helloadhavan/github_issues huggingface dataset:
Repository: ollama/ollama
Pull request: fix linux archive

add target to artifact name since there are multiple targets for each os/arch combination now

Changed files:
filename: .github/workflows/release.yaml
patch: @@ -326,7 +326,7 @@ jobs:
           PLATFORM: ${{ matrix.os }}/${{ matrix.arch }}
       - uses: actions/upload-artifact@v4
         with:
-          name: dist-${{ matrix.os }}-${{ matrix.arch }}
+          name: dist-${{ matrix.os }}-${{ matrix.arch }}-${{ matrix.target }}
           path: |
             dist/*.tgz
 
@@ -391,9 +391,6 @@ jobs:
       GH_TOKEN: ${{ github.token }}
     steps:
       - uses: actions/checkout@v4
-      - name: Set Version
-        shell: bash
-        run: |
       - uses: actions/download-artifact@v4
         with:
           name: dist-darwin
@@ -406,11 +403,9 @@ jobs:
         with:
           pattern: dist-linux-*
           path: dist
-      - run: |
-          ls -lh dist/
-          (cd dist; find . -type f | xargs sha256sum > ../sha256sum.txt)
-          mv sha256sum.txt dist/
-          cat dist/sha256sum.txt
+          merge-multiple: true
+      - run: find . -type f -not -name 'sha256sum.txt' | xargs sha256sum | tee sha256sum.txt
+        working-directory: dist
       - name: Create or update Release
         run: |
           RELEASE_VERSION=$(echo ${GITHUB_REF_NAME} | cut -f1 -d-)"

filename: scripts/build_darwin.sh
patch: @@ -34,7 +34,8 @@ _build_darwin() {
                 -DCMAKE_OSX_ARCHITECTURES=x86_64 \
                 -DCMAKE_OSX_DEPLOYMENT_TARGET=11.3
             cmake --build build/darwin-$ARCH --target ggml-cpu -j
-            install build/darwin-$ARCH/lib/ollama/*.{dylib,so} $INSTALL_PREFIX
+            install -d $INSTALL_PREFIX/lib/ollama
+            install build/darwin-$ARCH/lib/ollama/*.{dylib,so} $INSTALL_PREFIX/lib/ollama
         fi
     done
 }
@@ -43,6 +44,7 @@ _sign_darwin() {
     status "Creating universal binary..."
     mkdir -p dist/darwin
     lipo -create -output dist/darwin/ollama dist/darwin-*/ollama
+    chmod +x dist/darwin/ollama
 
     if [ -n "$APPLE_IDENTITY" ]; then
         for F in dist/darwin/ollama dist/darwin-amd64/lib/ollama/*; do
Repository: huggingface/transformers
Pull request: Fix formatting of trackio model tag

Quick followup fixing: https://github.com/huggingface/transformers/pull/42850/files#r2637239463

cc @SunMarc @qgallouedec @julien-c 

Changed files:
filename: src/transformers/integrations/integration_utils.py
patch: @@ -1086,12 +1086,12 @@ def on_push_begin(self, args, state, control, model, **kwargs):
         if badge_markdown not in modelcard.AUTOGENERATED_TRAINER_COMMENT:
             modelcard.AUTOGENERATED_TRAINER_COMMENT += f"\n{badge_markdown}"
 
+        trackio_tags = ["trackio", f"trackio:{space_url}"]
         if getattr(model, "model_tags", None) is not None:
             if "trackio" not in model.model_tags:
-                model.model_tags.append("trackio")
-                model.model_tags.append(f"trackio::{space_url}")
+                model.model_tags.extend(trackio_tags)
         else:
-            model.model_tags = ["trackio", f"trackio:{space_url}"]
+            model.model_tags = trackio_tags
 
 
 class CometCallback(TrainerCallback):
Repository: nodejs/node
Pull request: src: remove unused private field in `SQLTagStore`


Changed files:
filename: src/node_sqlite.cc
patch: @@ -2664,8 +2664,7 @@ SQLTagStore::SQLTagStore(Environment* env,
                          int capacity)
     : BaseObject(env, object),
       database_(std::move(database)),
-      sql_tags_(capacity),
-      capacity_(capacity) {
+      sql_tags_(capacity) {
   MakeWeak();
 }
 

filename: src/node_sqlite.h
patch: @@ -330,7 +330,6 @@ class SQLTagStore : public BaseObject {
       const v8::FunctionCallbackInfo<v8::Value>& args);
   BaseObjectWeakPtr<DatabaseSync> database_;
   LRUCache<std::string, BaseObjectPtr<StatementSync>> sql_tags_;
-  int capacity_;
   friend class StatementExecutionHelper;
 };
Repository: vuejs/vue
Pull request: Fix the env detection of server side rendering

**What kind of change does this PR introduce?**

- [x] Bugfix

**Does this PR introduce a breaking change?**

- [x] No

**Other information:**

In Weex, `global` does exist, but `global.process` does not. I think it's more reliable to exclude Weex platform when detecting the ssr env.

