helloadhavan/github_issues huggingface dataset:
Repository: ggml-org/llama.cpp
Pull request: metal : fix API availability warnings for legacy iOS/macOS targets

This PR resolves a compiler warning in ggml-metal-device.m where recommendedMaxWorkingSetSize is accessed on iOS versions earlier than 16.0 or macOS earlier than 10.12.

Technical Rationale: While llama.cpp moves towards supporting modern APIs, maintaining build health for established deployment targets (e.g., iOS 15.0) remains essential for broader hardware compatibility.

Key Changes:

Wrapped recommendedMaxWorkingSetSize in @available guards to silence availability warnings.

Implemented a heuristic fallback to maxBufferLength. On legacy systems, this provides the inference engine with a valid physical boundary, ensuring robust model loading instead of defaulting to an uninitialized or zero value.

Testing:

Verified zero warnings with Xcode 16 on a deployment target of iOS 15.0.

Confirmed successful compilation on macOS.


Changed files:
filename: ggml/src/ggml-metal/ggml-metal-device.m
patch: @@ -785,8 +785,12 @@ ggml_metal_device_t ggml_metal_device_init(void) {
             dev->props.op_offload_min_batch_size  = getenv("GGML_OP_OFFLOAD_MIN_BATCH") ? atoi(getenv("GGML_OP_OFFLOAD_MIN_BATCH")) : 32;
 
             dev->props.max_buffer_size            = dev->mtl_device.maxBufferLength;
-            dev->props.max_working_set_size       = dev->mtl_device.recommendedMaxWorkingSetSize;
             dev->props.max_theadgroup_memory_size = dev->mtl_device.maxThreadgroupMemoryLength;
+            if (@available(macOS 10.12, iOS 16.0, *)) {
+                dev->props.max_working_set_size   = dev->mtl_device.recommendedMaxWorkingSetSize;
+            } else {
+                dev->props.max_working_set_size   = dev->mtl_device.maxBufferLength;
+            }
 
             strncpy(dev->props.name, [[dev->mtl_device name] UTF8String], sizeof(dev->props.name) - 1);
Repository: huggingface/transformers
Pull request: fix some cases in xpu

tests/models/sam_hq/test_modeling_sam_hq.py::SamHQModelIntegrationTest::test_inference_mask_generation_no_point
tests/models/mpt/test_modeling_mpt.py::MptIntegrationTests::test_model_logits
@ydshieh please help review

Changed files:
filename: tests/models/mpt/test_modeling_mpt.py
patch: @@ -528,6 +528,7 @@ def test_generation_batched(self):
         for i, predicted_output in enumerate(decoded_outputs):
             self.assertEqual(predicted_output, expected_output[i])
 
+    @require_deterministic_for_xpu
     def test_model_logits(self):
         model_id = "mosaicml/mpt-7b"
 
@@ -546,7 +547,7 @@ def test_model_logits(self):
         expected_slices = Expectations(
             {
                 (None, None): torch.Tensor([-0.2520, -0.2178, -0.1953]),
-                ("xpu", 3): torch.Tensor([-0.2090, -0.2061, -0.1465]),
+                ("xpu", 3): torch.Tensor([-0.2656, -0.2246, -0.2637]),
                 ("cuda", 8): torch.Tensor([-0.2559, -0.2227, -0.2217]),
                 # TODO: This is quite a bit off, check BnB
                 ("rocm", (9, 5)): torch.Tensor([-0.3008, -0.1309, -0.1562]),

filename: tests/models/sam_hq/test_modeling_sam_hq.py
patch: @@ -801,6 +801,7 @@ def test_inference_mask_generation_no_point(self):
             {
                 (None, None): [-13.1695, -14.6201, -14.8989],
                 ("cuda", 8): [-7.6769, -9.6935, -9.8773],
+                ("xpu", None): [-7.6769, -9.6935, -9.8773],
             }
         )
         EXPECTED_MASKS = torch.tensor(expectations.get_expectation()).to(torch_device)
Repository: ggml-org/llama.cpp
Pull request: convert : yield Gemma3N custom_map tensors directly

cont #19064 #19084

Fixes #19079

Changed files:
filename: convert_hf_to_gguf.py
patch: @@ -6145,7 +6145,8 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
 
         if name.startswith("model.vision_tower.timm_model.blocks."):
             # Double-indexed block tensors through custom logic
-            new_name = self.custom_map(name)
+            yield (self.custom_map(name), data_torch)
+            return
         else:
             # Route non-repeating (conv_stem, msfa, embedding, etc.) and un-catched through tensor_mapping.py
             new_name = self.map_tensor_name(name)
Repository: electron/electron
Pull request: ci: ignore lost communication with server annotation in audit

#### Description of Change

<!--
Thank you for your Pull Request. Please provide a description above and review
the requirements below.

