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 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Qwen3-4B-Instruct-2507/tokenizer.json filter=lfs diff=lfs merge=lfs -text

LICENSE

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+                                 Apache License
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Qwen3-4B-Instruct-2507/LICENSE

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+
+                                 Apache License
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+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
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+   Copyright 2024 Alibaba Cloud
+
+   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
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Qwen3-4B-Instruct-2507/README.md

@@ -0,0 +1,211 @@
+---
+library_name: transformers
+license: apache-2.0
+license_link: https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507/blob/main/LICENSE
+pipeline_tag: text-generation
+---
+
+# Qwen3-4B-Instruct-2507
+<a href="https://chat.qwen.ai" target="_blank" style="margin: 2px;">
+    <img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
+</a>
+
+## Highlights
+
+We introduce the updated version of the **Qwen3-4B non-thinking mode**, named **Qwen3-4B-Instruct-2507**, featuring the following key enhancements:
+
+- **Significant improvements** in general capabilities, including **instruction following, logical reasoning, text comprehension, mathematics, science, coding and tool usage**.
+- **Substantial gains** in long-tail knowledge coverage across **multiple languages**.
+- **Markedly better alignment** with user preferences in **subjective and open-ended tasks**, enabling more helpful responses and higher-quality text generation.
+- **Enhanced capabilities** in **256K long-context understanding**.
+
+![image/jpeg](https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3-2507/Qwen3-4B-Instruct.001.jpeg)
+
+## Model Overview
+
+**Qwen3-4B-Instruct-2507** has the following features:
+- Type: Causal Language Models
+- Training Stage: Pretraining & Post-training
+- Number of Parameters: 4.0B
+- Number of Paramaters (Non-Embedding): 3.6B
+- Number of Layers: 36
+- Number of Attention Heads (GQA): 32 for Q and 8 for KV
+- Context Length: **262,144 natively**. 
+
+**NOTE: This model supports only non-thinking mode and does not generate ``<think></think>`` blocks in its output. Meanwhile, specifying `enable_thinking=False` is no longer required.**
+
+For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/).
+
+
+## Performance
+
+|  | GPT-4.1-nano-2025-04-14 | Qwen3-30B-A3B Non-Thinking | Qwen3-4B Non-Thinking | Qwen3-4B-Instruct-2507 |
+|--- | --- | --- | --- | --- |
+| **Knowledge** | | | |
+| MMLU-Pro | 62.8 | 69.1 | 58.0 | **69.6** |
+| MMLU-Redux | 80.2 | 84.1 | 77.3 | **84.2** |
+| GPQA | 50.3 | 54.8 | 41.7 | **62.0** |
+| SuperGPQA | 32.2 | 42.2 | 32.0 | **42.8** |
+| **Reasoning** | | | |
+| AIME25 | 22.7 | 21.6 | 19.1 | **47.4** |
+| HMMT25 | 9.7 | 12.0 | 12.1 | **31.0** |
+| ZebraLogic | 14.8 | 33.2 | 35.2 | **80.2** |
+| LiveBench 20241125 | 41.5 | 59.4 | 48.4 | **63.0** |
+| **Coding** | | | |
+| LiveCodeBench v6 (25.02-25.05) | 31.5 | 29.0 | 26.4 | **35.1** |
+| MultiPL-E | 76.3 | 74.6 | 66.6 | **76.8** |
+| Aider-Polyglot |  9.8 | **24.4** | 13.8 | 12.9 |
+| **Alignment** | | | |
+| IFEval | 74.5 | **83.7** | 81.2 | 83.4 |
+| Arena-Hard v2* | 15.9 | 24.8 | 9.5 | **43.4** |
+| Creative Writing v3 | 72.7 | 68.1 | 53.6 | **83.5** |
+| WritingBench | 66.9 | 72.2 | 68.5 | **83.4** |
+| **Agent** | | | |
+| BFCL-v3 | 53.0 | 58.6 | 57.6 | **61.9** |
+| TAU1-Retail | 23.5 | 38.3 | 24.3 | **48.7** |
+| TAU1-Airline | 14.0 | 18.0 | 16.0 | **32.0** |
+| TAU2-Retail | - | 31.6 | 28.1 | **40.4** |
+| TAU2-Airline | - | 18.0 | 12.0 | **24.0** |
+| TAU2-Telecom | - | **18.4** | 17.5 | 13.2 |
