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Model: cs-552-2026-catma/general_knowledge_model
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# Automated MNLP evaluation report
- **Model repo:** [`cs-552-2026-catma/general_knowledge_model`](https://huggingface.co/cs-552-2026-catma/general_knowledge_model)
- **Owner(s):** group **catma**
- **Generated at:** 2026-06-11T06:23:10+00:00 (UTC)
- **Pipeline:** [mnlp-project-ci](https://github.com/eric11eca/mnlp-project-ci)
_This PR is opened automatically by the course CI. It is **non-blocking** — you do not need to merge it. The next nightly run will refresh this file._
## Evaluated checkpoint
- **Commit:** [`381739b`](https://huggingface.co/cs-552-2026-catma/general_knowledge_model/commit/381739bfd0d506028c4782a6fad618c9f4d7e03e)
- **Message:** Upload GK Stage 5 merge-aware DPO checkpoint-500 merged local best 249-290
- **Committed:** 2026-06-04T19:48:25+00:00
## Summary
| Benchmark | Accuracy | Status |
|---|---:|---|
| Math | — | not run |
| Knowledge | 0.4900 | ok |
| Multilingual | — | not run |
| Safety | — | not run |
## Sample completions
_Prompts are intentionally omitted to avoid revealing benchmark contents. For multi-completion problems, only one completion is shown per sample._
### Knowledge
**Correct** (1 shown)
- **reference**: `E`
- **overall** (1/1 completions correct)
- **extracted** (✓): `E`
- **completion**:
```text
\boxed{E}
```
**Incorrect** (1 shown)
- **reference**: `B`
- **overall** (0/1 completions correct)
- **extracted** (✗): `C`
- **completion**:
```text
\boxed{C}
```

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---
license: apache-2.0
language:
- en
base_model: Qwen/Qwen3-1.7B
pipeline_tag: text-generation
library_name: transformers
tags:
- qwen3
- sft
- dpo
- lora
- general-knowledge
- multiple-choice
- cs-552
datasets:
- cais/mmlu
- TIGER-Lab/MMLU-Pro
- allenai/ai2_arc
- allenai/openbookqa
- allenai/sciq
- tau/commonsense_qa
- allenai/quartz
metrics:
- accuracy
---
# General Knowledge Model
This model is the General Knowledge individual-model submission for the CS-552 Modern NLP course project. It is a merged post-trained checkpoint based on [`Qwen/Qwen3-1.7B`](https://huggingface.co/Qwen/Qwen3-1.7B), developed by Tuan Dang Nguyen for closed-book multiple-choice general knowledge evaluation.
The uploaded checkpoint corresponds to the final Stage 5 merge-aware DPO model:
```text
sft_dpo_stage5_error_contrastive_mergeaware_v1_r16_lr8e8_beta003_eval100_500_merged
```
## Task And Output Format
The model receives a multiple-choice question and should answer with exactly one option letter inside a LaTeX boxed expression:
```text
\boxed{C}
```
The evaluation pipeline extracts the letter inside `\boxed{...}`. Any surrounding reasoning is ignored for scoring, but the intended behavior is a concise boxed final answer.
## Training Summary
The training campaign used LoRA-based post-training on top of `Qwen/Qwen3-1.7B`.
Main stages:
- Supervised fine-tuning on mixed general-knowledge multiple-choice data.
- Hard-source and CI-style refinements, including MMLU-Pro and variable option-count examples.
- Plus Quartz v1 SFT, which first reached the best hidden-CI score.
- Conservative Stage 2 SFT refinement from the Plus Quartz anchor.
- Stage 5 merge-aware DPO using the Stage 2 model's own wrong boxed answers plus protection pairs.
The final Stage 5 model is selected because it is the strongest merged local checkpoint. The strongest hidden-CI score was first reached by the Plus Quartz SFT anchor, and the later Stage 2/DPO submissions tied that hidden score.
## Evaluation
Local evaluation used the course ten-example public General Knowledge validation snapshot in both prompt modes plus a 290-example diagnostic set built from public multiple-choice sources.
| Model | Role | Local diagnostic | Public 10-example validation | Extraction | Hidden CI |
| --- | --- | ---: | ---: | ---: | ---: |
| `sft_plus_quartz_v1_r128_7200_merged` | First hidden-CI anchor | 247/290 | 7/10 in both prompt modes | 100% | **0.4900** |
| `sft_stage2_plus_quartz_v1_r32_lr5e7_800_merged` | Best retained SFT refinement | 248/290 | 7/10 in both prompt modes | 100% | 0.4900 tie |
| `sft_dpo_stage2_plus_quartz_v1_from_800_mistake_only_r16_lr2e7_beta005_200_merged` | Early DPO refinement | 248/290 | 7/10 in both prompt modes | 100% | 0.4900 tie |
| `sft_dpo_stage5_error_contrastive_mergeaware_v1_r16_lr8e8_beta003_eval100_500_merged` | Uploaded final model | **249/290** | 7/10 in both prompt modes | 100% | 0.4900 tie |
Interpretation: DPO improved the retained merged local diagnostic result and made checkpoint selection more robust, but it did not improve beyond the best hidden-CI SFT score of `0.4900`.
## Usage Notes
This checkpoint is a fully merged model, not a standalone LoRA adapter. It can be loaded with standard `transformers` text-generation tooling.
For best compatibility with the course evaluator:
- Ask closed-book multiple-choice questions.
- Include clear answer options.
- Require the model to finish with `\boxed{LETTER}`.
- Score only the extracted boxed letter.
Example prompt:
```text
Answer the following multiple-choice question. Return only the final answer in the form \boxed{LETTER}.
Question: Which planet is known as the Red Planet?
A) Venus
B) Mars
C) Jupiter
D) Mercury
```
Expected style:
```text
\boxed{B}
```
## Limitations
This model is specialized for English closed-book multiple-choice general knowledge. It is not a general chat assistant and should not be used as a reliable factual oracle outside the benchmark setting. Local diagnostics were useful for model selection but did not perfectly predict hidden-CI changes; hidden-CI accuracy remained tied at `0.4900` for the final refinements.