Contributors guide: https://github.com/electron/electron/blob/main/CONTRIBUTING.md
-->

This flake comes up a decent amount and is creating a lot of noise on the Slack messages for the audit, so filter it out.

#### Checklist
<!-- Remove items that do not apply. For completed items, change [ ] to [x]. -->

- [x] PR description included and stakeholders cc'd

#### Release Notes

Notes: none <!-- Please add a one-line description for app developers to read in the release notes, or 'none' if no notes relevant to app developers. Examples and help on special cases: https://github.com/electron/clerk/blob/main/README.md#examples -->


Changed files:
filename: .github/workflows/audit-branch-ci.yml
patch: @@ -73,6 +73,7 @@ jobs:
                       annotation_level === "failure" &&
                       !message.startsWith("Process completed with exit code") &&
                       !message.startsWith("Response status code does not indicate success") &&
+                      !message.startsWith("The hosted runner lost communication with the server") &&
                       !/Unable to make request/.test(message) &&
                       !/The requested URL returned error/.test(message),
                   )
Repository: vuejs/vue
Pull request: fix Flow lifecycle method name [Vue@2.x]

fix lifecycle method name `init` to `beforeCreate`


Changed files:
filename: flow/options.js
patch: @@ -34,7 +34,7 @@ declare type ComponentOptions = {
   render: () => VNode;
   staticRenderFns?: Array<() => VNode>;
   // lifecycle
-  init?: Function;
+  beforeCreate?: Function;
   created?: Function;
   beforeMount?: Function;
   mounted?: Function;
Repository: huggingface/transformers
Pull request: fix a typo: pbd -> pdb

# What does this PR do?

Fix a typo in the docstring of `output_to_pdb` method of `EsmforProteinFolding` class.


## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker @Cyrilvallez
- vision models: @yonigozlan @molbap
- audio models: @eustlb @ebezzam @vasqu
- multimodal models: @zucchini-nlp
- graph models: @clefourrier

Library:

- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- continuous batching: @remi-or @ArthurZucker @McPatate
- pipelines: @Rocketknight1
- tokenizers: @ArthurZucker and @itazap
- trainer: @SunMarc
- attention: @vasqu @ArthurZucker @CyrilVallez
- model loading (from pretrained, etc): @CyrilVallez
- distributed: @3outeille @ArthurZucker
- CIs: @ydshieh

Integrations:

- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization: @SunMarc @MekkCyber
- kernels: @MekkCyber @drbh
- peft: @BenjaminBossan @githubnemo

Devices/Backends:

- AMD ROCm: @ivarflakstad
- Intel XPU: @IlyasMoutawwakil
- Ascend NPU: @ivarflakstad 

Documentation: @stevhliu

Research projects are not maintained and should be taken as is.

 -->


Changed files:
filename: src/transformers/models/esm/modeling_esmfold.py
patch: @@ -2257,7 +2257,7 @@ def infer(
 
     @staticmethod
     def output_to_pdb(output: dict) -> list[str]:
-        """Returns the pbd (file) string from the model given the model output."""
+        """Returns the pdb (file) string from the model given the model output."""
         output = {k: v.to("cpu").numpy() for k, v in output.items()}
         pdbs = []
         final_atom_positions = atom14_to_atom37(output["positions"][-1], output)
Repository: facebook/react
Pull request: Add Profiler mode to fixtures even if React DevTools is not installed

Currently you need to do one of either:

1. Install React DevTools
2. Install React Refresh
3. Add Profiler component

To opt in to component level profiling.

It was a bit confusing that some of the fixtures was doing 2 which made them work while other was depending on if you had DevTools.

Really React Refresh shouldn't really opt you in I think.