+| **Multilingualism** | | | |
+| MultiIF | 60.7 | **70.8** | 61.3 | 69.0 |
+| MMLU-ProX | 56.2 | **65.1** | 49.6 | 61.6 |
+| INCLUDE | 58.6 | **67.8** | 53.8 | 60.1 |
+| PolyMATH | 15.6 | 23.3 | 16.6 | **31.1** |
+
+*: For reproducibility, we report the win rates evaluated by GPT-4.1.
+
+
+## Quickstart
+
+The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`.
+
+With `transformers<4.51.0`, you will encounter the following error:
+```
+KeyError: 'qwen3'
+```
+
+The following contains a code snippet illustrating how to use the model generate content based on given inputs. 
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "Qwen/Qwen3-4B-Instruct-2507"
+
+# load the tokenizer and the model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    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,
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+# conduct text completion
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=16384
+)
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 
+
+content = tokenizer.decode(output_ids, skip_special_tokens=True)
+
+print("content:", content)
+```
+
+For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint:
+- SGLang:
+    ```shell
+    python -m sglang.launch_server --model-path Qwen/Qwen3-4B-Instruct-2507 --context-length 262144
+    ```
+- vLLM:
+    ```shell
+    vllm serve Qwen/Qwen3-4B-Instruct-2507 --max-model-len 262144
+    ```
+
+**Note: If you encounter out-of-memory (OOM) issues, consider reducing the context length to a shorter value, such as `32,768`.**
+
+For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3.
+
+## Agentic Use
+
+Qwen3 excels in tool calling capabilities. We recommend using [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity.
+
+To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself.
+```python
+from qwen_agent.agents import Assistant
+
+# Define LLM
+llm_cfg = {
+    'model': 'Qwen3-4B-Instruct-2507',
+
+    # Use a custom endpoint compatible with OpenAI API:
+    'model_server': 'http://localhost:8000/v1',  # api_base
+    'api_key': 'EMPTY',
+}
+
+# Define Tools
+tools = [
+    {'mcpServers': {  # You can specify the MCP configuration file
+            'time': {
+                'command': 'uvx',
+                'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai']
+            },
+            "fetch": {
+                "command": "uvx",
+                "args": ["mcp-server-fetch"]
+            }
+        }
+    },
+  'code_interpreter',  # Built-in tools
+]
+
+# Define Agent
+bot = Assistant(llm=llm_cfg, function_list=tools)
+
+# Streaming generation
+messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}]
+for responses in bot.run(messages=messages):
+    pass
+print(responses)
+```
+
+## Best Practices
+
+To achieve optimal performance, we recommend the following settings:
+
+1. **Sampling Parameters**:
+   - We suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`.
+   - For supported frameworks, you can adjust the `presence_penalty` parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.
+
+2. **Adequate Output Length**: We recommend using an output length of 16,384 tokens for most queries, which is adequate for instruct models.
+
+3. **Standardize Output Format**: We recommend using prompts to standardize model outputs when benchmarking.
+   - **Math Problems**: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
+   - **Multiple-Choice Questions**: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the `answer` field with only the choice letter, e.g., `"answer": "C"`."
+
+### Citation
+
+If you find our work helpful, feel free to give us a cite.
+
+```
+@misc{qwen3technicalreport,
+      title={Qwen3 Technical Report}, 
+      author={Qwen Team},
+      year={2025},
+      eprint={2505.09388},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2505.09388}, 
+}
+```

