helloadhavan/github_issues huggingface dataset:
Repository: huggingface/transformers
Pull request: Fix Qwen3Next dtype API usage

This PR fixes an invalid PyTorch API usage in the Qwen3Next model.

### Changes
- Replaced `torch.get_current_dtype()` with `torch.get_default_dtype()` in both modular and modeling files
- This change fixes FLA (Flash Linear Attention) compatibility when using different dtypes like float32/float16

### Technical Details
- The original code was using a non-existent PyTorch API `get_current_dtype()`
- The correct API is `get_default_dtype()` which returns the global default dtype setting
- This change ensures proper dtype handling in the Qwen3Next GatedDeltaNet normalization layer

### Testing
The changes have been tested with `make fixup` and pass the repository consistency checks.

Fix #41732 

Changed files:
filename: src/transformers/models/qwen3_next/modeling_qwen3_next.py
patch: @@ -638,7 +638,7 @@ def __init__(self, config: Qwen3NextConfig, layer_idx: int):
                 eps=self.layer_norm_epsilon,
                 activation=self.activation,
                 device=torch.cuda.current_device(),
-                dtype=config.dtype if config.dtype is not None else torch.get_current_dtype(),
+                dtype=config.dtype if config.dtype is not None else torch.get_default_dtype(),
             )
         )
 

filename: src/transformers/models/qwen3_next/modular_qwen3_next.py
patch: @@ -473,7 +473,7 @@ def __init__(self, config: Qwen3NextConfig, layer_idx: int):
                 eps=self.layer_norm_epsilon,
                 activation=self.activation,
                 device=torch.cuda.current_device(),
-                dtype=config.dtype if config.dtype is not None else torch.get_current_dtype(),
+                dtype=config.dtype if config.dtype is not None else torch.get_default_dtype(),
             )
         )
Repository: ollama/ollama
Pull request: Doc updates for supporting Llama3.2


Changed files:
filename: docs/import.md
patch: @@ -32,7 +32,7 @@ ollama run my-model
 
 Ollama supports importing adapters based on several different model architectures including:
 
-  * Llama (including Llama 2, Llama 3, and Llama 3.1);
+  * Llama (including Llama 2, Llama 3, Llama 3.1, and Llama 3.2);
   * Mistral (including Mistral 1, Mistral 2, and Mixtral); and
   * Gemma (including Gemma 1 and Gemma 2)
 
@@ -67,14 +67,12 @@ ollama run my-model
 
 Ollama supports importing models for several different architectures including:
 
-  * Llama (including Llama 2, Llama 3, and Llama 3.1);
+  * Llama (including Llama 2, Llama 3, Llama 3.1, and Llama 3.2);
   * Mistral (including Mistral 1, Mistral 2, and Mixtral);
   * Gemma (including Gemma 1 and Gemma 2); and
   * Phi3
 
-This includes importing foundation models as well as any fine tuned models which which have been _fused_ with a foundation model.
-
-
+This includes importing foundation models as well as any fine tuned models which have been _fused_ with a foundation model.
 ## Importing a GGUF based model or adapter
 
 If you have a GGUF based model or adapter it is possible to import it into Ollama. You can obtain a GGUF model or adapter by:

filename: docs/modelfile.md
patch: @@ -120,7 +120,7 @@ FROM <model directory>
 The model directory should contain the Safetensors weights for a supported architecture.
 
 Currently supported model architectures:
-  * Llama (including Llama 2, Llama 3, and Llama 3.1)
+  * Llama (including Llama 2, Llama 3, Llama 3.1, and Llama 3.2)
   * Mistral (including Mistral 1, Mistral 2, and Mixtral)
   * Gemma (including Gemma 1 and Gemma 2)
   * Phi3
Repository: vercel/next.js
Pull request: [ci] Cancel in-progress build_and_deploy for PRs

Broke in https://github.com/vercel/next.js/pull/50436

Doesn't cancel pushes on `canary` as expected:
<img width="2552" height="336" alt="CleanShot 2025-12-15 at 21 46 10@2x" src="https://github.com/user-attachments/assets/279b6016-058f-4d04-81d2-77d0933355d1" />


Changed files:
filename: .github/workflows/build_and_deploy.yml
patch: @@ -11,6 +11,12 @@ on:
     types: [opened, synchronize]
   workflow_dispatch:
 
