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Model: wallfacers/weft-lineage-extractor-jvm-1.5b Source: Original Platform
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README.md
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README.md
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---
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license: other
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license_name: weft-research
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license_link: https://github.com/wallfacers/data-weave
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pipeline_tag: text-generation
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library_name: transformers
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base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
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datasets:
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- wallfacers/weft-script-lineage-synth
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language:
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- en
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tags:
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- research-artifact
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- negative-result
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- memorization
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- domain-shift
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- cross-language
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- data-lineage
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- etl
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- lora
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model-index:
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- name: weft-lineage-extractor-jvm-1.5b
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results:
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- task:
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type: table-level-lineage-extraction
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name: ETL table-level data-lineage extraction (Scala/Java)
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dataset:
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type: real-jvm-etl
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name: real Scala/Java ETL (human gold, n=141)
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metrics:
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- type: precision
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value: 0.165
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name: Table precision (real JVM, out-of-distribution)
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- type: accuracy
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value: 0.418
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name: Read/write direction accuracy (real JVM)
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---
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# weft-lineage-extractor-jvm-1.5b — cross-language reproduction of a NEGATIVE RESULT
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> ## ⚠️ RESEARCH ARTIFACT — the Scala/Java (JVM) point of a *synthetic-only* training study. Not a production tool.
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>
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> ### ✅ Resolved by real data: use **[weft-lineage-extractor-3b](https://huggingface.co/wallfacers/weft-lineage-extractor-3b)** (real corpus, real precision **0.64**). Full study: **[weft-lineage-extractor-1.5b](https://huggingface.co/wallfacers/weft-lineage-extractor-1.5b)**.
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The **JVM (Scala/Java)** point of a study showing that **synthetic-only training** induces a
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**verbatim memorization leak** in small ETL table-lineage models — and that the failure
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**reproduces, worse, across languages**. Synthetic held-out looks near-perfect (~0.99); on real
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Scala/Java ETL it drops to **precision 0.165**, with **40.8%** of hallucinations being table names
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recited verbatim from the synthetic training pool — the **highest leak** of all scale/language
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points, because real JVM code is the most out-of-distribution.
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Same recipe as the [1.5B main model](https://huggingface.co/wallfacers/weft-lineage-extractor-1.5b)
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(LoRA on Qwen2.5-Coder-1.5B), extended with synthetic Scala/Java forms; `task_type: SCALA | JAVA`.
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## This point's numbers (table-level, Convention A)
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| metric | synthetic held-out | real Scala/Java ETL (n=141, non-empty 28) |
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|---|---|---|
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| precision | ~0.99 | **0.165** |
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| direction accuracy | — | **0.418** |
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| **verbatim memorization leak** | — | **40.8%** |
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The leak metric is **gold-independent** and stable to full-file re-adjudication (verbatim
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40.4% → 40.8%). Given a real `SqlDao.java` it cannot parse, the model emits training-pool names
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like `dws.dws_member_point_df` — memorized generator vocabulary, not a label artifact.
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## Where it fits (scale / cross-language curve)
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| point | real precision | real direction | verbatim leak |
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|---|---|---|---|
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| 0.5B (Py/Sh) | 0.243 | 0.369 | 37.4% |
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| 1.5B (Py/Sh) | 0.270 | 0.496 | 22.4% |
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| 3B (Py/Sh, synthetic) | 0.325 | 0.468 | 10.9% |
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| **1.5B + JVM (this, real JVM)** | **0.165** | **0.418** | **40.8%** |
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| **3B (real corpus)** | **0.64** | — | **~0** |
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"Add another language" does not fix the real-world gap; **real training data does**.
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## Intended use
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- ✅ Reproducing / studying the cross-language reproduction of the memorization-leak failure.
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- ❌ Not for production lineage — use the [real-corpus 3B](https://huggingface.co/wallfacers/weft-lineage-extractor-3b).
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## Usage, prompt format, training details, citation
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Identical to the main model (this variant adds `task_type: SCALA | JAVA` and JVM system-prompt wording):
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**[weft-lineage-extractor-1.5b](https://huggingface.co/wallfacers/weft-lineage-extractor-1.5b)**.
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- **Dataset & reports:** [wallfacers/weft-script-lineage-synth](https://huggingface.co/datasets/wallfacers/weft-script-lineage-synth)
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- **Base model:** [Qwen/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct)
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- **Platform:** [Weft (data-weave)](https://github.com/wallfacers/data-weave)
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54
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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61
config.json
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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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"hidden_act": "silu",
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"hidden_size": 1536,
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"initializer_range": 0.02,
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"intermediate_size": 8960,
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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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"max_position_embeddings": 32768,
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"max_window_layers": 28,
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"model_type": "qwen2",
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"num_attention_heads": 12,
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"use_cache": false,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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generation_config.json
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{
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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": "5.5.0"
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}
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model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 3087467144
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tokenizer.json
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
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size 11421892
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tokenizer_config.json
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"eos_token": "<|im_end|>",
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"<|vision_start|>",
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"<|vision_end|>",
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"<|vision_pad|>",
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"<|image_pad|>",
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"<|video_pad|>"
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"is_local": false,
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"model_max_length": 32768,
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"pad_token": "<|endoftext|>",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null
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}
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