update

README.md

@@ -15,7 +15,13 @@
 </p>
 
 ## News
-* 2/22/2025 2.0.0-dev: 🎉 `GPTQ` quantization internals are now broken into multiple stages (processes) for feature expansion. Synced `Marlin` kernel inference quality fix from upstream. Added `MARLIN_FP16`, lower-quality but faster, backend. `ModelScope` support added. Logging and cli progress bar output has been revamped with sticky bottom progress. Fixed `generation_config.json` save and load. Fix Transformers v4.49.0 compat. Fixed compat of models without `bos`. Fixed `group_size=-1` and `bits=3` packing regression. Added CI tests to track regression in kernel inference quality and sweep all bits/group_sizes. Delegate loggin/progressbar to [LogBar](https://github.com/modelcloud/logbar) pkg.
+* 03/03/2025 [2.0.0](https://github.com/ModelCloud/GPTQModel/releases/tag/v2.0.0): 🎉 `GPTQ` quantization internals are now broken into multiple stages (processes) for feature expansion. 
+Synced `Marlin` kernel inference quality fix from upstream. Added `MARLIN_FP16`, lower-quality but faster backend. 
+`ModelScope` support added. Logging and cli progress bar output has been revamped with sticky bottom progress.
+Fixed `generation_config.json` save and load. Fixed Transformers v4.49.0 compat. Fixed compat of models without `bos`. Fixed `group_size=-1` and `bits=3` packing regression. 
+Fixed Qwen 2.5 MoE regressions. 
+Added CI tests to track regression in kernel inference quality and sweep all bits/group_sizes. Delegate loggin/progressbar to [LogBar](https://github.com/modelcloud/logbar) pkg.
+Fix ROCm version auto detection in `setup` install.
 * 02/12/2025 [1.9.0](https://github.com/ModelCloud/GPTQModel/releases/tag/v1.9.0): ⚡ Offload `tokenizer` fixes to [Toke(n)icer](https://github.com/modelcloud/tokenicer) pkg. Optimized `lm_head` quant time and vram usage.
   Optimized `DeepSeek v3/R1` model quant vram usage. Fixed `Optimum` compat regresion in `v1.8.1`. 3x speed-up for `Torch` kernel when using Pytorch >= 2.5.0 with `model.optimize()`. New `calibration_dataset_concat_size` option to enable calibration data `concat` mode to mimic original GPTQ data packing strategy which may improve quant speed and accuracy for datasets like `wikitext2`. 
 * 02/08/2025 [1.8.1](https://github.com/ModelCloud/GPTQModel/releases/tag/v1.8.1): ⚡ `DeepSeek v3/R1` model support. New flexible weight `packing`: allow quantized weights to be packed to `[int32, int16, int8]` dtypes. 

gptqmodel/utils/importer.py

@@ -45,7 +45,7 @@
 
 AUTO_SELECT_BACKEND_ORDER = OrderedDict({
     BACKEND.MARLIN: MarlinQuantLinear, # optimized for bs > 1
-    BACKEND.EXLLAMA_EORA: ExllamaEoraQuantLinear, #
+    # BACKEND.EXLLAMA_EORA: ExllamaEoraQuantLinear, #
     BACKEND.EXLLAMA_V2: ExllamaV2QuantLinear, # optimized for bs > 1
     BACKEND.EXLLAMA_V1: ExllamaQuantLinear, # optimized for bs == 1
     BACKEND.TRITON: TritonV2QuantLinear, # good all around kernel that JIT compiles
@@ -56,7 +56,7 @@
 })
 
 FORMAT_DICT = {
-    FORMAT.GPTQ: [BACKEND.MARLIN, BACKEND.EXLLAMA_V2, BACKEND.EXLLAMA_V1, BACKEND.EXLLAMA_EORA, BACKEND.TRITON, BACKEND.CUDA, BACKEND.IPEX, BACKEND.TORCH, BACKEND.MARLIN_FP16],
+    FORMAT.GPTQ: [BACKEND.MARLIN, BACKEND.EXLLAMA_V2, BACKEND.EXLLAMA_V1, BACKEND.TRITON, BACKEND.CUDA, BACKEND.IPEX, BACKEND.TORCH, BACKEND.MARLIN_FP16, BACKEND.EXLLAMA_EORA],
     FORMAT.GPTQ_V2: [BACKEND.EXLLAMA_V2, BACKEND.EXLLAMA_V1, BACKEND.TRITON, BACKEND.CUDA, BACKEND.TORCH],
     FORMAT.MARLIN: [BACKEND.MARLIN, BACKEND.MARLIN_FP16],
     FORMAT.BITBLAS: [BACKEND.BITBLAS],

gptqmodel/version.py

@@ -14,4 +14,4 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-__version__ = "2.0.0-dev"
+__version__ = "2.0.0"

tests/test_lora.py

@@ -29,7 +29,7 @@
 
 class Test(ModelTest):
     NATIVE_MODEL_ID = "/monster/data/model/sliuau-llama3.2-1b-4bit-group128"
-    lora_path = "/monster/data/model/sliuau-llama3.2-1b-4bit-group128/llama3.2-1b-4bit-group128-eora-rank128-arc/adapter_model.safetensors" #"sliuau/llama3.2-1b-4bit-group128-eora_test-rank128-arc/blob/main/adapter_model.safetensors" #"sliuau/llama3.2-1b-4bit-group128-eora_test-rank128-arc"
+    lora_path = "/monster/data/model/sliuau-llama3.2-1b-4bit-group128/llama3.2-1b-4bit-group128-eora-rank128-arc" #"sliuau/llama3.2-1b-4bit-group128-eora_test-rank128-arc/blob/main/adapter_model.safetensors" #"sliuau/llama3.2-1b-4bit-group128-eora_test-rank128-arc"
 
     NATIVE_ARC_CHALLENGE_ACC = 0.3567
     NATIVE_ARC_CHALLENGE_ACC_NORM = 0.3805
@@ -45,8 +45,8 @@ def setUpClass(cls):
         # BACKEND.CUDA,
         # BACKEND.TRITON,
         # BACKEND.EXLLAMA_V1,
-        BACKEND.EXLLAMA_V2,
-        # BACKEND.MARLIN,
+        # BACKEND.EXLLAMA_V2,
+        BACKEND.MARLIN,
         # # (BACKEND.IPEX), <-- not tested yet
         # # (BACKEND.BITBLAS, <-- not tested yet
     ])
@@ -65,10 +65,10 @@ def test_load(self, backend: BACKEND):
         self.assertIn("paris", result.lower())
 
     @parameterized.expand([
-        BACKEND.EXLLAMA_V2,
+        BACKEND.MARLIN,
     ])
     def test_download(self, backend: BACKEND):
-        adapter = Lora(path="https://huggingface.co/sliuau/llama3.2-1b-4bit-group128-eora-rank128-arc/blob/main/adapter_model.safetensors", rank=128)
+        adapter = Lora(path="sliuau/llama3.2-1b-4bit-group128-eora-rank128-arc", rank=128)
 
         model = GPTQModel.load(
             self.NATIVE_MODEL_ID,