+concurrency:
+  # Limit concurrent runs to 1 per PR,
+  # but allow concurrent runs on push if they potentially use different source code
+  group: ${{ github.event_name == 'pull_request' && format('{0}-pr-{1}', github.workflow, github.ref_name) || format('{0}-sha-{1}', github.workflow, github.sha) }}
+  cancel-in-progress: true
+
 env:
   NAPI_CLI_VERSION: 2.18.4
   TURBO_VERSION: 2.5.5

filename: .github/workflows/cancel.yml
patch: @@ -13,5 +13,5 @@ jobs:
     steps:
       - uses: styfle/cancel-workflow-action@0.12.1
         with:
-          workflow_id: 444987, 57419851
+          workflow_id: 57419851
           access_token: ${{ github.token }}
Repository: ollama/ollama
Pull request: runner.go: Propagate panics back to the user.

This is a partial revert of 8a35bb92
"runner.go: Increase survivability of main processing loop", removing the panic handler.

Although we want to avoid errors taking down the runner, we also should make the user aware of problems when they happen. In the future, we can restructure things so both parts are true.

Changed files:
filename: llama/runner/runner.go
patch: @@ -14,7 +14,6 @@ import (
 	"path/filepath"
 	"regexp"
 	"runtime"
-	"runtime/debug"
 	"strconv"
 	"strings"
 	"sync"
@@ -340,15 +339,6 @@ func (s *Server) run(ctx context.Context) {
 // it should only be responsible for accepting tokens or embeddings and
 // processing batches as fast as possible
 func (s *Server) processBatch(tokenBatch *llama.Batch, embedBatch *llama.Batch) {
-	// Try to keep going even if we hit a panic so that corner cases don't take the whole
-	// runner down. In most cases, this will result in dropping the tokens that we are currently
-	// processing and then continuing with what is remaining.
-	defer func() {
-		if err := recover(); err != nil {
-			slog.Error("error while processing batch", "error", err, "stack", debug.Stack())
-		}
-	}()
-
 	s.mu.Lock()
 	for s.allNil() {
 		s.cond.Wait() // Wait until an item is added
Repository: vercel/next.js
Pull request: [ci] Stop building when creating release

Lerna implicitly runs the `release` scripts in each package on `lerna version` which builds from source for some packages (e.g. https://github.com/vercel/next.js/actions/runs/20215733181/job/58028355225#step:14:151)

The actual building and publishing of artifacts happens in build_and_deploy.yml, so we don't need to do it when creating the release.

Couldn't find any other explicit usage of the `release` scripts.

## Test plan

- [x] Will trigger canary release once that's merged: This didn't actually work. Following up in 

Changed files:
filename: packages/create-next-app/package.json
patch: @@ -22,10 +22,9 @@
   ],
   "scripts": {
     "dev": "ncc build ./index.ts -w -o dist/",
-    "prerelease": "node ../../scripts/rm.mjs dist",
-    "release": "ncc build ./index.ts -o ./dist/ --minify --no-cache --no-source-map-register",
+    "prebuild": "node ../../scripts/rm.mjs dist",
+    "build": "ncc build ./index.ts -o ./dist/ --minify --no-cache --no-source-map-register",
     "prepublishOnly": "cd ../../ && turbo run build",
-    "build": "pnpm release",
     "lint-fix": "pnpm prettier -w --plugin prettier-plugin-tailwindcss 'templates/*-tw/{ts,js}/{app,pages}/**/*.{js,ts,tsx}'"
   },
   "devDependencies": {

filename: packages/next-env/package.json
patch: @@ -22,10 +22,10 @@
   ],
   "scripts": {
     "dev": "ncc build ./index.ts -w -o dist/",
-    "prerelease": "node ../../scripts/rm.mjs dist",
+    "prebuild:source": "node ../../scripts/rm.mjs dist",
     "types": "tsc --declaration --emitDeclarationOnly --declarationDir dist",
-    "release": "ncc build ./index.ts -o ./dist/ --minify --no-cache --no-source-map-register",
-    "build": "pnpm release && pnpm types",
+    "build:source": "ncc build ./index.ts -o ./dist/ --minify --no-cache --no-source-map-register",
+    "build": "pnpm build:source && pnpm types",
     "prepublishOnly": "cd ../../ && turbo run build"
   },
   "devDependencies": {