Changed files:
filename: fixtures/flight/src/index.js
patch: @@ -1,5 +1,5 @@
 import * as React from 'react';
-import {use, Suspense, useState, startTransition} from 'react';
+import {use, Suspense, useState, startTransition, Profiler} from 'react';
 import ReactDOM from 'react-dom/client';
 import {createFromFetch, encodeReply} from 'react-server-dom-webpack/client';
 
@@ -54,14 +54,20 @@ async function hydrateApp() {
     }
   );
 
-  ReactDOM.hydrateRoot(document, <Shell data={root} />, {
-    // TODO: This part doesn't actually work because the server only returns
-    // form state during the request that submitted the form. Which means it
-    // the state needs to be transported as part of the HTML stream. We intend
-    // to add a feature to Fizz for this, but for now it's up to the
-    // metaframework to implement correctly.
-    formState: formState,
-  });
+  ReactDOM.hydrateRoot(
+    document,
+    <Profiler id="root">
+      <Shell data={root} />
+    </Profiler>,
+    {
+      // TODO: This part doesn't actually work because the server only returns
+      // form state during the request that submitted the form. Which means it
+      // the state needs to be transported as part of the HTML stream. We intend
+      // to add a feature to Fizz for this, but for now it's up to the
+      // metaframework to implement correctly.
+      formState: formState,
+    }
+  );
 }
 
 // Remove this line to simulate MPA behavior

filename: fixtures/ssr/src/index.js
patch: @@ -1,6 +1,12 @@
 import React from 'react';
+import {Profiler} from 'react';
 import {hydrateRoot} from 'react-dom/client';
 
 import App from './components/App';
 
-hydrateRoot(document, <App assets={window.assetManifest} />);
+hydrateRoot(
+  document,
+  <Profiler id="root">
+    <App assets={window.assetManifest} />
+  </Profiler>
+);
Repository: electron/electron
Pull request: build: bump version in .nvmrc to 22

#### Description of Change

build-tools requires Node v22. This updates our .nvmrc file to reflect that.

```
> e sync 
ERROR Node.js is not installed or does not meet minimum version requirements. Node.js 22.12.0 or higher must be installed to use build-tools.
```

https://github.com/electron/build-tools/blob/c13f4bdb50e65da46a4703f8f882079dd21fd99e/src/utils/prereqs.js#L6

#### Checklist
<!-- Remove items that do not apply. For completed items, change [ ] to [x]. -->

- [x] PR description included and stakeholders cc'd

#### Release Notes

Notes: none


Changed files:
filename: .nvmrc
patch: @@ -1 +1 @@
-20
+22
Repository: vuejs/vue
Pull request: Add type augmentation test 

Plugins of Vue.js usually add some static or instance properties/methods to `Vue` and some new options to component options.
I think it is good to provide how to augment their typings for the plugin developers.

I add the test code of such augmentation by this PR and it would be the example for it.


Changed files:
filename: types/test/augmentation-test.ts
patch: @@ -0,0 +1,35 @@
+import Vue = require("../index");
+
+declare module "../vue" {
+  // add instance property and method
+  interface Vue {
+    $instanceProperty: string;
+    $instanceMethod(): void;
+  }
+
+  // add static property and method
+  namespace Vue {
+    const staticProperty: string;
+    function staticMethod(): void;
+  }
+}
+
+// augment ComponentOptions
+declare module "../options" {
+  interface ComponentOptions<V extends Vue> {
+    foo?: string;
+  }
+}
+
+const vm = new Vue({
+  data: {
+    a: true
+  },
+  foo: "foo"
+});
+
+vm.$instanceProperty;
+vm.$instanceMethod();
+
+Vue.staticProperty;
+Vue.staticMethod();

filename: types/test/tsconfig.json
patch: @@ -14,7 +14,8 @@
     "../vue.d.ts",
     "options-test.ts",
     "plugin-test.ts",
-    "vue-test.ts"
+    "vue-test.ts",
+    "augmentation-test.ts"
   ],
   "compileOnSave": false
 }
Repository: huggingface/transformers
Pull request: Enable glm46v UTs on XPU

# What does this PR do?

This PR enabled testing of `glm46v` on XPU.

**Notes:**

1. [General] Directly running `Glm46VIntegrationTest` currently fails with error: 
```
FAILED tests/models/glm46v/test_modeling_glm46v.py::Glm46VIntegrationTest::test_small_model_integration_test_batch_flashatt2 - RuntimeError: Expected tensor for argument #1 'indices' to have one of the following scalar types: Long, Int; but got XPUBFloat16Type instead (while checking argumen...
```
Need to wait for `zai-org/GLM-4.1V-9B-Thinking` PR [21](https://huggingface.co/zai-org/GLM-4.1V-9B-Thinking/discussions/21) to be merged, or use the workaround: change [here](https://github.com/huggingface/transformers/blob/a5c903f877fda21e739027eed133e03162eb7712/src/transformers/models/glm46v/modeling_glm46v.py#L90) to `self.visual = AutoModel.from_config(config.vision_config.vision_config)`.