  [edits: minor grammar edits]
  

Changed files:
filename: src/core/util/env.js
patch: @@ -38,7 +38,7 @@ let _isServer
 export const isServerRendering = () => {
   if (_isServer === undefined) {
     /* istanbul ignore if */
-    if (!inBrowser && typeof global !== 'undefined') {
+    if (!inBrowser && !inWeex && typeof global !== 'undefined') {
       // detect presence of vue-server-renderer and avoid
       // Webpack shimming the process
       _isServer = global['process'].env.VUE_ENV === 'server'
Repository: huggingface/transformers
Pull request: [`Tests`] Fix inputs placement

As per title, we init our model on CPU but the inputs are not guaranteed to be on CPU as well. Forcing CPU here

Related failure: https://github.com/huggingface/transformers/actions/runs/20375489555/job/58553450832

Changed files:
filename: tests/test_modeling_common.py
patch: @@ -1202,21 +1202,30 @@ def test_all_tensors_are_parameter_or_buffer(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
 
         for model_class in self.all_model_classes:
-            # apparently this model cannot correctly create its inputs and has to use another function....
+            # Apparently this model cannot correctly create its inputs and has to use another function....
             if "modeling_perceiver.py" in inspect.getfile(model_class):
                 _, inputs_dict = self.model_tester.prepare_config_and_inputs_for_model_class(model_class)
 
             # Initialize the model fully on meta device, then move everything to cpu and run `init_weights`
             with torch.device("meta"):
                 model = model_class(copy.deepcopy(config)).eval()
-            # move everything randomly to cpu
+            # Move everything randomly to cpu
             model.to_empty(device="cpu")
             # Now, run all the inits
             model.init_weights()
 
+            # Prepare inputs to correct device
+            inputs = self._prepare_for_class(inputs_dict, model_class)
+            final_inputs = {}
+            for k, v in inputs.items():
+                if isinstance(v, torch.Tensor):
+                    final_inputs[k] = v.to(device="cpu")
+                else:
+                    final_inputs[k] = v
+
             # Try running a forward, to see if a tensor stayed on meta somewhere
             try:
-                _ = model(**self._prepare_for_class(inputs_dict, model_class))
+                _ = model(**final_inputs)
             except (RuntimeError, NotImplementedError) as e:
                 # Re-raise a more friendly exception (unfortunately, we cannot know which tensor it was...)
                 if "Cannot copy out of meta tensor; no data!" in str(
Repository: ollama/ollama
Pull request: readme: add Chipper to community integrations

Chipper provides a web interface, CLI, and a modular, hackable, and lightweight architecture for RAG pipelines, document splitting, web scraping, and query workflows for Ollama.

Changed files:
filename: README.md
patch: @@ -371,6 +371,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Perplexica](https://github.com/ItzCrazyKns/Perplexica) (An AI-powered search engine & an open-source alternative to Perplexity AI)
 - [AI Toolkit for Visual Studio Code](https://aka.ms/ai-tooklit/ollama-docs) (Microsoft-official VSCode extension to chat, test, evaluate models with Ollama support, and use them in your AI applications.)
 - [MinimalNextOllamaChat](https://github.com/anilkay/MinimalNextOllamaChat) (Minimal Web UI for Chat and Model Control)
+- [Chipper](https://github.com/TilmanGriesel/chipper) AI interface for tinkerers (Ollama, Haystack RAG, Python)
 
 ### Cloud
Repository: electron/electron
Pull request: build: disallow non-maintainer changes to GitHub Actions workflows

#### Description of Change

Refs https://github.com/electron/electron/pull/48024

We should also disallow non-maintainer changes to GitHub Actions files by default.

