Add ThinkingCap-Qwen3.5-2B LoRA adapter (loss-level variant) with model card

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,107 @@
+---
+base_model: Qwen/Qwen3.5-2B
+library_name: peft
+license: apache-2.0
+pipeline_tag: text-generation
+tags:
+  - token-efficient
+  - efficient-thinking
+  - reasoning
+  - grpo
+  - lora
+  - peft
+---
+
+# ThinkingCap-Qwen3.5-2B (loss-level variant)
+
+A LoRA adapter for **Qwen3.5-2B** trained with GRPO to reach the same answers with **43% fewer thinking tokens** — while *improving* accuracy by **+13 to +17 points**, because the dominant failure of the base model is not verbosity but never finishing its reasoning at all.
+
+This adapter is the **loss-level variant** of a controlled study on how to compress chain-of-thought at small scale. It combines three modifications to the GRPO objective — Dr.GRPO normalization, positional advantage decay inside the reasoning span, and KL restricted to that span — on top of a correctness-gated length penalty. The companion study that isolates each component is linked at the bottom.
+
+---
+
+## In-domain results
+
+Measured under a **natural-length protocol**: greedy decoding, a 4,096-token budget, and *no forced truncation of thinking*. A response whose reasoning span never closes contains no answer and is scored incorrect — so token reduction and accuracy are read off the same generations.
+
+| Benchmark | Base Acc | Ours Acc | Base Thinking Tokens | Ours Thinking Tokens | Reduction |
+|---|:---:|:---:|:---:|:---:|:---:|
+| Held-out dev (300 problems) | 69.0 | **82.3** | 2,922 | 1,656 | **↓ 43.3%** |
+| GSM8K | 50.0 | **66.7** | 3,545 | 2,438 | **↓ 31.2%** |
+| ARC-Challenge | 70.8 | **87.5** | 2,216 | 1,247 | **↓ 43.7%** |
+
+## Out-of-domain
+
+| Benchmark | Base Acc | Ours Acc | Base Thinking Tokens | Ours Thinking Tokens | Reduction |
+|---|:---:|:---:|:---:|:---:|:---:|
+| MATH-500 | 20.8 | 20.8 | 4,096 | 3,320 | **↓ 19.0%** |
+
+Out of distribution the adapter preserves accuracy exactly while cutting a fifth of the reasoning budget. Compression transfers; the accuracy gains do not — a 2B model has little headroom on competition mathematics either way.
+
+## Failure modes
+
+The base model's real pathology is **non-termination**: it finds the answer early, then loops on self-verification and never emits a closing marker.
+
+| Metric (out-of-domain) | Base | Ours |
+|---|:---:|:---:|
+| Reasoning never closes within budget | 100% | **75%** |
+| Terminates normally (EOS) | 0% | **25%** |
+
+On in-domain data the effect is far stronger — unclosed reasoning drops from 58% to 21%. Most of the accuracy gain is simply answers that now exist.
+
+## Training
+
+| | |
+|---|---|
+| Base model | Qwen/Qwen3.5-2B (thinking mode) |
+| Method | GRPO + LoRA (rank 16, α 32, bf16), 200 optimizer steps |
+| Reward | correctness-gated length penalty; zero reward when the answer is wrong **or** the reasoning span never closes |
+| Objective modifications | Dr.GRPO normalization · positional advantage decay over the thinking span · KL restricted to the thinking span |
+| Data | 10.7K verifiable problems (GSM8K + ARC training splits), deduplicated against every evaluation set |
+| Hardware | 1× NVIDIA RTX 5080 (16 GB) |
+
+No generation-time intervention is used at any point — no forced stopping, no length cap on thinking. The model learns to terminate purely from the reward.
+
+## Usage
+
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tok = AutoTokenizer.from_pretrained("Qwen/Qwen3.5-2B")
+model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-2B", dtype="bfloat16", device_map="auto")
+model = PeftModel.from_pretrained(model, "drlee1/ThinkingCap-Qwen3.5-2B")
+
+messages = [{"role": "user", "content": "If a train travels 120 km in 1.5 hours, what is its average speed?"}]
+prompt = tok.apply_chat_template(
+    messages, add_generation_prompt=True, enable_thinking=True, tokenize=False
+)
+
+inputs = tok(prompt, return_tensors="pt").to(model.device)
+out = model.generate(**inputs, max_new_tokens=2048, do_sample=False)
+print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
+```
+
+`enable_thinking=True` is required — Qwen3.5-2B runs in non-thinking mode by default, and this adapter is trained on the thinking-mode chat template.
+
+To merge the adapter for serving with vLLM or SGLang, load the base model, apply the adapter, and call `merge_and_unload()` before saving.
+
+## Honest scope
+
+- **Single training seed, 200 optimizer steps.** Numbers are a screening result, not a multi-seed benchmark. Evaluation uses 300 held-out problems for the dev set and 24 items per public benchmark.
+- **A simpler recipe is stronger.** In the companion ablation, dropping all three loss-level modifications and keeping only the reward-level length penalty reaches **79% token reduction at 90.7% dev accuracy** — better on both axes than this adapter. Each objective modification measurably *costs* compression in this regime (2B, LoRA, small rollout groups). This adapter is released as the loss-level arm of that comparison, not as the recommended configuration.
+- Findings are specific to this scale and setup; they are not a general verdict on the underlying techniques in their original settings.
+
+## Citation
+
+```bibtex
+@misc{reducing-think-token-2026,
+  title  = {Reducing Thinking Tokens via Reinforcement Learning:
+            A Controlled Study on Loss-Level Interventions},
+  author = {DONGRYEOLLEE1},
+  year   = {2026},
+  url    = {https://github.com/DONGRYEOLLEE1/reducing-think-token}
+}
+```
+
+**Acknowledgements.** Inspired by [ThinkingCap-Qwen3.6-27B](https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.6-27B) (BottleCapAI). Built on Qwen3.5-2B, TRL, and PEFT. The full study — including the component-wise ablation with confidence intervals — is on [GitHub](https://github.com/DONGRYEOLLEE1/reducing-think-token).

