初始化项目，由ModelHub XC社区提供模型
Model: malaysia-ai/Qwen3-1.7B-Multilingual-TTS Source: Original Platform
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7-languages.mp3
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version https://git-lfs.github.com/spec/v1
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oid sha256:a2dd2529d5c195e42ab26e01c28b4e611cf8a3de86c6c6af6841ed14d24fb42a
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README.md
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---
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library_name: transformers
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datasets:
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- malaysia-ai/Multilingual-TTS
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- malaysia-ai/Emilia-YODAS-Voice-Conversion
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- mesolitica/Malaysian-Emilia-v2
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base_model:
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- Qwen/Qwen3-1.7B-Base
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new_version: Scicom-intl/Multilingual-TTS-1.7B-Base
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---
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# Qwen3-1.7B-Multilingual-TTS
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Continue pretraining [Qwen/Qwen3-1.7B-Base](https://huggingface.co/Qwen/Qwen3-1.7B-Base) on Multilingual Voice Conversion and TTS.
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1. Use [neucodec](https://huggingface.co/neuphonic/neucodec) as speech detokenizer, 50 TPS, output in 24k sample rate.
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2. Multi-speaker multilingual Voice Conversion **up to 25.5B tokens**.
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3. Multi-speaker multilingual TTS up to **5B tokens**.
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4. Flash Attention 3 10k context length multipacking.
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5. Liger Kernel for `swiglu`, `rms_norm` and `fused_linear_cross_entropy`.
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WanDB at https://wandb.ai/huseinzol05/Qwen-Qwen3-1.7B-Base-multilingual-tts-neucodec
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**We released better version at [Scicom-intl/Multilingual-TTS-1.7B-Base](https://huggingface.co/Scicom-intl/Multilingual-TTS-1.7B-Base)**.
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## How to
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```python
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import soundfile as sf
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import torch
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import torchaudio
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from neucodec import NeuCodec
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import re
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model_name = "malaysia-ai/Qwen3-1.7B-Multilingual-TTS"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto").to('cuda')
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codec = NeuCodec.from_pretrained("neuphonic/neucodec")
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_ = codec.eval().to('cuda')
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```
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## TTS
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```python
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text = "Hello! how come I help you? 你好！有什么可以帮你的吗？வணக்கம்! நான் உங்களுக்கு எப்படி உதவுவது? Bonjour! Comment puis-je vous aider ? Xin chào! Tôi có thể giúp gì cho bạn? こんにちは！どうしてお手伝いしましょうか？안녕하세요! 어떻게 도와드릴까요?"
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prompt = f"<|im_start|>jenny_tts_dataset_audio_jenny: {text}<|speech_start|>"
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inputs = tokenizer(prompt,return_tensors="pt", add_special_tokens=True).to('cuda')
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_new_tokens=2048,
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do_sample=True,
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temperature=0.6,
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repetition_penalty=1.15,
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)
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generated_text = tokenizer.decode(outputs[0], skip_special_tokens=False)
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audio_tokens = re.findall(r'<\|s_(\d+)\|>', generated_text.split('<|speech_start|>')[1])
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audio_tokens = [int(token) for token in audio_tokens]
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audio_codes = torch.tensor(audio_tokens)[None, None]
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with torch.no_grad():
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audio_waveform = codec.decode_code(audio_codes.cuda())
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sf.write('7-languages.mp3', audio_waveform[0, 0].cpu(), 24000)
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```
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You can check the audio [7-languages.mp3](7-languages.mp3).
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- You can pick any speaker name from [malaysia-ai/Multilingual-TTS](malaysia-ai/Multilingual-TTS).
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- **Not bad from 0.35 epoch model**.