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{%- set enable_thinking = false %}
{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0].role == 'system' %}
{{- messages[0].content + '\n\n' }}
{%- endif %}
{{- "# 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 answering a multiple-choice question. Your entire final response must be exactly one LaTeX box containing only the option letter. Do not explain. Do not write the option text. Do not output multiple letters. Valid examples: \\boxed{A}, \\boxed{B}, \\boxed{C}. /no_think<|im_end|>\n' }}
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{%- for message in messages[::-1] %}
{%- set index = (messages|length - 1) - loop.index0 %}
{%- 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>')) %}
{%- set ns.multi_step_tool = false %}
{%- set ns.last_query_index = index %}
{%- endif %}
{%- endfor %}
{%- for message in messages %}
{%- if message.content is string %}
{%- set content = message.content %}
{%- else %}
{%- set content = '' %}
{%- endif %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{%- set reasoning_content = '' %}
{%- if message.reasoning_content is string %}
{%- set reasoning_content = message.reasoning_content %}
{%- else %}
{%- if '</think>' in content %}
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
{%- set content = content.split('</think>')[-1].lstrip('\n') %}
{%- endif %}
{%- endif %}
{%- if loop.index0 > ns.last_query_index %}
{%- if loop.last or (not loop.last and reasoning_content) %}
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- if message.tool_calls %}
{%- for tool_call in message.tool_calls %}
{%- if (loop.first and content) or (not loop.first) %}
{{- '\n' }}
{%- endif %}
{%- if tool_call.function %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{%- if tool_call.arguments is string %}
{{- tool_call.arguments }}
{%- else %}
{{- tool_call.arguments | tojson }}
{%- endif %}
{{- '}\n</tool_call>' }}
{%- endfor %}
{%- endif %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- 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' }}
{%- if enable_thinking is defined and enable_thinking is false %}
{{- '<think>\n\n</think>\n\n' }}
{%- endif %}
{%- endif %}

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