adapter_config.json

@@ -0,0 +1,50 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "Qwen/Qwen3.5-2B",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 32,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.19.1",
+  "qalora_group_size": 16,
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "up_proj",
+    "in_proj_qkv",
+    "k_proj",
+    "v_proj",
+    "q_proj",
+    "out_proj",
+    "down_proj",
+    "o_proj",
+    "gate_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cde98aa3049d02f31abeb9ad1c24a81f871f92c45bc981d6f328bd0650cf5c1d
+size 57842040

chat_template.jinja

@@ -0,0 +1,154 @@
+{%- set image_count = namespace(value=0) %}
+{%- set video_count = namespace(value=0) %}
+{%- macro render_content(content, do_vision_count, is_system_content=false) %}
+    {%- if content is string %}
+        {{- content }}
+    {%- elif content is iterable and content is not mapping %}
+        {%- for item in content %}
+            {%- if 'image' in item or 'image_url' in item or item.type == 'image' %}
+                {%- if is_system_content %}
+                    {{- raise_exception('System message cannot contain images.') }}
+                {%- endif %}
+                {%- if do_vision_count %}
+                    {%- set image_count.value = image_count.value + 1 %}
+                {%- endif %}
+                {%- if add_vision_id %}
+                    {{- 'Picture ' ~ image_count.value ~ ': ' }}
+                {%- endif %}
+                {{- '<|vision_start|><|image_pad|><|vision_end|>' }}
+            {%- elif 'video' in item or item.type == 'video' %}
+                {%- if is_system_content %}
+                    {{- raise_exception('System message cannot contain videos.') }}
+                {%- endif %}
+                {%- if do_vision_count %}
+                    {%- set video_count.value = video_count.value + 1 %}
+                {%- endif %}
+                {%- if add_vision_id %}
+                    {{- 'Video ' ~ video_count.value ~ ': ' }}
+                {%- endif %}
+                {{- '<|vision_start|><|video_pad|><|vision_end|>' }}
+            {%- elif 'text' in item %}
+                {{- item.text }}
+            {%- else %}
+                {{- raise_exception('Unexpected item type in content.') }}
+            {%- endif %}
+        {%- endfor %}
+    {%- elif content is none or content is undefined %}
+        {{- '' }}
+    {%- else %}
+        {{- raise_exception('Unexpected content type.') }}
+    {%- endif %}
+{%- endmacro %}
+{%- if not messages %}
+    {{- raise_exception('No messages provided.') }}
+{%- endif %}
+{%- if tools and tools is iterable and tools is not mapping %}
+    {{- '<|im_start|>system\n' }}
+    {{- "# Tools\n\nYou have access to the following functions:\n\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>" }}
+    {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n</IMPORTANT>' }}
+    {%- if messages[0].role == 'system' %}
+        {%- set content = render_content(messages[0].content, false, true)|trim %}
+        {%- if content %}
+            {{- '\n\n' + content }}
+        {%- endif %}
+    {%- endif %}
+    {{- '<|im_end|>\n' }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {%- set content = render_content(messages[0].content, false, true)|trim %}
+        {{- '<|im_start|>system\n' + 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" %}
+        {%- set content = render_content(message.content, false)|trim %}
+        {%- if not(content.startswith('<tool_response>') and content.endswith('</tool_response>')) %}