2. [XPU] Tests related to `flash_attention_2` need to wait for PR [41956](https://github.com/huggingface/transformers/pull/41956) to be merged.

Changed files:
filename: tests/models/glm46v/test_modeling_glm46v.py
patch: @@ -29,7 +29,7 @@
     require_deterministic_for_xpu,
     require_flash_attn,
     require_torch,
-    require_torch_gpu,
+    require_torch_accelerator,
     slow,
     torch_device,
 )
@@ -512,7 +512,7 @@ def test_small_model_integration_test_batch_different_resolutions(self):
 
     @slow
     @require_flash_attn
-    @require_torch_gpu
+    @require_torch_accelerator
     def test_small_model_integration_test_batch_flashatt2(self):
         model = Glm46VForConditionalGeneration.from_pretrained(
             "THUDM/GLM-4.1V-9B-Thinking",
@@ -547,7 +547,7 @@ def test_small_model_integration_test_batch_flashatt2(self):
 
     @slow
     @require_flash_attn
-    @require_torch_gpu
+    @require_torch_accelerator
     def test_small_model_integration_test_batch_wo_image_flashatt2(self):
         model = Glm46VForConditionalGeneration.from_pretrained(
             "THUDM/GLM-4.1V-9B-Thinking",
Repository: facebook/react
Pull request: [rcr] Relax react peer dep requirement


There's no real reason to restrict the React peer dep to non-experimental, so relax it.
---
[//]: # (BEGIN SAPLING FOOTER)
Stack created with [Sapling](https://sapling-scm.com). Best reviewed with [ReviewStack](https://reviewstack.dev/facebook/react/pull/31915).
* #31919
* #31918
* #31917
* #31916
* __->__ #31915
* #31920

Changed files:
filename: compiler/packages/react-compiler-runtime/package.json
patch: @@ -9,7 +9,7 @@
     "src"
   ],
   "peerDependencies": {
-    "react": "^17.0.0 || ^18.0.0 || ^19.0.0"
+    "react": "^17.0.0 || ^18.0.0 || ^19.0.0 || ^0.0.0-experimental"
   },
   "scripts": {
     "build": "rimraf dist && rollup --config --bundleConfigAsCjs",
Repository: nodejs/node
Pull request: doc: add missing CAA type to dns.resolveAny() & dnsPromises.resolveAny()

The AnyTraits::Parse() method in cares_wrap.cc will "Parse CAA records"
as the code comment suggests. The tables in doc should indicate this as
well.


Changed files:
filename: doc/api/dns.md
patch: @@ -540,6 +540,7 @@ will be present on the object:
 | --------- | ------------------------------------------------------------------------------------------------------------------------------------------------ |
 | `'A'`     | `address`/`ttl`                                                                                                                                  |
 | `'AAAA'`  | `address`/`ttl`                                                                                                                                  |
+| `'CAA'`   | Refer to [`dns.resolveCaa()`][]                                                                                                                  |
 | `'CNAME'` | `value`                                                                                                                                          |
 | `'MX'`    | Refer to [`dns.resolveMx()`][]                                                                                                                   |
 | `'NAPTR'` | Refer to [`dns.resolveNaptr()`][]                                                                                                                |
@@ -1309,6 +1310,7 @@ present on the object:
 | --------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `'A'`     | `address`/`ttl`                                                                                                                                          |
 | `'AAAA'`  | `address`/`ttl`                                                                                                                                          |
+| `'CAA'`   | Refer to [`dnsPromises.resolveCaa()`][]                                                                                                                  |
 | `'CNAME'` | `value`                                                                                                                                                  |
 | `'MX'`    | Refer to [`dnsPromises.resolveMx()`][]                                                                                                                   |
 | `'NAPTR'` | Refer to [`dnsPromises.resolveNaptr()`][]                                                                                                                |
Repository: vercel/next.js
Pull request: Turbopack: RuleCondition support resourceQuery contains

## What?

In utoopack, we have the situation to match modules with query like following case:

```ts
import styles from './index.less?modules';
```

To deal the case above into css modules(other case the same), so i need to use the rule condition to match the module with query like:

```rust
RuleCondition::ResourceQueryContains {
    extension: ".less".into(),
    query: "?modules".into(),
},
```

