---
license: apache-2.0
base_model: Qwen/Qwen3-1.7B
pipeline_tag: text-generation
library_name: transformers
language:
- en
datasets:
- cs-552-2026-aaty/sft_mixture
tags:
- qwen3
- general-knowledge
- reasoning
- sft
- cs-552
- mnlp
metrics:
- accuracy
---

# general_knowledge_model

General-knowledge specialty model for team **AATY**, CS-552 MNLP (EPFL).
It is `Qwen/Qwen3-1.7B` supervised fine-tuned on a general-knowledge mixture
to answer closed-book factual and reasoning questions across the sciences,
humanities, and geography.

## Model details

- **Base model:** `Qwen/Qwen3-1.7B`
- **Post-training:** supervised fine-tuning (LoRA adapter, merged back into the
  base weights)
- **Domain:** general knowledge, multiple-choice, scored at pass@1
- **Format:** vLLM-loadable safetensors with `config.json`,
  `generation_config.json`, and a tokenizer `chat_template`

## Output contract

The model writes its reasoning and then wraps the final answer in `\boxed{...}`.
For multiple-choice items the boxed content is the letter of the chosen option,
and option counts can range from 2 to 20.

```
Q: Which planet is closest to the Sun?
A) Venus
B) Mercury
C) Mars
D) Earth
A: ...reasoning... \boxed{B}
```

## Thinking mode

This model runs in **thinking mode**: it emits a `<think>...</think>` reasoning
block before the final `\boxed{...}` answer. Thinking is forced on inside the
chat template, because the evaluation passes only
`tokenizer.apply_chat_template(messages, add_generation_prompt=True)` with no
`enable_thinking` argument, so the template default is the only signal honored.

The relevant line in `chat_template.jinja`:

```jinja
{%- set enable_thinking = true %}
```

## Usage

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tok = AutoTokenizer.from_pretrained("cs-552-2026-aaty/general_knowledge_model")
model = AutoModelForCausalLM.from_pretrained("cs-552-2026-aaty/general_knowledge_model")

messages = [{"role": "user", "content": "What is the capital of Australia?"}]
prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=512)
print(tok.decode(out[0], skip_special_tokens=True))
```

## Training data

Supervised fine-tuning on `cs-552-2026-aaty/sft_mixture`, the chat-formatted
mixture built from public QA and knowledge datasets. See the team data pipeline
in `code/data/` for the exact sources and filters.