#### Checklist
<!-- Remove items that do not apply. For completed items, change [ ] to [x]. -->

- [x] PR description included and stakeholders cc'd
- [x] `npm test` passes
- [x] [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: .github/workflows/non-maintainer-dependency-change.yml
patch: @@ -1,16 +1,18 @@
-name: Check for Non-Maintainer Dependency Change
+name: Check for Disallowed Non-Maintainer Change
 
 on:
   pull_request_target:
     paths:
       - 'yarn.lock'
       - 'spec/yarn.lock'
+      - '.github/workflows/**'
+      - '.github/actions/**'
 
 permissions: {}
 
 jobs:
   check-for-non-maintainer-dependency-change:
-    name: Check for non-maintainer dependency change
+    name: Check for disallowed non-maintainer change
     if: ${{ github.event.pull_request.user.type != 'Bot' && !github.event.pull_request.draft }}
     permissions:
       contents: read
@@ -32,7 +34,7 @@ jobs:
           PR_URL: ${{ github.event.pull_request.html_url }}
         run: |
           set -eo pipefail
-          REVIEW_COUNT=$(gh pr view $PR_URL --json reviews | jq '[ .reviews[] | select(.author.login == "github-actions") | select(.body | startswith("<!-- no-dependency-change -->")) ] | length')
+          REVIEW_COUNT=$(gh pr view $PR_URL --json reviews | jq '[ .reviews[] | select(.author.login == "github-actions") | select(.body | startswith("<!-- disallowed-non-maintainer-change -->")) ] | length')
           if [[ $REVIEW_COUNT -eq 0 ]]; then
             echo "SHOULD_REVIEW=1" >> "$GITHUB_OUTPUT"
           fi
@@ -42,4 +44,4 @@ jobs:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
           PR_URL: ${{ github.event.pull_request.html_url }}
         run: |
-          printf "<!-- no-dependency-change -->\n\nHello @${{ github.event.pull_request.user.login }}! It looks like this pull request touches one of our dependency files, and per [our contribution policy](https://github.com/electron/electron/blob/main/CONTRIBUTING.md#dependencies-upgrades-policy) we do not accept these types of changes in PRs." | gh pr review $PR_URL -r --body-file=-
+          printf "<!-- disallowed-non-maintainer-change -->\n\nHello @${{ github.event.pull_request.user.login }}! It looks like this pull request touches one of our dependency or CI files, and per [our contribution policy](https://github.com/electron/electron/blob/main/CONTRIBUTING.md#dependencies-upgrades-policy) we do not accept these types of changes in PRs." | gh pr review $PR_URL -r --body-file=-
Repository: vercel/next.js
Pull request: perf: use length = 0 to clear the logging array

Use array `.length = 0` to clean up the logging queue in dev, which doesn't create new array everytime and avoid unnecessary GC churn

Changed files:
filename: packages/next/src/server/dev/browser-logs/file-logger.ts
patch: @@ -89,7 +89,7 @@ export class FileLogger {
 
     // Only flush to disk if mcpServer is enabled
     if (!this.mcpServerEnabled) {
-      this.logQueue = [] // Clear the queue without writing
+      this.logQueue.length = 0 // Clear the queue without GC overhead
       this.flushTimer = null
       return
     }
@@ -104,7 +104,7 @@ export class FileLogger {
       const logsToWrite = this.logQueue.join('')
       // Writing logs to files synchronously to ensure they're written before returning
       fs.appendFileSync(this.logFilePath, logsToWrite)
-      this.logQueue = []
+      this.logQueue.length = 0 // Clear the queue without GC overhead
     } catch (error) {
       console.error('Failed to flush logs to file:', error)
     } finally {
Repository: ollama/ollama
Pull request: llm: do not error if LibOllamaPath does not exist


Changed files:
filename: llm/server.go
patch: @@ -89,7 +89,6 @@ func LoadModel(model string, maxArraySize int) (*GGML, error) {
 // NewLlamaServer will run a server for the given GPUs
 // The gpu list must be a single family.
 func NewLlamaServer(gpus discover.GpuInfoList, model string, ggml *GGML, adapters, projectors []string, opts api.Options, numParallel int) (LlamaServer, error) {
-	var err error
 	var systemTotalMemory uint64
 	var systemFreeMemory uint64
 	var systemSwapFreeMemory uint64
@@ -233,19 +232,9 @@ func NewLlamaServer(gpus discover.GpuInfoList, model string, ggml *GGML, adapter
 		params = append(params, "--multiuser-cache")
 	}
 
