fix modelscope load (#195)

neural_speed/convert/__init__.py

@@ -34,7 +34,7 @@ def convert_model(model, outfile, outtype="f32", format="NE", model_hub="hugging
         from modelscope import AutoConfig
     else:
         from transformers import AutoConfig
-        config = AutoConfig.from_pretrained(model, trust_remote_code=True)
+    config = AutoConfig.from_pretrained(model, trust_remote_code=True)
     model_type = model_maps.get(config.model_type, config.model_type)
 
     if use_quantized_model:

tests/requirements.txt

@@ -1,3 +1,4 @@
 gguf
+modelscope==1.13.1
 optimum==1.13.2
 optimum-intel==1.11.0

tests/test_modelscope.py

@@ -0,0 +1,32 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2024 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import sys
+from modelscope import AutoTokenizer
+from transformers import TextStreamer
+from neural_speed import Model
+
+model_name = "/tf_dataset2/models/pytorch/Qwen-7B"
+
+prompt = "Once upon a time, a little girl"
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+inputs = tokenizer(prompt, return_tensors="pt").input_ids
+streamer = TextStreamer(tokenizer)
+
+model = Model()
+# If you want to run GPTQ or AWQ models, just set use_gptq = True or use_awq = True.
+model.init(model_name, weight_dtype="int4", compute_dtype="int8", model_hub="modelscope")
+outputs = model.generate(inputs, streamer=streamer, max_new_tokens=300, do_sample=True)