filename: packages/next/package.json
patch: @@ -79,8 +79,7 @@
   },
   "scripts": {
     "dev": "cross-env NEXT_SERVER_NO_MANGLE=1 taskr",
-    "release": "taskr release",
-    "build": "pnpm release",
+    "build": "taskr release",
     "prepublishOnly": "cd ../../ && turbo run build",
     "types": "tsc --project tsconfig.build.json --declaration --emitDeclarationOnly --stripInternal --declarationDir dist",
     "typescript": "tsec --noEmit",
Repository: ollama/ollama
Pull request: runner.go: Increase survivability of main processing loop

Currently, if an error occurs during the prep stages (such as tokenizing) of a single request, it will only affect that request. However, if an error happens during decoding, it can take down the entire runner.

Instead, it's better to drop the tokens that triggered the error and try to keep going. However, we also need to stop when we run out of tokens, otherwise, this just causes an infinite loop. This is likely the cause of at least some of the hanging issues that have been reported.

Bug #7573

Changed files:
filename: llama/runner/runner.go
patch: @@ -14,6 +14,7 @@ import (
 	"path/filepath"
 	"regexp"
 	"runtime"
+	"runtime/debug"
 	"strconv"
 	"strings"
 	"sync"
@@ -339,6 +340,15 @@ func (s *Server) run(ctx context.Context) {
 // it should only be responsible for accepting tokens or embeddings and
 // processing batches as fast as possible
 func (s *Server) processBatch(tokenBatch *llama.Batch, embedBatch *llama.Batch) {
+	// Try to keep going even if we hit a panic so that corner cases don't take the whole
+	// runner down. In most cases, this will result in dropping the tokens that we are currently
+	// processing and then continuing with what is remaining.
+	defer func() {
+		if err := recover(); err != nil {
+			slog.Error("error while processing batch", "error", err, "stack", debug.Stack())
+		}
+	}()
+
 	s.mu.Lock()
 	for s.allNil() {
 		s.cond.Wait() // Wait until an item is added
@@ -357,6 +367,14 @@ func (s *Server) processBatch(tokenBatch *llama.Batch, embedBatch *llama.Batch)
 			continue
 		}
 
+		// If an error occurred during the processing of a previous batch then we may have emptied the inputs
+		// without adding a new one. In this case, end the sequence rather than infinite looping.
+		if len(seq.inputs) == 0 {
+			slog.Error("removing sequence due to no input tokens", "index", seqIdx, "cache id", seq.cache.Id)
+			s.removeSequence(seqIdx, "error")
+			continue
+		}
+
 		// if past the num predict limit
 		if seq.numPredict > 0 && seq.numPredicted >= seq.numPredict {
 			s.removeSequence(seqIdx, "limit")
Repository: facebook/react
Pull request: [ci] Add daily stale branch cache cleanup


Cleans up stale non-main caches daily
---
[//]: # (BEGIN SAPLING FOOTER)
Stack created with [Sapling](https://sapling-scm.com). Best reviewed with [ReviewStack](https://reviewstack.dev/facebook/react/pull/32691).
* #32692
* __->__ #32691

Changed files:
filename: .github/workflows/shared_cleanup_stale_branch_caches.yml
patch: @@ -0,0 +1,33 @@
+# https://github.com/actions/cache/blob/main/tips-and-workarounds.md#force-deletion-of-caches-overriding-default-cache-eviction-policy
+
+name: (Shared) Cleanup Stale Branch Caches
+on:
+  schedule:
+    - cron: 0 0 * * *
+  workflow_dispatch:
+
+jobs:
+  cleanup:
+    runs-on: ubuntu-latest
+    permissions:
+      # `actions:write` permission is required to delete caches
+      #   See also: https://docs.github.com/en/rest/actions/cache?apiVersion=2022-11-28#delete-a-github-actions-cache-for-a-repository-using-a-cache-id
+      actions: write
+      contents: read
+    steps:
+      - name: Cleanup
+        run: |
+          echo "Fetching list of cache keys"
+          cacheKeysForPR=$(gh cache list --limit 100 --json id,ref --jq '.[] | select(.ref != "refs/heads/main") | .id')
+
+          ## Setting this to not fail the workflow while deleting cache keys.
+          set +e
+          for cacheKey in $cacheKeysForPR
+          do
+              gh cache delete $cacheKey
+              echo "Deleting $cacheKey"
+          done
+          echo "Done"
+        env:
+          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          GH_REPO: ${{ github.repository }}
Repository: nodejs/node
Pull request: tools: lint Temporal global


