Text-to-Speech
Safetensors
GGUF
Arabic
English
tts
speech-synthesis
arabic
arabic-tts
voice-cloning
zero-shot-tts
expressive-tts
neucodec
audar
llama-cpp
on-device
edge
conversational
Instructions to use audarai/Audar-TTS-V1-Flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use audarai/Audar-TTS-V1-Flash with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf audarai/Audar-TTS-V1-Flash:Q4_K_M # Run inference directly in the terminal: llama cli -hf audarai/Audar-TTS-V1-Flash:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf audarai/Audar-TTS-V1-Flash:Q4_K_M # Run inference directly in the terminal: llama cli -hf audarai/Audar-TTS-V1-Flash:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf audarai/Audar-TTS-V1-Flash:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf audarai/Audar-TTS-V1-Flash:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf audarai/Audar-TTS-V1-Flash:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf audarai/Audar-TTS-V1-Flash:Q4_K_M
Use Docker
docker model run hf.co/audarai/Audar-TTS-V1-Flash:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use audarai/Audar-TTS-V1-Flash with Ollama:
ollama run hf.co/audarai/Audar-TTS-V1-Flash:Q4_K_M
- Unsloth Studio
How to use audarai/Audar-TTS-V1-Flash with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for audarai/Audar-TTS-V1-Flash to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for audarai/Audar-TTS-V1-Flash to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for audarai/Audar-TTS-V1-Flash to start chatting
- Pi
How to use audarai/Audar-TTS-V1-Flash with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf audarai/Audar-TTS-V1-Flash:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "audarai/Audar-TTS-V1-Flash:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use audarai/Audar-TTS-V1-Flash with Docker Model Runner:
docker model run hf.co/audarai/Audar-TTS-V1-Flash:Q4_K_M
- Lemonade
How to use audarai/Audar-TTS-V1-Flash with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull audarai/Audar-TTS-V1-Flash:Q4_K_M
Run and chat with the model
lemonade run user.Audar-TTS-V1-Flash-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use audarai/Audar-TTS-V1-Flash with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf audarai/Audar-TTS-V1-Flash:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default audarai/Audar-TTS-V1-Flash:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use audarai/Audar-TTS-V1-Flash with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf audarai/Audar-TTS-V1-Flash:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "audarai/Audar-TTS-V1-Flash:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Add full-precision safetensors (Transformers) under transformers/ subfolder
Browse files- transformers/added_tokens.json +0 -0
- transformers/chat_template.jinja +54 -0
- transformers/config.json +54 -0
- transformers/generation_config.json +13 -0
- transformers/merges.txt +0 -0
- transformers/model.safetensors +3 -0
- transformers/special_tokens_map.json +34 -0
- transformers/tokenizer.json +3 -0
- transformers/tokenizer_config.json +3 -0
- transformers/vocab.json +0 -0
transformers/added_tokens.json
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transformers/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'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\n\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>" }}
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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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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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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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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) 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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{%- endif %}
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transformers/config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"dtype": "bfloat16",
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 896,
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"initializer_range": 0.02,
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"intermediate_size": 4864,
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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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],
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"max_position_embeddings": 32768,
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"max_window_layers": 21,
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"model_type": "qwen2",
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"num_attention_heads": 14,
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"num_hidden_layers": 24,
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"num_key_value_heads": 2,
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"pad_token_id": 151645,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 1000000.0,
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"sliding_window": null,
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"tie_word_embeddings": true,
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"transformers_version": "4.57.6",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 217668
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}
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transformers/generation_config.json
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{
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"do_sample": true,
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"eos_token_id": [
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151645,
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151643
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],
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| 7 |
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"pad_token_id": 151645,
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| 8 |
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"repetition_penalty": 1.1,
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"temperature": 0.7,
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"top_k": 20,
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"top_p": 0.8,
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"transformers_version": "4.57.6"
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}
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transformers/merges.txt
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transformers/model.safetensors
ADDED
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version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:7a70f5be8361721c0004c4d3ae83ef2b9b89b169b5f1a8fd6adf1561c8ac784e
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| 3 |
+
size 1105889664
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transformers/special_tokens_map.json
ADDED
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{
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| 2 |
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"additional_special_tokens": [
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| 3 |
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"<|REF_TEXT_START|>",
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| 4 |
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"<|REF_TEXT_END|>",
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| 5 |
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"<|REF_SPEECH_START|>",
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| 6 |
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"<|REF_SPEECH_END|>",
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| 7 |
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"<|TARGET_TEXT_START|>",
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| 8 |
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"<|TARGET_TEXT_END|>",
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| 9 |
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"<|TARGET_CODES_START|>",
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| 10 |
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"<|TARGET_CODES_END|>",
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| 11 |
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"[laughs]",
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| 12 |
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"[curious]",
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| 13 |
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"[excited]",
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| 14 |
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"[sighs]",
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| 15 |
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"[exhales]",
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"[mischievously]",
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| 17 |
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"[whispers]",
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| 18 |
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"[sarcastic]"
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| 19 |
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],
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| 20 |
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"eos_token": {
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| 21 |
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"content": "<|im_end|>",
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| 22 |
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"lstrip": false,
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| 23 |
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"normalized": false,
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"rstrip": false,
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"single_word": false
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| 26 |
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},
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| 27 |
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"pad_token": {
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| 28 |
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"content": "<|im_end|>",
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| 29 |
+
"lstrip": false,
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| 30 |
+
"normalized": false,
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| 31 |
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"rstrip": false,
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| 32 |
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"single_word": false
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| 33 |
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}
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| 34 |
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}
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transformers/tokenizer.json
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version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:bd7a90e83b7d8f600797a5b04e99c113a48c184e6ca90e0c924d02fd6bcabc8c
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size 24143303
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transformers/tokenizer_config.json
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version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:6c989a68bee235b552774e3b4e90af84bb70e830a887402eeea8e76527c6ab66
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| 3 |
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size 12066161
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transformers/vocab.json
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