-	// get available libraries
-	if err != nil {
-		return nil, fmt.Errorf("could not get libollama dir: %w", err)
-	}
-
-	entries, err := os.ReadDir(discover.LibOllamaPath)
-	if err != nil {
-		return nil, fmt.Errorf("could not read libollama dir: %w", err)
-	}
-
 	libs := make(map[string]string)
-	for _, entry := range entries {
-		if entry.IsDir() {
+	if entries, err := os.ReadDir(discover.LibOllamaPath); err == nil {
+		for _, entry := range entries {
 			libs[entry.Name()] = filepath.Join(discover.LibOllamaPath, entry.Name())
 		}
 	}
@@ -285,7 +274,7 @@ func NewLlamaServer(gpus discover.GpuInfoList, model string, ggml *GGML, adapter
 			}
 		}
 		if port == 0 {
-			slog.Debug("ResolveTCPAddr failed ", "error", err)
+			slog.Debug("ResolveTCPAddr failed, using random port")
 			port = rand.Intn(65535-49152) + 49152 // get a random port in the ephemeral range
 		}
 		finalParams := []string{"runner"}
Repository: facebook/react
Pull request: [compiler] PruneNonEscapingScopes understands terminal operands

Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom):
* __->__ #33062

Handles a few edge cases relating to for..of terminals, which have a compound `init` value block:
* For..of with a return statement inside would not memoize the rvalue of the init expression (the collection). This is because there was no direct dependency relationship between the value being returned and the outer scope. The fix is to track the set of active scopes and make return values depend on all the active scopes at the point of the return.
* For..of that reassigns a variable which later escapes were also not memoizing the collection. This is similar: the escape analysis aliasing didn't consider scopes that reassign, so there was no dependency between the returned variable and the scope that did the reassigning. The fix is to establish that dependency relationship.

In both cases, adding the additional scopes as a dependency of the right variable ensures that the scope's dependencies are treated as escaping (needing memoization), ensuring the collection is memoized.

Changed files:
filename: compiler/packages/babel-plugin-react-compiler/src/__tests__/fixtures/compiler/todo.memoize-loops-that-produce-memoizeable-values.expect.md
patch: @@ -6,7 +6,7 @@ function useHook(nodeID, condition) {
   const graph = useContext(GraphContext);
   const node = nodeID != null ? graph[nodeID] : null;
 
-  // (2) Instead we can create a scope around the loop since the loop produces an escapinng value
+  // (2) Instead we can create a scope around the loop since the loop produces an escaping value
   let value;
   for (const key of Object.keys(node?.fields ?? {})) {
     if (condition) {

filename: compiler/packages/babel-plugin-react-compiler/src/__tests__/fixtures/compiler/todo.memoize-loops-that-produce-memoizeable-values.js
patch: @@ -2,7 +2,7 @@ function useHook(nodeID, condition) {
   const graph = useContext(GraphContext);
   const node = nodeID != null ? graph[nodeID] : null;
 
-  // (2) Instead we can create a scope around the loop since the loop produces an escapinng value
+  // (2) Instead we can create a scope around the loop since the loop produces an escaping value
   let value;
   for (const key of Object.keys(node?.fields ?? {})) {
     if (condition) {
Repository: vuejs/vue
Pull request: fix(core): fix defineReactive unnecessary getters call fix #7280

The defineReactive gets the property of the object, only if it has no getter

fix #7280

<!--
Please make sure to read the Pull Request Guidelines:
https://github.com/vuejs/vue/blob/dev/.github/CONTRIBUTING.md#pull-request-guidelines
-->

<!-- PULL REQUEST TEMPLATE -->
<!-- (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:
- [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:**


Changed files:
filename: src/core/observer/index.js
patch: @@ -61,7 +61,7 @@ export class Observer {
   walk (obj: Object) {
     const keys = Object.keys(obj)
     for (let i = 0; i < keys.length; i++) {
-      defineReactive(obj, keys[i], obj[keys[i]])
+      defineReactive(obj, keys[i])
     }
   }
 
@@ -145,6 +145,9 @@ export function defineReactive (
 
   // cater for pre-defined getter/setters
   const getter = property && property.get
+  if (!getter && arguments.length === 2) {
+    val = obj[key]
+  }
   const setter = property && property.set
 
   let childOb = !shallow && observe(val)
Repository: huggingface/transformers
Pull request: [`Generation`] Fix default overwrite for non-`None` defaults

Followup to #42702 where defaults are now either `None` or a boolean value. 