Changed files:
filename: lib/eslint.config_partial.mjs
patch: @@ -230,6 +230,12 @@ export default [
           name: 'SharedArrayBuffer',
           message: "Use `const { constructSharedArrayBuffer } = require('internal/util');` instead of the global.",
         },
+        // Temporal is not available in primordials because it can be
+        // disabled with --no-harmony-temporal CLI flag.
+        {
+          name: 'Temporal',
+          message: 'Use `const { Temporal } = globalThis;` instead of the global.',
+        },
         {
           name: 'TextDecoder',
           message: "Use `const { TextDecoder } = require('internal/encoding');` instead of the global.",
Repository: vuejs/vue
Pull request: fix typos in src/server/bundle-renderer/create-bundle-runner.js

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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
- [ ] Feature
- [ ] Code style update
- [x] 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:**
avaialbe => available

Changed files:
filename: src/server/bundle-renderer/create-bundle-runner.js
patch: @@ -115,7 +115,7 @@ export function createBundleRunner (entry, files, basedir, runInNewContext) {
         // styles injected by vue-style-loader.
         initialContext = sandbox.__VUE_SSR_CONTEXT__ = {}
         runner = evaluate(entry, sandbox)
-        // On subsequent renders, __VUE_SSR_CONTEXT__ will not be avaialbe
+        // On subsequent renders, __VUE_SSR_CONTEXT__ will not be available
         // to prevent cross-request pollution.
         delete sandbox.__VUE_SSR_CONTEXT__
         if (typeof runner !== 'function') {
Repository: ggml-org/llama.cpp
Pull request: [server] save generated text for the /slots endpoint (for LLAMA_SERVER_SLOTS_DEBUG=1)

when the environment variable `LLAMA_SERVER_SLOTS_DEBUG=1` is set, the `/slots` endpoint allows reading the last prompt and the last generated text.

The slot reset also deletes the generated text, so it always looks empty when the slot is idle.

This PR adds a variable to allow keeping the data until a new generation is started.

Changed files:
filename: tools/server/server-context.cpp
patch: @@ -77,6 +77,7 @@ struct server_slot {
     size_t last_nl_pos = 0;
 
     std::string  generated_text;
+    std::string  debug_generated_text;
     llama_tokens generated_tokens;
 
     // idx of draft tokens in the main batch
@@ -425,7 +426,7 @@ struct server_slot {
 
             if (!only_metrics) {
                 res["prompt"] = ptask->tokens.detokenize(ctx, true);
-                res["generated"] = generated_text;
+                res["generated"] = generated_text.empty() ? debug_generated_text : generated_text;
             }
         }
 
@@ -1442,6 +1443,12 @@ struct server_context_impl {
         res->id_slot = slot.id;
 
         res->index           = slot.task->index;
+
+        // keep copy of last generated text for debugging purposes
+        if (slots_debug) {
+            slot.debug_generated_text = slot.generated_text;
+        }
+
         // in stream mode, content and tokens are already in last partial chunk
         if (slot.task->params.stream) {
             res->content     = "";
Repository: electron/electron
Pull request: build: apply additional compression to dsym on upload

#### Description of Change

This PR attempts to fix an issue where darwin dsym files are exceeding the GitHub 2GB file upload limit. Replaces the deflate compression with xz compression.

#### Checklist
<!-- Remove items that do not apply. For completed items, change [ ] to [x]. -->

- [ ] PR description included and stakeholders cc'd
- [ ] `npm test` passes
- [ ] tests are [changed or added](https://github.com/electron/electron/blob/main/docs/development/testing.md)
- [ ] relevant API documentation, tutorials, and examples are updated and follow the [documentation style guide](https://github.com/electron/electron/blob/main/docs/development/style-guide.md)
- [ ] [PR release notes](https://github.com/electron/clerk/blob/main/README.md) describe the change in a way relevant to app developers, and are [capitalized, punctuated, and past tense](https://github.com/electron/clerk/blob/main/README.md#examples).

