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Model: visproj/proofkit-qwen0.5b-7k
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+library_name: transformers
+pipeline_tag: text-generation
+language:
+- en
+tags:
+- proofkit
+- fine-tuned
+- sft
+- lora
+- build-small-hackathon
+- work-sample
+datasets:
+- visproj/proofkit-sft
+---
+
+# ProofKit Qwen 0.5B — fine-tuned (direct SFT)
+
+`Qwen/Qwen2.5-0.5B-Instruct` fine-tuned **directly** on the ProofKit SFT set
+([`visproj/proofkit-sft`](https://huggingface.co/datasets/visproj/proofkit-sft),
+~7,000 synthetic examples). LoRA-trained, then merged to standalone weights. This is
+the **in-Space Transformers** option in [ProofKit](https://huggingface.co/spaces/visproj/proofkit) (runs on ZeroGPU / a small
+GPU; loads lazily on first generation).
+
+It learns ProofKit's task contracts — section drafting, co-author drafting from rough
+user answers, revision actions, and strict-JSON scenario / recommendation / readiness
+/ portfolio generation.
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+m = "visproj/proofkit-qwen0.5b-7k"
+tok = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")  # carries the chat template
+model = AutoModelForCausalLM.from_pretrained(m, torch_dtype=torch.float16).to("cuda").eval()
+
+# NOTE: prompt it with ProofKit's trained prompt_formats.py shapes — see below.
+messages = [{"role": "system", "content": SYSTEM}, {"role": "user", "content": PROMPT}]
+text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+ids = tok(text, return_tensors="pt").to("cuda")
+out = model.generate(**ids, max_new_tokens=600, do_sample=True, temperature=0.3, top_p=0.9)
+print(tok.decode(out[0][ids["input_ids"].shape[-1]:], skip_special_tokens=True))
+```
+
+## Evaluation (post-fix, 3-judge panel)
+
+Mean score (0–100) on 15 held-out prompts, graded by Claude Opus 4.7, GPT-5.5, and a
+local Qwen-3B (`gpt-oss experts` is a deliberately *un-retrained* stale control):
+
+| model | Claude | GPT-5.5 | Qwen-3B | **Avg** |
+|---|---:|---:|---:|---:|
+| gpt-5.5 (frontier ceiling) | 94.6 | 95.6 | 90.8 | **93.7** |
+| gpt-oss attn (retrained teacher) | 82.0 | 66.8 | 81.4 | **76.7** |
+| **qwen-0.5b distilled (served)** | 79.0 | 68.6 | 82.2 | **76.6** |
+| **qwen-0.5b direct 7k (served)** | 78.6 | 64.4 | 82.0 | **75.0** |
+| gpt-oss experts *(stale control)* | 67.6 | 68.6 | 81.8 | **72.7** |
+| qwen-3b base | 62.1 | 67.1 | 80.5 | **69.9** |
+| gpt-oss base | 55.4 | 53.8 | 68.2 | **59.1** |
+| qwen-0.5b base | 36.5 | 44.5 | 67.9 | **49.7** |
+
+Both served retrained 0.5Bs beat the stale control and every untuned base across all
+three judges, and the distilled 0.5B ≈ ties its own 20B teacher.
+
+## Limitations
+
+- **0.5B capacity.** Reliably carries trained-style specifics, but can miss a truly
+  arbitrary novel token and occasionally garbles. ProofKit adds a runtime fallback to a
+  hosted instruct baseline when a draft drops the user's answers.
+- **Prompt-format-frozen** (see below) — not a general chat model.
+
+## About ProofKit
+
+[ProofKit](https://huggingface.co/spaces/visproj/proofkit) is a work-sample generator for job seekers — it turns a target
+role, background, and skills-to-prove into a realistic, **clearly-fictional**
+practice work sample (a role-specific challenge, a guided builder, a readiness
+review, and a recruiter-ready portfolio packet). Built for the Hugging Face **Build
+Small Hackathon** (Backyard AI track). Integrity rules are load-bearing: outputs
+never claim real employment, metrics are labeled hypothetical, and exports carry an
+ethical disclosure.
+
+### The ProofKit model family
+
+| Repo | What it is |
+|---|---|
+| [`visproj/proofkit-qwen0.5b-7k`](https://huggingface.co/visproj/proofkit-qwen0.5b-7k) | Qwen2.5-0.5B fine-tuned directly on the 7k set (Transformers) |
+| [`visproj/proofkit-gpt-oss-20b-lora`](https://huggingface.co/visproj/proofkit-gpt-oss-20b-lora) | gpt-oss-20b LoRA — the distillation **teacher** |
+| [`visproj/proofkit-distilled-qwen0.5b`](https://huggingface.co/visproj/proofkit-distilled-qwen0.5b) | Qwen2.5-0.5B distilled from the teacher (merged) |
+| [`visproj/proofkit-distilled-qwen0.5b-gguf`](https://huggingface.co/visproj/proofkit-distilled-qwen0.5b-gguf) | GGUF of the distilled student (llama.cpp — **served**) |
+| [`visproj/proofkit-sft`](https://huggingface.co/datasets/visproj/proofkit-sft) | SFT dataset (synthetic, license-safe) |
+| [`visproj/proofkit-distill-qwen0.5b`](https://huggingface.co/datasets/visproj/proofkit-distill-qwen0.5b) | Distillation dataset (teacher completions) |
+
+### A note on training data (the "static responses" fix)
+
+An earlier version of these models produced repetitive, input-ignoring drafts. The
+root cause was **synthetic-data leakage**: the dataset rendered the example *user
+answers* and the *target* from the same template slots, so the model learned
+`target = template` instead of `target = f(input)`. The fix — **faithfulness anchors**
+(a distinctive token shared by the answer and the target) + **seeded per-example
+variation** across every task, then a full-chain retrain — is what these current
+weights reflect.
+
+### Prompt format is a frozen contract
+
+These 0.5B models were trained on the **exact** prompt shapes from ProofKit's
+`prompt_formats.py`. They only behave well when prompted in that format; reworded or
+free-form prompts push them off-distribution. They are purpose-built components of the
+ProofKit app, not general chat models.

added_tokens.json

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config.json

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+{
+  "_name_or_path": "Qwen/Qwen2.5-0.5B-Instruct",
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
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+  "hidden_act": "silu",
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+  "initializer_range": 0.02,
+  "intermediate_size": 4864,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 21,
+  "model_type": "qwen2",
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+  "num_hidden_layers": 24,
+  "num_key_value_heads": 2,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "torch_dtype": "float16",
+  "transformers_version": "4.46.3",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.46.3"
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special_tokens_map.json

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+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\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>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\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\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
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+  "errors": "replace",
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+  "tokenizer_class": "Qwen2Tokenizer",
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+}