Merge branch 'main' into uv-first

.github/workflows/tests.yml

@@ -84,7 +84,7 @@ jobs:
           uv pip show --system torch
           uv pip install --system wheel
           printf "torch==${{ matrix.pytorch_version }}\n" > torch-constraints.txt
-          uv pip install --system --no-build-isolation -e ".[dev]" --constraints torch-constraints.txt
+          uv pip install --system --no-cache-dir --no-build-isolation -e ".[dev]" --constraints torch-constraints.txt
           set -o pipefail
           python scripts/unsloth_install.py | bash
           python scripts/cutcrossentropy_install.py | bash
@@ -155,12 +155,10 @@ jobs:
       - name: Install dependencies
         run: |
           uv pip show --system torch
-          uv pip install --system wheel
-          uv pip install --system build
+          uv pip install --system wheel build
           python -m build --sdist
-          uv pip install --system dist/*.tar.gz
           printf "torch==${{ matrix.pytorch_version }}\n" > torch-constraints.txt
-          uv pip install --system ".[dev]" --constraints torch-constraints.txt
+          uv pip install --no-cache-dir --no-build-isolation --system "dist/axolotl*.tar.gz[dev]" --constraints torch-constraints.txt
           python scripts/unsloth_install.py | sh
           python scripts/cutcrossentropy_install.py | sh
 

src/axolotl/cli/delinearize_llama4.py

@@ -85,9 +85,7 @@ def do_cli(model: Union[Path, str], output: Union[Path, str]) -> None:
     unpatch_llama4 = patch_llama4_linearized_modeling()
     from transformers import Llama4ForConditionalGeneration
 
-    model_ = Llama4ForConditionalGeneration.from_pretrained(
-        model, torch_dtype=torch.bfloat16
-    )
+    model_ = Llama4ForConditionalGeneration.from_pretrained(model, dtype=torch.bfloat16)
     processor = AutoProcessor.from_pretrained(model)
     processor.save_pretrained(output)
 

src/axolotl/cli/quantize.py

@@ -69,7 +69,7 @@ def do_quantize(
     config = AutoConfig.from_pretrained(model_path)
     torch_dtype = config.torch_dtype if hasattr(config, "torch_dtype") else None
     model = AutoModelForCausalLM.from_pretrained(
-        model_path, device_map="auto", torch_dtype=torch_dtype
+        model_path, device_map="auto", dtype=torch_dtype
     )
 
     LOG.info(

src/axolotl/utils/model_shard_quant.py

@@ -148,7 +148,7 @@ def load_sharded_model(
         model = AutoModelForCausalLM.from_pretrained(
             model_name,
             use_cache=False,
-            torch_dtype=torch.float32,
+            dtype=torch.float32,
             _attn_implementation=model_config._attn_implementation,
             trust_remote_code=cfg.trust_remote_code,
         )
@@ -158,7 +158,7 @@ def load_sharded_model(
         with init_empty_weights():
             model = AutoModelForCausalLM.from_config(
                 model_config,
-                torch_dtype=torch_dtype,
+                dtype=torch_dtype,
                 trust_remote_code=cfg.trust_remote_code,
             )
     return model

tests/e2e/patched/lora_kernels/test_lora_kernel_patching.py

@@ -160,7 +160,7 @@ def test_geglu_model_integration():
     """Test GeGLU activation with Gemma model."""
     model = AutoModelForCausalLM.from_pretrained(
         "trl-internal-testing/tiny-Gemma2ForCausalLM",
-        torch_dtype=torch.float16,
+        dtype=torch.float16,
         device_map="cuda:0",
     )
     peft_config = get_peft_config(

tests/e2e/test_quantization.py

@@ -39,7 +39,7 @@ def model():
     dummy_model = AutoModelForCausalLM.from_pretrained(
         "Qwen/Qwen2-0.5B",
         device_map="auto",
-        torch_dtype=torch.bfloat16,
+        dtype=torch.bfloat16,
     )
     with torch.device(dummy_model.device):
         dummy_model.model.embed_tokens = torch.nn.Embedding(