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### Voice Conversion
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```python
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import librosa
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y, sr = librosa.load('jenny.wav', sr = 16000)
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with torch.no_grad():
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codes = codec.encode_code(torch.tensor(y)[None, None])
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tokens = ''.join([f'<|s_{i}|>' for i in codes[0, 0]])
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prompt = f"<|im_start|>I wonder if I shall ever be happy enough to have real lace on my clothes and bows on my caps.<|speech_start|>{tokens}<|im_end|><|im_start|>Hello, how come I help you, 你好, 有什么可以帮你的吗, வணக்கம், நான் உங்களுக்கு எப்படி உதவுவது, bonjour, comment puis-je vous aider.<|speech_start|>"
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inputs = tokenizer(prompt,return_tensors="pt", add_special_tokens=True).to('cuda')
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_new_tokens=2048,
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do_sample=True,
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temperature=0.6,
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repetition_penalty=1.15,
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)
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generated_text = tokenizer.decode(outputs[0], skip_special_tokens=False)
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audio_tokens = re.findall(r'<\|s_(\d+)\|>', generated_text.split('<|speech_start|>')[-1])
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audio_tokens = [int(token) for token in audio_tokens]
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audio_codes = torch.tensor(audio_tokens)[None, None]
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with torch.no_grad():
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audio_waveform = codec.decode_code(audio_codes.cuda())
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sf.write('jenny-4-languages.mp3', audio_waveform[0, 0].cpu(), 24000)
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```
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You can check the audio [jenny-4-languages.mp3](jenny-4-languages.mp3).
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- **Not too great, we need to trim the silents first before convert to audio tokens, the model tends to generate long silents**.
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## Source code
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Source code at https://github.com/malaysia-ai/cooking/tree/main/qwen-tts
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added_tokens.json
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0].role == 'system' %}
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{{- messages[0].content + '\n\n' }}
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{%- endif %}
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{{- "# 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>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\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" }}
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{%- else %}
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{%- if messages[0].role == 'system' %}
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{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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{%- for message in messages[::-1] %}
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{%- set index = (messages|length - 1) - loop.index0 %}
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{%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
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{%- set ns.multi_step_tool = false %}
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{%- set ns.last_query_index = index %}
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{%- endif %}
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{%- endfor %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{%- set content = message.content %}
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{%- set reasoning_content = '' %}
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{%- if message.reasoning_content is defined and message.reasoning_content is not none %}
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{%- set reasoning_content = message.reasoning_content %}
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{%- else %}
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{%- if '</think>' in message.content %}
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{%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
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{%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
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{%- endif %}
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{%- endif %}
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{%- if loop.index0 > ns.last_query_index %}
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{%- if loop.last or (not loop.last and reasoning_content) %}
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{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- if message.tool_calls %}
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{%- for tool_call in message.tool_calls %}
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{%- if (loop.first and content) or (not loop.first) %}
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{{- '\n' }}
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{%- endif %}
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{%- if tool_call.function %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{%- if tool_call.arguments is string %}
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{{- tool_call.arguments }}
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{%- else %}
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{{- tool_call.arguments | tojson }}
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{%- endif %}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{%- endif %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- if enable_thinking is defined and enable_thinking is false %}
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{{- '<think>\n\n</think>\n\n' }}
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{%- endif %}
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{%- endif %}
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config.json
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{
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"architectures": [
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"Qwen3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"eos_token_id": 151643,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 6144,
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"layer_types": [
|
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"full_attention",
|
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"full_attention",
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention"
|
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],
|
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"max_position_embeddings": 32768,
|
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"max_window_layers": 28,
|
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"model_type": "qwen3",
|
||||
"num_attention_heads": 16,
|
||||
"num_hidden_layers": 28,
|
||||
"num_key_value_heads": 8,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 1000000,
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": true,
|
||||
"torch_dtype": "float32",
|
||||
"transformers_version": "4.55.0",
|
||||
"use_cache": true,
|
||||
"use_sliding_window": false,
|
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"vocab_size": 217208
|
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}
|
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generation_config.json
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{
|
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"bos_token_id": 151643,
|
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"eos_token_id": 151645,
|
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"max_new_tokens": 4096,
|
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"transformers_version": "4.55.0"
|
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}
|
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BIN
jenny-4-languages.mp3
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jenny-4-languages.mp3
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jenny.wav
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size 604844
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{
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31
special_tokens_map.json
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31
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3
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3
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1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user