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

.gitattributes

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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
+- it
+- es
+- zh
+- ru
+- hi
+datasets:
+- cs-552-2026-aaty/sft_mixture
+- cs-552-2026-aaty/grpo_mixture
+tags:
+- qwen3
+- group-model
+- reasoning
+- sft
+- grpo
+- cs-552
+- mnlp
+metrics:
+- accuracy
+---
+
+# group_model
+
+Group model for team **AATY**, CS-552 MNLP (EPFL). It is `Qwen/Qwen3-1.7B`
+post-trained with supervised fine-tuning followed by GRPO, and is the
+whole-team submission evaluated on all four domains: math, general knowledge,
+safety, and multilinguality. Its leaderboard rank is the 4-domain average.
+
+## Model details
+
+- **Base model:** `Qwen/Qwen3-1.7B`
+- **Post-training:** SFT (LoRA adapter, merged back into the base weights),
+  then GRPO seeded from the SFT checkpoint with reward functions for the math
+  and reasoning objectives
+- **Domains:** math (free-form, pass@8) and general knowledge, safety,
+  multilinguality (multiple-choice, 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{...}`.
+The training mix covers both question styles, because the group model is scored
+on both:
+
+Free-form:
+
+```
+Q: What is the smallest prime greater than 100?
+A: ...reasoning... \boxed{101}
+```
+
+Multiple-choice (the boxed content is the option letter, with 2 to 20 options):
+
+```
+Q: Which of the following is a noble gas?
+A) Oxygen
+B) Argon
+C) Nitrogen
+D) Hydrogen
+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/group_model")
+model = AutoModelForCausalLM.from_pretrained("cs-552-2026-aaty/group_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
+
+- **SFT:** `cs-552-2026-aaty/sft_mixture`, the chat-formatted mixture built from
+  public QA, knowledge, instruction, and math datasets.
+- **GRPO:** `cs-552-2026-aaty/grpo_mixture`, prompts with verifiable answers
+  used for reward-driven optimization.
+
+See the team data pipeline in `code/data/` for the exact sources and filters.

chat_template.jinja

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+{%- if enable_thinking is not defined %}{%- set enable_thinking = true %}{%- endif %}{%- 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' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- 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 %}

config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 6144,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 40960,
+  "max_window_layers": 28,
+  "model_type": "qwen3",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "pad_token_id": null,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.9.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+{
+  "do_sample": true,
+  "eos_token_id": 151645,
+  "max_new_tokens": 4096,
+  "pad_token_id": 151643,
+  "temperature": 0.3,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "5.9.0"
+}

model.safetensors

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+size 3441185608

tokenizer.json

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+size 11422650

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "is_local": false,
+  "local_files_only": false,
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}