements were newly added/removed, but that should be okay in this case.
       // It would only impact the search state, which is unlikely to exist yet at this point.
-      transitionDispatch({
+      dispatch({
         type: 'HANDLE_STORE_MUTATION',
         payload: [[], new Map()],
       });