Remove model card for repo consistency

README.md

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----
-license: apache-2.0
-base_model:
-- Qwen/Qwen3-1.7B-Base
-library_name: transformers
----
- # CS-552 Ma Que — General Knowledge Model
-
-  General knowledge expert for the EPFL CS-552 (Modern NLP, Spring 2026) group project
-  "Building a robust small language model for edge devices" (Group 11, Ma Que).
-  This is an individual model; the team also maintains a merged
-  group model (https://huggingface.co/cs-552-2026-ma-que/group_model).
-
-  ## What it is
-
-  - Base model: Qwen/Qwen3-1.7B-Base (https://huggingface.co/Qwen/Qwen3-1.7B-Base) (1.7B params).
-  - Post-training: LoRA SFT using nlp_project/sft_thinking.py with the configuration in nlp_project/cfgs_thinking.yml. The run uses LoRA rank 256, alpha 256, dropout
-    0, and targets q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, and down_proj. Training uses bf16, gradient checkpointing, Liger kernel, assistant-only loss,
-    max sequence length 6144, learning rate 2e-4, cosine scheduler, 5% warmup, batch size 20, gradient accumulation 1, and 2 epochs.
-  - Task: general knowledge multiple-choice answering with reasoning-style responses. The model reads a question with lettered options and commits to one boxed option
-    letter.
-  - Output contract: the final answer is emitted once as \boxed{X} (X is a single capital option letter), for the course OpenCompass/vLLM parser. Thinking mode is ON.
-  - How behaviour is set: the Qwen3 chat template is applied automatically by tokenizer.apply_chat_template(messages, add_generation_prompt=True). Training examples
-    include assistant reasoning traces with <think>...</think>, and the model is intended to answer in thinking mode before producing the final boxed answer.
-
-  ## Inference / decoding
-
-  Values below are exactly those in generation_config.json:
-
-  ┌────────────────┬────────────────────────────────────────────────────────────────┐
-  │ param          │ value                                                          │
-  ├────────────────┼────────────────────────────────────────────────────────────────┤
-  │ do_sample      │ true                                                           │
-  │ temperature    │ 0.6                                                            │
-  │ top_k          │ 20                                                             │
-  │ top_p          │ 0.95                                                           │
-  │ max_new_tokens │ not set in generation_config.json; use 16384 for final grading │
-  └────────────────┴────────────────────────────────────────────────────────────────┘
-
-  Usage:
-
-      from transformers import AutoModelForCausalLM, AutoTokenizer
-
-      repo = "cs-552-2026-ma-que/general_knowledge_model"
-      tok = AutoTokenizer.from_pretrained(repo)
-      model = AutoModelForCausalLM.from_pretrained(
-          repo,
-          torch_dtype="auto",
-          device_map="auto",
-      )
-
-      messages = [{"role": "user", "content":
-          "Which planet is known as the Red Planet?\n"
-          "A) Venus\nB) Mars\nC) Jupiter\nD) Mercury"}]
-
-      text = tok.apply_chat_template(
-          messages,
-          tokenize=False,
-          add_generation_prompt=True,
-      )
-
-      out = model.generate(
-          **tok(text, return_tensors="pt").to(model.device),
-          max_new_tokens=16384,
-      )
-
-      print(tok.decode(out[0], skip_special_tokens=True))   # -> ... \boxed{B}
-
-  The model is also vLLM-loadable for batched evaluation.
-
-  ## Training data
-
-  The thinking model was trained with sft_thinking.py using the dataset
-  TangYeqing/maque-data, as configured in cfgs_thinking.yml.
-
-  The script loads the dataset's train split and performs a local
-  train_test_split(test_size=0.005, seed=42). Each row contains conversational
-  messages; if a row has no system message, the training script prepends:
-
-  You are a helpful assistant that provides step-by-step solutions to math problems.
-
-  The assistant turns already contain literal <think>...</think> reasoning traces,
-  which are preserved during SFT. Training uses assistant-only loss, so the loss is
-  computed on the assistant response, including the thinking trace and final boxed
-  answer.
-
-  ## Intended use & limitations
-
-  Research artifact for the CS-552 general knowledge benchmark; produces boxed
-  multiple-choice answers only. It is not an authoritative factual system and may
-  encode outdated, incomplete, or incorrect facts, so outputs should be verified
-  before any real-world or safety-critical use.
-
-  ## Citation
-
-  Built on Qwen3-1.7B (Qwen Team, 2025), arXiv:2505.09388 —
-  https://arxiv.org/abs/2505.09388