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Model: wallfacers/weft-lineage-extractor-0.5b
Source: Original Platform

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+license: other
+license_name: weft-research
+license_link: https://github.com/wallfacers/data-weave
+pipeline_tag: text-generation
+library_name: transformers
+base_model: Qwen/Qwen2.5-Coder-0.5B-Instruct
+datasets:
+- wallfacers/weft-script-lineage-synth
+language:
+- en
+tags:
+- research-artifact
+- negative-result
+- memorization
+- domain-shift
+- data-lineage
+- etl
+- lora
+model-index:
+- name: weft-lineage-extractor-0.5b
+  results:
+  - task:
+      type: table-level-lineage-extraction
+      name: ETL table-level data-lineage extraction
+    dataset:
+      type: synthetic-etl
+      name: synthetic held-out (structural-form isolated)
+    metrics:
+    - type: precision
+      value: 0.994
+      name: Table precision (synthetic held-out)
+  - task:
+      type: table-level-lineage-extraction
+      name: ETL table-level data-lineage extraction
+    dataset:
+      type: real-github-etl
+      name: real GitHub ETL (human gold)
+    metrics:
+    - type: precision
+      value: 0.243
+      name: Table precision (real, out-of-distribution)
+---
+
+# weft-lineage-extractor-0.5b — smallest scale point of a NEGATIVE RESULT
+
+> ## ⚠️ RESEARCH ARTIFACT — the 0.5B point of a *synthetic-only* training study. Not a production tool.
+>
+> ### ✅ 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)**.
+
+The **0.5B** point of a study showing that **synthetic-only training** induces a **verbatim
+memorization leak** in small models for ETL table-lineage extraction. It is the smallest scale
+point and shows the **worst** leak: near-perfect synthetic precision (0.994) collapses to
+**0.243** on real GitHub scripts, with **37.4%** of hallucinations being table names recited
+verbatim from the synthetic training pool.
+
+Same recipe as the [1.5B main model](https://huggingface.co/wallfacers/weft-lineage-extractor-1.5b)
+(LoRA on Qwen2.5-Coder-Instruct, Python/Shell synthetic ETL scripts, zero real scripts); only the
+base size differs.
+
+## This point's numbers (table-level, Convention A)
+
+| metric | synthetic held-out | real GitHub ETL |
+|---|---|---|
+| precision | 0.994 | **0.243** |
+| direction accuracy | — | **0.369** |
+| **verbatim memorization leak** | — | **37.4%** |
+
+## Where it fits (scale curve)
+
+| scale | real precision | real direction | verbatim leak |
+|---|---|---|---|
+| **0.5B (this)** | **0.243** | **0.369** | **37.4%** |
+| 1.5B (main) | 0.270 | 0.496 | 22.4% |
+| 3B (synthetic) | 0.325 | 0.468 | 10.9% |
+| **3B (real corpus)** | **0.64** | — | **~0** |
+
+Leak shrinks with scale (capacity), but only **real training data** closes the real-world gap
+(bottom row). Direction confusion does not improve with size.
+
+## Intended use
+
+- ✅ Reproducing / studying the synthetic-training memorization-leak failure at minimal scale.
+- ❌ Not for production lineage — use the [real-corpus 3B](https://huggingface.co/wallfacers/weft-lineage-extractor-3b).
+
+## Usage, prompt format, training details, citation
+
+Identical to the main model (this variant uses `task_type: PYTHON | SHELL`; everything else the same):
+**[weft-lineage-extractor-1.5b](https://huggingface.co/wallfacers/weft-lineage-extractor-1.5b)**.
+
+- **Dataset & reports:** [wallfacers/weft-script-lineage-synth](https://huggingface.co/datasets/wallfacers/weft-script-lineage-synth)
+- **Base model:** [Qwen/Qwen2.5-Coder-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct)
+- **Platform:** [Weft (data-weave)](https://github.com/wallfacers/data-weave)

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\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>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\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" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": null,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 896,
+  "initializer_range": 0.02,
+  "intermediate_size": 4864,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 24,
+  "model_type": "qwen2",
+  "num_attention_heads": 14,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 2,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.5.0",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+{
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "5.5.0"
+}

model.safetensors

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+size 988097824

tokenizer.json

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+size 11421892

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_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|>"
+  ],
+  "is_local": false,
+  "model_max_length": 32768,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}