Qwen3-4B-Instruct-2507/config.json

@@ -0,0 +1,30 @@
+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 9728,
+  "max_position_embeddings": 262144,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 5000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

Qwen3-4B-Instruct-2507/configuration.json

@@ -0,0 +1 @@
+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

Qwen3-4B-Instruct-2507/generation_config.json

@@ -0,0 +1,13 @@
+{
+    "bos_token_id": 151643,
+    "do_sample": true,
+    "eos_token_id": [
+        151645,
+        151643
+    ],
+    "pad_token_id": 151643,
+    "temperature": 0.7,
+    "top_k": 20,
+    "top_p": 0.8,
+    "transformers_version": "4.51.0"
+}

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Qwen3-4B-Instruct-2507/tokenizer.json

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Qwen3-4B-Instruct-2507/tokenizer_config.json

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+{
+    "add_prefix_space": false,
+    "added_tokens_decoder": {
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+            "content": "<|endoftext|>",
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+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": true
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+        "151644": {
+            "content": "<|im_start|>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": true
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+        "151645": {
+            "content": "<|im_end|>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": true
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+        "151646": {
+            "content": "<|object_ref_start|>",
+            "lstrip": false,
+            "normalized": false,
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+            "single_word": false,
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+        "151647": {
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+            "normalized": false,
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+            "content": "<|box_start|>",
+            "lstrip": false,
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+            "rstrip": false,
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+            "content": "<|quad_end|>",
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+            "normalized": false,
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+            "single_word": false,
+            "special": true
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+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": true
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+            "content": "<|vision_end|>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
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+            "special": true
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+            "content": "<|image_pad|>",
+            "lstrip": false,
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+            "rstrip": false,
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+            "content": "<|video_pad|>",
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+            "normalized": false,
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+            "single_word": false,
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+            "normalized": false,
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+            "single_word": false,
+            "special": false
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+        "151658": {
+            "content": "</tool_call>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
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+    "additional_special_tokens": [
+        "<|im_start|>",
+        "<|im_end|>",
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+        "<|object_ref_end|>",
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+        "<|vision_pad|>",
+        "<|image_pad|>",
+        "<|video_pad|>"
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+    "bos_token": null,
+    "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0].role == 'system' %}\n        {{- messages[0].content + '\\n\\n' }}\n    {%- endif %}\n    {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0].role == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if message.content is string %}\n        {%- set content = message.content %}\n    {%- else %}\n        {%- set content = '' %}\n    {%- endif %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n        {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role + '\\n' + content }}\n        {%- if message.tool_calls %}\n            {%- for tool_call in message.tool_calls %}\n                {%- if (loop.first and content) or (not loop.first) %}\n                    {{- '\\n' }}\n                {%- endif %}\n                {%- if tool_call.function %}\n                    {%- set tool_call = tool_call.function %}\n                {%- endif %}\n                {{- '<tool_call>\\n{\"name\": \"' }}\n                {{- tool_call.name }}\n                {{- '\", \"arguments\": ' }}\n                {%- if tool_call.arguments is string %}\n                    {{- tool_call.arguments }}\n                {%- else %}\n                    {{- tool_call.arguments | tojson }}\n                {%- endif %}\n                {{- '}\\n</tool_call>' }}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}",
+    "clean_up_tokenization_spaces": false,
+    "eos_token": "<|im_end|>",
+    "errors": "replace",
+    "model_max_length": 1010000,
+    "pad_token": "<|endoftext|>",
+    "split_special_tokens": false,
+    "tokenizer_class": "Qwen2Tokenizer",
+    "unk_token": null,
+    "add_bos_token": false
+}