#### Release Notes

Notes: none

Changed files:
filename: script/lib/util.py
patch: @@ -84,6 +84,12 @@ def make_zip(zip_file_path, files, dirs):
       zip_file.close()
 
 
+def make_tar_xz(tar_file_path, files, dirs):
+  safe_unlink(tar_file_path)
+  allfiles = files + dirs
+  execute(['tar', '-cJf', tar_file_path] + allfiles)
+
+
 def rm_rf(path):
   try:
     shutil.rmtree(path)

filename: script/zip-symbols.py
patch: @@ -6,7 +6,7 @@
 import sys
 
 from lib.config import PLATFORM
-from lib.util import scoped_cwd, get_electron_version, make_zip, \
+from lib.util import scoped_cwd, get_electron_version, make_zip, make_tar_xz, \
                      get_electron_branding, get_out_dir, execute
 
 ELECTRON_VERSION = get_electron_version()
@@ -27,16 +27,15 @@ def main():
     make_zip(zip_file, licenses, dirs)
 
   if PLATFORM == 'darwin':
-    dsym_name = 'dsym.zip'
+    dsym_name = 'dsym.tar.xz'
     with scoped_cwd(args.build_dir):
       dsyms = glob.glob('*.dSYM')
       snapshot_dsyms = ['v8_context_snapshot_generator.dSYM']
       for dsym in snapshot_dsyms:
         if (dsym in dsyms):
           dsyms.remove(dsym)
-      dsym_zip_file = os.path.join(args.build_dir, dsym_name)
-      print('Making dsym zip: ' + dsym_zip_file)
-      make_zip(dsym_zip_file, licenses, dsyms)
+      dsym_tar_file = os.path.join(args.build_dir, dsym_name)
+      make_tar_xz(dsym_tar_file, licenses, dsyms)
       dsym_snapshot_name = 'dsym-snapshot.zip'
       dsym_snapshot_zip_file = os.path.join(args.build_dir, dsym_snapshot_name)
       print('Making dsym snapshot zip: ' + dsym_snapshot_zip_file)
Repository: huggingface/transformers
Pull request: [Quantization] Fix Static FP8 Quantization

# What does this PR do?

Fix https://github.com/huggingface/transformers/issues/42746, before downcasting to FP8 we should clamp values to the right range to avoid having NaNs

Changed files:
filename: src/transformers/integrations/finegrained_fp8.py
patch: @@ -466,9 +466,11 @@ def forward(self, input: torch.Tensor) -> torch.Tensor:
                     qinput, scale = act_quant(input, self.block_size[1])
                 elif self.activation_scheme == "static":
                     scale = self.activation_scale.to(torch.float32)
-                    qinput = (input / scale).to(torch.float8_e4m3fn)
+                    qinput = (input / scale).clamp(min=_FP8_MIN, max=_FP8_MAX).to(torch.float8_e4m3fn)
+
                 else:
                     raise NotImplementedError("Not supported")
+
                 output = w8a8_block_fp8_matmul_triton(
                     qinput,
                     weight,
@@ -483,7 +485,8 @@ def forward(self, input: torch.Tensor) -> torch.Tensor:
             torch_accelerator_module.synchronize()
             if self.bias is not None:
                 output = output + self.bias
-            output = torch.nan_to_num(output, nan=0.0)
+
+            #            output = torch.nan_to_num(output, nan=0.0)
             return output.to(dtype=input.dtype)
Repository: vuejs/vue
Pull request: Check enterToClass/leaveToClass existence before adding it

Related to [#5800](https://github.com/vuejs/vue/issues/5800)
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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
- [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
- [ ] 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:**


Changed files:
filename: src/platforms/web/runtime/transition-util.js
patch: @@ -69,8 +69,11 @@ export function nextFrame (fn: Function) {
 }
 
 export function addTransitionClass (el: any, cls: string) {
-  (el._transitionClasses || (el._transitionClasses = [])).push(cls)
-  addClass(el, cls)
+  const transitionClasses = el._transitionClasses || (el._transitionClasses = [])
+  if (transitionClasses.indexOf(cls) < 0) {
+    transitionClasses.push(cls)
+    addClass(el, cls)
+  }
 }
 
 export function removeTransitionClass (el: any, cls: string) {