Changed files:
filename: turbopack/crates/turbopack/src/module_options/rule_condition.rs
patch: @@ -45,6 +45,7 @@ pub enum RuleCondition {
         glob: ReadRef<Glob>,
     },
     ResourceBasePathGlob(#[turbo_tasks(trace_ignore)] ReadRef<Glob>),
+    ResourceQueryContains(String),
 }
 
 impl RuleCondition {
@@ -282,6 +283,10 @@ impl RuleCondition {
                             FileContent::NotFound => return Ok(false),
                         }
                     }
+                    RuleCondition::ResourceQueryContains(query) => {
+                        let ident = source.ident().await?;
+                        return Ok(ident.query.contains(query));
+                    }
                 }
             }
         }
Repository: facebook/react
Pull request: [compiler] Fix broken fire snapshot


This was not committed in #31811
---
[//]: # (BEGIN SAPLING FOOTER)
Stack created with [Sapling](https://sapling-scm.com). Best reviewed with [ReviewStack](https://reviewstack.dev/facebook/react/pull/31920).
* #31919
* #31918
* #31917
* #31916
* #31915
* __->__ #31920

Changed files:
filename: compiler/packages/babel-plugin-react-compiler/src/__tests__/fixtures/compiler/transform-fire/error.invalid-rewrite-deps-spread.expect.md
patch: @@ -12,9 +12,12 @@ function Component(props) {
 
   const deps = [foo, props];
 
-  useEffect(() => {
-    fire(foo(props));
-  }, ...deps);
+  useEffect(
+    () => {
+      fire(foo(props));
+    },
+    ...deps
+  );
 
   return null;
 }
@@ -25,13 +28,13 @@ function Component(props) {
 ## Error
 
 ```
-  11 |   useEffect(() => {
-  12 |     fire(foo(props));
-> 13 |   }, ...deps);
-     |         ^^^^ Invariant: Cannot compile `fire`. You must use an array literal for an effect dependency array when that effect uses `fire()` (13:13)
-  14 |
-  15 |   return null;
-  16 | }
+  13 |       fire(foo(props));
+  14 |     },
+> 15 |     ...deps
+     |        ^^^^ Invariant: Cannot compile `fire`. You must use an array literal for an effect dependency array when that effect uses `fire()` (15:15)
+  16 |   );
+  17 |
+  18 |   return null;
 ```
           
       
\ No newline at end of file
Repository: ollama/ollama
Pull request: Better explain multi-gpu behavior

Fixes #5635 #5455 

This topic seems to come up ~weekly, so lets explain it more clearly in the docs, and expose the existing env var to force spreading over all GPUs.

Changed files:
filename: cmd/cmd.go
patch: @@ -1341,6 +1341,7 @@ func NewCLI() *cobra.Command {
 				envVars["OLLAMA_NUM_PARALLEL"],
 				envVars["OLLAMA_NOPRUNE"],
 				envVars["OLLAMA_ORIGINS"],
+				envVars["OLLAMA_SCHED_SPREAD"],
 				envVars["OLLAMA_TMPDIR"],
 				envVars["OLLAMA_FLASH_ATTENTION"],
 				envVars["OLLAMA_LLM_LIBRARY"],

filename: docs/faq.md
patch: @@ -272,4 +272,8 @@ The following server settings may be used to adjust how Ollama handles concurren
 - `OLLAMA_NUM_PARALLEL` - The maximum number of parallel requests each model will process at the same time.  The default will auto-select either 4 or 1 based on available memory.
 - `OLLAMA_MAX_QUEUE` - The maximum number of requests Ollama will queue when busy before rejecting additional requests. The default is 512
 
-Note: Windows with Radeon GPUs currently default to 1 model maximum due to limitations in ROCm v5.7 for available VRAM reporting.  Once ROCm v6.2 is available, Windows Radeon will follow the defaults above.  You may enable concurrent model loads on Radeon on Windows, but ensure you don't load more models than will fit into your GPUs VRAM.
\ No newline at end of file
+Note: Windows with Radeon GPUs currently default to 1 model maximum due to limitations in ROCm v5.7 for available VRAM reporting.  Once ROCm v6.2 is available, Windows Radeon will follow the defaults above.  You may enable concurrent model loads on Radeon on Windows, but ensure you don't load more models than will fit into your GPUs VRAM.
+
+## How does Ollama load models on multiple GPUs?
+
+Installing multiple GPUs of the same brand can be a great way to increase your available VRAM to load larger models.  When you load a new model, Ollama evaluates the required VRAM for the model against what is currently available.  If the model will entirely fit on any single GPU, Ollama will load the model on that GPU.  This typically provides the best performance as it reduces the amount of data transfering across the PCI bus during inference.  If the model does not fit entirely on one GPU, then it will be spread across all the available GPUs.
\ No newline at end of file