Qwen3-Omni-30B-A3B-Instruct/config.json

@@ -0,0 +1,485 @@
+{
+  "architectures": [
+    "Qwen3OmniMoeForConditionalGeneration"
+  ],
+  "assistant_token_id": 77091,
+  "code2wav_config": {
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "codebook_dim": 512,
+    "codebook_size": 2048,
+    "decoder_dim": 1536,
+    "hidden_act": "silu",
+    "hidden_size": 1024,
+    "intermediate_size": 3072,
+    "layer_scale_initial_scale": 0.01,
+    "max_position_embeddings": 8000,
+    "model_type": "",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 8,
+    "num_key_value_heads": 16,
+    "num_quantizers": 16,
+    "num_semantic_quantizers": 1,
+    "rms_norm_eps": 1e-05,
+    "rope_theta": 10000,
+    "semantic_codebook_size": 4096,
+    "sliding_window": 72,
+    "upsample_rates": [
+      8,
+      5,
+      4,
+      3
+    ],
+    "upsampling_ratios": [
+      2,
+      2
+    ],
+    "vector_quantization_hidden_dimension": 512
+  },
+  "dtype": "bfloat16",
+  "enable_audio_output": true,
+  "im_end_token_id": 151645,
+  "im_start_token_id": 151644,
+  "model_type": "qwen3_omni_moe",
+  "system_token_id": 8948,
+  "talker_config": {
+    "text_config":{
+      "attention_bias": false,
+      "attention_dropout": 0,
+      "decoder_sparse_step": 1,
+      "head_dim": 128,
+      "hidden_act": "silu",
+      "hidden_size": 1024,
+      "initializer_range": 0.02,
+      "intermediate_size": 2048,
+      "max_position_embeddings": 65536,
+      "mlp_only_layers": [],
+      "moe_intermediate_size": 384,
+      "norm_topk_prob": true,
+      "num_attention_heads": 16,
+      "num_experts": 128,
+      "num_experts_per_tok": 6,
+      "num_hidden_layers": 20,
+      "num_key_value_heads": 2,
+      "rms_norm_eps": 1e-06,
+      "rope_scaling": {
+        "interleaved": true,
+        "mrope_section": [
+          24,
+          20,
+          20
+        ],
+        "rope_type": "default",
+        "type": "default"
+      },
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+      "router_aux_loss_coef": 0.001,
+      "shared_expert_intermediate_size": 768,
+      "sliding_window": null,
+      "use_cache": true,
+      "use_sliding_window": false,
+      "vocab_size": 3072
+    },
+    "accept_hidden_layer": 24,
+    "audio_end_token_id": 151670,
+    "audio_start_token_id": 151669,
+    "audio_token_id": 151675,
+    "code_predictor_config": {
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+      "architectures": null,
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+    "codec_eos_token_id": 2150,
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+    "codec_think_eos_id": 2157,
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+    "model_type": "qwen3_omni_moe_talker",
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+    "output_router_logits": false,
+    "position_id_per_seconds": 13,
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+    "spatial_merge_size": 2,
+    "speaker_id": {
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+      "aiden": 2303
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+    "video_token_id": 151656,
+    "vision_start_token_id": 151652
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+      "model_type": "qwen3_omni_moe_audio_encoder",
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+    "model_type": "qwen3_omni_moe_thinker",
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+    "seconds_per_chunk": 2,
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+        "1": "LABEL_1"
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+      "is_encoder_decoder": false,
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+        "LABEL_1": 1
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+      "max_length": 20,
+      "max_position_embeddings": 65536,
+      "min_length": 0,
+      "mlp_only_layers": [],
+      "model_type": "qwen3_omni_moe_text",
+      "moe_intermediate_size": 768,
+      "no_repeat_ngram_size": 0,
+      "norm_topk_prob": true,
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+          20,
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+        "type": "default"
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+    },
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+      ],
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+        "0": "LABEL_0",
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+}