+            {%- set ns.multi_step_tool = false %}
+            {%- set ns.last_query_index = index %}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if ns.multi_step_tool %}
+    {{- raise_exception('No user query found in messages.') }}
+{%- endif %}
+{%- for message in messages %}
+    {%- set content = render_content(message.content, true)|trim %}
+    {%- if message.role == "system" %}
+        {%- if not loop.first %}
+            {{- raise_exception('System message must be at the beginning.') }}
+        {%- endif %}
+    {%- elif message.role == "user" %}
+        {{- '<|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 %}
+        {%- set reasoning_content = reasoning_content|trim %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content + '\n</think>\n\n' + content }}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if tool_call.function is defined %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {%- if loop.first %}
+                    {%- if content|trim %}
+                        {{- '\n\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                    {%- else %}
+                        {{- '<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                    {%- endif %}
+                {%- else %}
+                    {{- '\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                {%- endif %}
+                {%- if tool_call.arguments is defined %}
+                    {%- for args_name, args_value in tool_call.arguments|items %}
+                        {{- '<parameter=' + args_name + '>\n' }}
+                        {%- set args_value = args_value | tojson | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string %}
+                        {{- args_value }}
+                        {{- '\n</parameter>\n' }}
+                    {%- endfor %}
+                {%- endif %}
+                {{- '</function>\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.previtem and loop.previtem.role != "tool" %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if not loop.last and loop.nextitem.role != "tool" %}
+            {{- '<|im_end|>\n' }}
+        {%- elif loop.last %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- else %}
+        {{- raise_exception('Unexpected message role.') }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is true %}
+        {{- '<think>\n' }}
+    {%- else %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:06b9509352d2af50381ab2247e083b80d32d5c0aba91c272ca9ff729b6a0e523
+size 19989325

tokenizer_config.json

@@ -0,0 +1,32 @@
+{
+  "add_prefix_space": false,
+  "audio_bos_token": "<|audio_start|>",
+  "audio_eos_token": "<|audio_end|>",
+  "audio_token": "<|audio_pad|>",
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "image_token": "<|image_pad|>",
+  "is_local": true,
+  "local_files_only": false,
+  "model_max_length": 262144,
+  "model_specific_special_tokens": {
+    "audio_bos_token": "<|audio_start|>",
+    "audio_eos_token": "<|audio_end|>",
+    "audio_token": "<|audio_pad|>",
+    "image_token": "<|image_pad|>",
+    "video_token": "<|video_pad|>",
+    "vision_bos_token": "<|vision_start|>",
+    "vision_eos_token": "<|vision_end|>"
+  },
+  "pad_token": "<|endoftext|>",
+  "pretokenize_regex": "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?[\\p{L}\\p{M}]+|\\p{N}| ?[^\\s\\p{L}\\p{M}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null,
+  "video_token": "<|video_pad|>",
+  "vision_bos_token": "<|vision_start|>",
+  "vision_eos_token": "<|vision_end|>"
+}