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Model: Alrightlone/minimind-63M-full-sft-Junhan Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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- zh
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license: cc-by-nc-4.0
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- minimind
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- causal-lm
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- chat
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- text-generation
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- sft
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- qwen3
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---
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# minimind-63M-full-sft-Junhan
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This repository contains a 63.9M-parameter dense MiniMind chat model converted to a Transformers-compatible checkpoint for easy loading with `transformers`.
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## Model Summary
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- Architecture: dense decoder-only causal LM
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- Exported architecture name: `Qwen3ForCausalLM`
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- Original training codebase: MiniMind
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- Parameters: 63.9M
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- Hidden size: 768
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- Layers: 8
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- Attention heads: 8
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- KV heads: 4
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- Vocab size: 6400
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- Max position embeddings: 32768
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- RoPE theta: 1e6
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- MoE: no
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- Checkpoint type: full-parameter SFT
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This model was trained from a MiniMind pretraining checkpoint and then fully fine-tuned on the MiniMind SFT pipeline. The exported folder was produced from the local `full_sft_768.pth` checkpoint using `scripts/convert_model.py`.
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## Training Notes
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- Base training pipeline: MiniMind
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- SFT training script: `trainer/train_full_sft.py`
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- SFT data used locally: `sft_t2t_mini.jsonl`
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- Typical SFT sequence length in this setup: `max_seq_len=768`
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The upstream MiniMind SFT data mixes general instruction-following samples with some tool-calling and reasoning-style samples. As a result, this checkpoint is mainly a lightweight chat model, not a specialized tool-use or reasoning model.
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## Usage
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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repo_id = "YOUR_USERNAME/minimind-63M-full-sft-Junhan"
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tokenizer = AutoTokenizer.from_pretrained(repo_id)
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model = AutoModelForCausalLM.from_pretrained(
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repo_id,
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torch_dtype="auto",
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device_map="auto",
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)
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messages = [
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{"role": "user", "content": "你好，介绍一下你自己。"}
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]
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inputs = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt=True,
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tokenize=True,
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return_tensors="pt",
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).to(model.device)
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outputs = model.generate(
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inputs,
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max_new_tokens=256,
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do_sample=True,
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temperature=0.7,
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top_p=0.9,
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)
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print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))
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```
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## Intended Use
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- Lightweight chat experiments
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- Small-model SFT baselines
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- Educational and debugging purposes
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- Simple local inference and deployment tests
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## Limitations
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- This is a very small model, so factuality, planning, and reasoning ability are limited.
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- Tool-use style may appear in some responses, but robustness is limited.
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- The model is not suitable for high-stakes medical, legal, financial, or safety-critical use.
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- The training mixture includes distilled or synthetic components, so behavior may inherit teacher-model style artifacts.
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## Source
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- Upstream codebase: https://github.com/jingyaogong/minimind
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## License
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This model card uses `cc-by-nc-4.0` conservatively because the upstream MiniMind dataset documentation mentions mixed source licenses, including non-commercial terms in parts of the training pipeline. Review your exact data provenance before using or relicensing this model for commercial scenarios.
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85
chat_template.jinja
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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 message.content is string 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.content is string %}
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{%- set content = message.content %}
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{%- else %}
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{%- set content = '' %}
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{%- endif %}
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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' + content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{%- set reasoning_content = '' %}
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{%- if message.reasoning_content is string %}
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{%- set reasoning_content = message.reasoning_content %}
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{%- else %}
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{%- if '</think>' in content %}
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{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
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{%- set content = content.split('</think>')[-1].lstrip('\n') %}
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{%- endif %}
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{%- endif %}
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{%- if true %}
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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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{%- 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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{{- 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 open_thinking is defined and open_thinking is true %}
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{{- '<think>\n' }}
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{%- else %}
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{{- '<think>\n\n</think>\n\n' }}
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{%- endif %}
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{%- endif %}
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38
config.json
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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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"dtype": "float16",
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"head_dim": 96,
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"hidden_act": "silu",
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"hidden_size": 768,
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"initializer_range": 0.02,
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"intermediate_size": 2432,
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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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],
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"max_position_embeddings": 32768,
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"max_window_layers": 28,
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"model_type": "qwen3",
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"num_attention_heads": 8,
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"num_hidden_layers": 8,
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"num_key_value_heads": 4,
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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,
|
||||
"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": 6400
|
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}
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4
generation_config.json
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generation_config.json
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{
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"_from_model_config": true,
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"transformers_version": "4.57.6"
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}
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model.safetensors
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model.safetensors
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size 127834168
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special_tokens_map.json
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special_tokens_map.json
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{
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"additional_special_tokens": [
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"<|im_start|>",
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"<|object_ref_end|>",
|
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"<|box_start|>",
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|
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|
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|
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|
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|
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"<|audio_pad|>",
|
||||
"<tts_pad>",
|
||||
"<tts_text_bos>",
|
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"<tts_text_eod>",
|
||||
"<tts_text_bos_single>"
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
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|
||||
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|
||||
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|
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|
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|
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}
|
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31191
tokenizer.json
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31191
tokenizer.json
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Load Diff
335
tokenizer_config.json
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335
tokenizer_config.json
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{
|
||||
"add_bos_token": false,
|
||||
"add_eos_token": false,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"14": {
|
||||
"content": "<|audio_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"15": {
|
||||
"content": "<|audio_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"16": {
|
||||
"content": "<|audio_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"17": {
|
||||
"content": "<tts_pad>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"18": {
|
||||
"content": "<tts_text_bos>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"19": {
|
||||
"content": "<tts_text_eod>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"20": {
|
||||
"content": "<tts_text_bos_single>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"21": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"22": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"23": {
|
||||
"content": "<tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"24": {
|
||||
"content": "</tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"25": {
|
||||
"content": "<think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"26": {
|
||||
"content": "</think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"27": {
|
||||
"content": "<|buffer1|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"28": {
|
||||
"content": "<|buffer2|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"29": {
|
||||
"content": "<|buffer3|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"30": {
|
||||
"content": "<|buffer4|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"31": {
|
||||
"content": "<|buffer5|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"32": {
|
||||
"content": "<|buffer6|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"33": {
|
||||
"content": "<|buffer7|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"34": {
|
||||
"content": "<|buffer8|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"35": {
|
||||
"content": "<|buffer9|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>",
|
||||
"<|audio_start|>",
|
||||
"<|audio_end|>",
|
||||
"<|audio_pad|>",
|
||||
"<tts_pad>",
|
||||
"<tts_text_bos>",
|
||||
"<tts_text_eod>",
|
||||
"<tts_text_bos_single>"
|
||||
],
|
||||
"audio_bos_token": "<|audio_start|>",
|
||||
"audio_eos_token": "<|audio_end|>",
|
||||
"audio_token": "<|audio_pad|>",
|
||||
"bos_token": "<|im_start|>",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"extra_special_tokens": {},
|
||||
"image_token": "<|image_pad|>",
|
||||
"legacy": true,
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"sp_model_kwargs": {},
|
||||
"spaces_between_special_tokens": false,
|
||||
"tokenizer_class": "PreTrainedTokenizerFast",
|
||||
"unk_token": "<|endoftext|>",
|
||||
"video_token": "<|video_pad|>",
|
||||
"vision_bos_token": "<|vision_start|>",
|
||||
"vision_eos_token": "<|vision_end|>"
|
||||
}
|
||||
Reference in New Issue
Block a user