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Model: cs-552-2026-aaty/general_knowledge_model
Source: Original Platform

README.md

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+---
+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.