The logic was dependent on the values being `None` which ignored overriding other values, e.g. `do_sample`. The fails due to this can be seen in a few integration tests, e.g. gpt2.

Changed files:
filename: src/transformers/generation/utils.py
patch: @@ -1795,6 +1795,20 @@ def _prepare_generation_config(
         generation_config.update(**self.generation_config.to_dict(), defaults_only=True)
         generation_config.update(**global_defaults, defaults_only=True)
 
+        # Due to some values being boolean and not `None`, we need additional logic to overwrite
+        # them explicitly (`defaults_only=False`) on the condition that it's only a previous
+        # default value
+        default_generation_config = GenerationConfig()
+        generation_config.update(
+            **{
+                k: v
+                for k, v in self.generation_config.to_dict().items()
+                if isinstance(v, bool)
+                and hasattr(default_generation_config, k)
+                and getattr(generation_config, k, None) == getattr(default_generation_config, k)
+            }
+        )
+
         # Finally, if there are any kwargs, update config with it -> highest priority at the end
         model_kwargs = generation_config.update(**kwargs)
 

filename: tests/generation/test_utils.py
patch: @@ -4802,6 +4802,25 @@ def test_hub_gen_strategies(self, custom_generate, extra_kwargs):
         output = model.generate(**generation_kwargs, **model_inputs)
         self.assertEqual(output.sequences.shape, (1, 9))
 
+    def test_model_generation_config_can_override_defaults(self):
+        """Sanity check that the model samples, not ignoring the model's generation config"""
+        torch.manual_seed(42)  # make it deterministic
+
+        model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-LlamaForCausalLM").eval()
+        model = model.to(torch_device)
+
+        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-LlamaForCausalLM")
+        model_inputs = tokenizer(["Write a poem about the market crashing in summer"], return_tensors="pt")
+        model_inputs = model_inputs.to(model.device)
+
+        # Overwrite default value or sampling
+        model.generation_config.do_sample = True
+        output = model.generate(**model_inputs, max_new_tokens=32)
+
+        EXPECTED_TEXT = 'Write a poem about the market crashing in summersong contradictionPr aucitated realiz Comicsflutterąc inventминцій Glad:` Raymond moreover KulturMillteger мартаTEXT CFщая Русбе Świ Sendlink heuresListener Luigiaceae'  # fmt: skip
+        output = tokenizer.decode(output[0], skip_special_tokens=True)
+        self.assertTrue(output == EXPECTED_TEXT)
+
 
 @require_torch
 class TokenHealingTestCase(unittest.TestCase):
Repository: ollama/ollama
Pull request: readme: add MinimalNextOllamaChat to community-integrations


Changed files:
filename: README.md
patch: @@ -370,6 +370,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [aidful-ollama-model-delete](https://github.com/AidfulAI/aidful-ollama-model-delete) (User interface for simplified model cleanup)
 - [Perplexica](https://github.com/ItzCrazyKns/Perplexica) (An AI-powered search engine & an open-source alternative to Perplexity AI)
 - [AI Toolkit for Visual Studio Code](https://aka.ms/ai-tooklit/ollama-docs) (Microsoft-official VSCode extension to chat, test, evaluate models with Ollama support, and use them in your AI applications.)
+- [MinimalNextOllamaChat](https://github.com/anilkay/MinimalNextOllamaChat) (Minimal Web UI for Chat and Model Control)
 
 ### Cloud
Repository: ggml-org/llama.cpp
Pull request: convert : register Qwen 3.5 ForCausalLM for text only

Support text only versions of Qwen 3.5.

Fixes #20116
Fixes #20102

Changed files:
filename: convert_hf_to_gguf.py
patch: @@ -4842,12 +4842,12 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
         yield from super().modify_tensors(data_torch, name, bid)
 
 
-@ModelBase.register("Qwen3_5ForConditionalGeneration")
+@ModelBase.register("Qwen3_5ForConditionalGeneration", "Qwen3_5ForCausalLM")
 class Qwen3_5TextModel(_LinearAttentionVReorderBase):
     model_arch = gguf.MODEL_ARCH.QWEN35
 
 
-@ModelBase.register("Qwen3_5MoeForConditionalGeneration")
+@ModelBase.register("Qwen3_5MoeForConditionalGeneration", "Qwen3_5MoeForCausalLM")
 class Qwen3_5MoeTextModel(_LinearAttentionVReorderBase):
     model_arch = gguf.MODEL_ARCH.QWEN35MOE