Upload Qwen3 30B AWQ model

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

.mv

@@ -0,0 +1 @@
+Revision:master,CreatedAt:1747201023

README.md

@@ -1,3 +1,110 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+tasks:
+- text-generation
+base_model:
+  - Qwen/Qwen3-30B-A3B
+---
+
+## Intro
+
+The AWQ version is quantized using [ms-swift](https://github.com/modelscope/ms-swift). You may refer to our best practice for training/fine-tuning Qwen3-models [here](https://github.com/modelscope/ms-swift/issues/4030).
+
+Note that the AWQ version for Qwen3-MoE models are verified to be working on Transformers/vLLM. We have not have the chance to tested them on other engines.
+
+## Inference
+
+```python
+import torch
+from modelscope import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "swift/Qwen3-30B-A3B-AWQ"
+
+# load the tokenizer and the model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+# prepare the model input
+prompt = "Give me a short introduction to large language model."
+messages = [
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+    enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+# conduct text completion
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=32768
+)
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 
+
+# parsing thinking content
+try:
+    # rindex finding 151668 (</think>)
+    index = len(output_ids) - output_ids[::-1].index(151668)
+except ValueError:
+    index = 0
+
+thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
+content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
+
+print("thinking content:", thinking_content)
+print("content:", content)
+```
+
+## Quantization
+
+The model has undergone AWQ int4 quantization using the [ms-swift](https://github.com/modelscope/ms-swift) framework. Since the model is based on the MoE (Mixture of Experts) architecture, all `linear` layers except for `gate` and `lm_head` have been quantized.
+
+If you have fine-tuned the model and wish to quantize the fine-tuned version, you can refer to the following quantization scripts:
+
+- Dense Model Quantization Script: [View Here](https://github.com/modelscope/ms-swift/blob/main/examples/export/quantize/awq.sh)
+- MoE Model Quantization Script: [View Here](https://github.com/modelscope/ms-swift/blob/main/examples/export/quantize/moe/awq.sh)
+
+With these scripts, you can easily complete the quantization process for the model.
+
+
+## Evaluation
+
+We evaluate the quality of this AWQ quantization with [EvalScope](https://github.com/modelscope/evalscope). For the best practice for evaluating Qwen3 models, one may refer to the following:
+- [最佳实践](https://evalscope.readthedocs.io/zh-cn/latest/best_practice/qwen3.html)
+- [Best Practice](https://evalscope.readthedocs.io/en/latest/best_practice/qwen3.html)
+
+Performance of Qwen3-30B-A3B-AWQ is evaluated on our mixed-benchmark of [Qwen3 Evaluation Collection](https://modelscope.cn/datasets/modelscope/EvalScope-Qwen3-Test), with the results listed below:
+
+> The performance comparison of Qwen3-30B-A3B-AWQ and Qwen3-30B-A3B
+
+| task_type   | dataset_name    | metric                  | average_score(AWQ) | average_score(without AWQ) | count |
+|-------------|-----------------|-------------------------|--------------------|----------------------------|-------|
+| exam        | MMLU-Pro        | AverageAccuracy         | 0.7655             | 0.7828                     | 12032 |
+| exam        | MMLU-Redux      | AverageAccuracy         | 0.8746             | 0.8872                     | 5700  |
+| exam        | C-Eval          | AverageAccuracy         | 0.844              | 0.8722                     | 1346  |
+| instruction | IFEval          | inst_level_strict_acc   | 0.8891             | 0.8925                     | 541   |
+| instruction | IFEval          | inst_level_loose_acc    | 0.9107             | 0.9174                     | 541   |
+| instruction | IFEval          | prompt_level_loose_acc  | 0.8651             | 0.8651                     | 541   |
+| instruction | IFEval          | prompt_level_strict_acc | 0.8373             | 0.8318                     | 541   |
+| math        | MATH-500        | AveragePass@1           | 0.944              | 0.938                      | 500   |
+| knowledge   | GPQA            | AveragePass@1           | 0.596              | 0.601                      | 198   |
+| code        | LiveCodeBench   | Pass@1                  | 0.5275             | 0.5549                     | 182   |
+| exam        | iQuiz           | AverageAccuracy         | 0.6917             | 0.7417                     | 120   |
+| math        | AIME 2024       | AveragePass@1           | 0.7333             | 0.8333                     | 30    |
+| math        | AIME 2025       | AveragePass@1           | 0.7                | 0.7333                     | 30    |
+
+> NOTE: For the pass@k metric, considering time cost of evaluation, we uniformly limit the number of generated responses to 1
+
+### Conclusion
+
+As we can see from the comparison above, evaluatoin results across different tasks and datasets suggest that our quantized-version with AWQ exihibit minimum fluctuation on model performance. 
+
+In fact, for most benchmarks, AWQ version performs mostly on-par with the original version, except for benchmarks (such as AIME2024 and iQuiz) where performance degration is relatively obvious.
+

added_tokens.json

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+}

config.json

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+{
+  "architectures": [
+    "Qwen3MoeForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "decoder_sparse_step": 1,
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 6144,
+  "max_position_embeddings": 40960,
+  "max_window_layers": 48,
+  "mlp_only_layers": [],
+  "model_type": "qwen3_moe",
+  "moe_intermediate_size": 768,
+  "norm_topk_prob": true,
+  "num_attention_heads": 32,
+  "num_experts": 128,
+  "num_experts_per_tok": 8,
+  "num_hidden_layers": 48,
+  "num_key_value_heads": 4,
+  "output_router_logits": false,
+  "pad_token_id": 151643,
+  "quantization_config": {
+    "bits": 4,
+    "group_size": 128,
+    "modules_to_not_convert": [
+      "mlp.gate", "lm_head"
+    ],
+    "quant_method": "awq",
+    "version": "gemm",
+    "zero_point": true
+  },
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "router_aux_loss_coef": 0.001,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.51.3",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

configuration.json

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+{"framework":"Pytorch","task":"text-generation"}

generation_config.json

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+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "4.51.3"
+}

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+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
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+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}