README.md

@@ -1,3 +1,103 @@
 ---
 license: apache-2.0
+language:
+- zh
+- en
 ---
+# Hojo-ASR-Multi-V1
+
+## Overview
+
+### Introduction
+
+Hojo‑ASR‑Multi‑V1 is a high‑performance conversational speech recognition model built upon the Qwen3 LLM decoder. It adopts the classic Encoder‑Adapter‑LLM architecture with a customized multi‑frame acoustic fusion structure, making full use of fine‑grained acoustic features and the powerful semantic capabilities of the large language model.
+
+Optimized via multi‑stage modular training and reinforcement learning, the model performs well in complex real‑world scenarios such as noisy conditions, informal pronunciation, oral correction, and Chinese‑English code‑switching. Building on its Chinese‑English and dialect recognition capabilities, the model further expands its full‑coverage multilingual recognition capacity, fully supporting major languages including German, French, Spanish, Portuguese, Japanese, Italian, Arabic, Korean and Russian.
+
+
+## Quickstart
+
+### Environment Setup
+
+The easiest way to use Hojo-ASR is to install the `hojo-asr` Python package from PyPI.
+
+```bash
+conda create -n hojo-asr python=3.10
+conda activate hojo-asr
+```
+
+Run the following command to get the minimal installation with transformers-backend support:
+
+```bash
+pip install -U hojo-asr
+```
+
+### Sample Usage
+
+```python
+from hojo_asr import HOJO_ASR
+parser = argparse.ArgumentParser()
+parser.add_argument(
+    "--batch_size", type=int, default=10, help="batch size for inference"
+)
+parser.add_argument("--device", type=str, default="cuda:0")
+args = parser.parse_args()
+model = HOJO_ASR.load_model("HojoAI/Hojo-ASR-Multi-V1", device=args.device)
+# Transcribe
+# List of wav paths; for a single scp file pass a str (see dataset.resolve_infer_audio_input)
+wav_paths = [
+    "/path/to/audio.wav",
+]
+wav_scp = "test.scp"
+with open(wav_paths[0], "rb") as f:
+    wav_bytes = f.read()
+#LIST OF BYTES
+wav_bytes_list = [wav_bytes, wav_bytes]
+res = model.run_infer(wav_scp, batch_size=args.batch_size)
+# res = model.run_infer(wav_paths, batch_size=args.batch_size)
+# res = model.run_infer(wav_bytes_list, batch_size=args.batch_size)
+for val in res:
+    print("key :", val["key"], " text :", val["text"])
+```
+
+
+## Evaluation
+
+ASR Benchmarks on Public Multilingual Datasets (WER ↓)
+
+| Dataset | Hojo-ASR-Multi-V1 |
+|:--------:|:-----------------:|
+| German CoVoST | 3.85 |
+| German FLEURS | 4.08 |
+| French CoVoST | 4.53 |
+| French MLS | 2.95 |
+| French FLEURS | 3.33 |
+| Italian CoVoST | 2.44 |
+| Italian MLS | 5.35 |
+| Italian FLEURS | 2.3 |
+| Spanish CoVoST | 3.27 |
+| Spanish MLS | 3.31 |
+| Spanish FLEURS | 2.66 |
+| Portuguese MLS | 4.07 |
+| Portuguese FLEURS | 3.61 |
+
+## Roadmap
+- [x] release Hojo-ASR-4B model and inference engine
+- [x] support Mandarin, English, Cantonese, and Sichuan dialect
+- [x] support multi-lingual and multi-dialect
+
+  
+## Commercial Support
+We offer commercial support for teams integrating Hojo ASR into their products. This includes integration assistance, custom voice development, and enterprise licensing.
+
+Contact us or email developer@hojoai.com to discuss your requirements.
+  
+## Credits
+Thanks to the following open-source works:
+- [Qwen](https://huggingface.co/Qwen)
+- [WenetSpeech-Yue](https://github.com/ASLP-lab/WenetSpeech-Yue)
+- [WenetSpeech-Chuan](https://github.com/ASLP-lab/WenetSpeech-Chuan)
+
+
+## Licence
+This project is open-sourced under the [Apache 2.0 License](LICENSE.txt), which can be freely used for academic research, personal projects, and commercial secondary development.

config.yaml

@@ -0,0 +1,35 @@
+#
+# Copyright 2026 HojoAI
+#
+# 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.
+#
+
+model:
+  encoder_path: "Qwen3-Omni-30B-A3B-Instruct"
+  llama_path: "Qwen3-4B-Instruct-2507"
+  whisper_path: "whisper-large-v3"
+
+  linear_units: 640
+  num_blocks: 2
+  input_layer: "linear"
+  max_txt_len: 300
+
+generate:
+  max_new_tokens: 200
+  num_beams: 4
+  do_sample: False 
+  min_length: 1
+  temperature: 1.0
+  top_p: 0.9
+  repetition_penalty: 2.0
+  length_penalty: 1

merged_full_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c046a5a26c142f5aded7331d36892293a025f579e342a27861e80509afaca88d
+size 11956909856

whisper-large-v3/preprocessor_config.json

@@ -0,0 +1,14 @@
+{
+  "chunk_length": 30,
+  "feature_extractor_type": "WhisperFeatureExtractor",
+  "feature_size": 128,
+  "hop_length": 160,
+  "n_fft": 400,
+  "n_samples": 480000,
+  "nb_max_frames": 3000,
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "processor_class": "WhisperProcessor",
+  "return_attention_mask": false,
+  "sampling_rate": 16000
+}