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@@ -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

@@ -1,3 +1,166 @@
 ---
-license: mit
+base_model: Qwen/Qwen3.5-27B
+library_name: peft
+license: apache-2.0
+pipeline_tag: text-generation
+language:
+- en
+tags:
+- lora
+- sft
+- qlora
+- clembench
+- game-playing
+- process-reward-model
+- transformers
+- trl
 ---
+
+# Qwen3.5-27B-sft-ep1
+
+A QLoRA supervised fine-tune of [Qwen/Qwen3.5-27B](https://huggingface.co/Qwen/Qwen3.5-27B) trained on high-scoring game trajectories from the [Playpen](https://github.com/lm-playpen/playpen) benchmark (clembench 2.0). This model serves as the policy in a Process Reward Model (PRM) guided inference pipeline, where a companion PRM scores candidate responses at each game turn to select the best action.
+
+## Model Details
+
+- **Developed by:** Diginyx
+- **Base model:** Qwen/Qwen3.5-27B
+- **Model type:** Causal LM — LoRA adapter (PEFT)
+- **Language:** English
+- **License:** Apache 2.0
+- **Fine-tuning method:** QLoRA (4-bit NF4 base + LoRA adapters)
+- **Training framework:** TRL + HuggingFace PEFT
+
+## Training Methodology
+
+The model is trained via supervised fine-tuning on game transcripts where the outcome was a win (positive clemscore contribution), filtered from rollouts of the base Qwen3.5-27B-Instruct model playing all clembench 2.0 games. The goal is to teach the policy the turn-level response patterns associated with successful multi-player game trajectories.
+
+**Training pipeline:**
+1. Run the base model on all clembench games to collect rollout transcripts
+2. Filter to transcripts with a positive game outcome (win)
+3. Fine-tune on the winning turns using QLoRA
+
+**Design decisions:**
+- **Positive-only filtering**: Using only winning trajectories (rather than all rollouts with reward labels) avoids the model learning from ambiguous partial-credit or losing sequences, keeping the training signal clean.
+- **4-bit QLoRA**: Reduces VRAM from ~55 GB to ~14 GB, allowing the full 27B model to train on a single 48 GB A40 alongside optimizer states. This makes training accessible without multi-node tensor parallelism.
+- **LoRA over full fine-tune**: Preserves the base model's general language capabilities while adapting the turn-level game response style. The small adapter (r=16) also prevents overfitting on the filtered game corpus.
+- **Max length 1024**: Game turns are typically short; truncating at 1024 tokens keeps the full-vocabulary causal-LM logits tensor (batch × seq × 152k vocab) within GPU memory budget.
+
+## Training Data
+
+- **Dataset:** [colab-potsdam/playpen-data](https://huggingface.co/datasets/colab-potsdam/playpen-data) — clembench 2.0 game instances (training split)
+- **Games:** All games present in the benchmark (wordle, taboo, reference, clean_up, and others)
+- **Filtering:** Turn-level transcripts where the final game outcome was a win
+- **Preprocessing:** Chat-templated using Qwen3.5 instruction template with `enable_thinking=False`
+
+## Hyperparameters
+
+| Parameter | Value |
+|---|---|
+| Learning rate | 2e-4 |
+| LR scheduler | Cosine with warmup |
+| Epochs trained | 1 (early stopping on val loss) |
+| Per-device batch size | 4 |
+| Effective batch size | 128 (auto grad-accum across GPUs) |
+| Max sequence length | 1024 tokens |
+| LoRA rank (r) | 16 |
+| LoRA alpha | 32 |
+| LoRA dropout | 0.05 |
+| Quantization | 4-bit NF4 (bitsandbytes) |
+| Compute dtype | bfloat16 |
+| Optimizer | paged_adamw_8bit |
+| Val loss (best checkpoint) | 0.2511 |
+| Val token accuracy | 94.0% |
+
+## Compute
+
+| Resource | Details |
+|---|---|
+| Hardware | 4× NVIDIA A40 (48 GB) |
+| Cluster | University of Michigan HPC (SLURM) |
+| Training time | ~1 epoch over the filtered positive corpus |
+| Total FLOPs | ~2.64 × 10¹⁸ |
+
+## Evaluation
+
+Evaluated on the [Playpen](https://github.com/lm-playpen/playpen) benchmark (clembench 2.0) using **clemscore** (quality-weighted success rate across all games) and **statscore** (static benchmark aggregate).
+
+When used with the companion PRM ([Diginyx/Qwen3.5-27B-prm-ep1](https://huggingface.co/Diginyx/Qwen3.5-27B-prm-ep1)) in a best-of-N or beam search guided inference setup, this model achieves higher clemscore than the greedy baseline.
+
+## Usage
+
+### Standalone (greedy inference)
+
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+import torch
+
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4",
+)
+base = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen3.5-27B",
+    quantization_config=bnb_config,
+    device_map="auto",
+)
+model = PeftModel.from_pretrained(base, "Diginyx/Qwen3.5-27B-sft-ep1")
+tokenizer = AutoTokenizer.from_pretrained("Diginyx/Qwen3.5-27B-sft-ep1")
+```
+
+### With PRM-guided inference (best-of-N)
+
+Install [Playpen](https://github.com/lm-playpen/playpen) and register the model in `model_registry.json`:
+
+```json
+{
+  "model_name": "Qwen3.5-27B-sft-ep1",
+  "backend": "huggingface_local",
+  "huggingface_id": "Qwen/Qwen3.5-27B",
+  "model_config": {
+    "premade_chat_template": true,
+    "load_in_4bit": true,
+    "chat_template_kwargs": {"enable_thinking": false},
+    "peft_model": "Diginyx/Qwen3.5-27B-sft-ep1"
+  }
+}
+```
+
+Then run:
+
+```bash
+python examples/trl/prm_eval.py \
+    --policy-model Qwen3.5-27B-sft-ep1 \
+    --prm-path Diginyx/Qwen3.5-27B-prm-ep1 \
+    --game-all \
+    --n-candidates 4 \
+    --temperature 0.7 \
+    --max-tokens 2048
+```
+
+### With beam search
+
+```bash
+python examples/trl/prm_eval.py \
+    --policy-model Qwen3.5-27B-sft-ep1 \
+    --prm-path Diginyx/Qwen3.5-27B-prm-ep1 \
+    --mode beam-search \
+    --n-candidates 4 \
+    --num-beam-iterations 20 \
+    --game-all \
+    --temperature 0.7 \
+    --max-tokens 2048
+```
+
+## Companion Models
+
+- **PRM:** [Diginyx/Qwen3.5-27B-prm-ep1](https://huggingface.co/Diginyx/Qwen3.5-27B-prm-ep1) — process reward model trained to score turn-level responses
+
+## Framework Versions
+
+- PEFT 0.19.1
+- TRL
+- Transformers
+- bitsandbytes

adapter_config.json

@@ -0,0 +1,53 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "/nfs/turbo/coe-chaijy-unreplicated/pre-trained-weights/Qwen3.5-27B",
+  "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": 0.05,
+  "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": [
+    "down_proj",
+    "v_proj",
+    "gate_proj",
+    "out_proj",
+    "in_proj_qkv",
+    "in_proj_a",
+    "o_proj",
+    "k_proj",
+    "in_proj_b",
+    "in_proj_z",
+    "up_proj",
+    "q_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 sha256:5d4e49569151a69d696e4fc03cf3b9c601e657e93d9f92b64c232c1f9b64e42b
+size 233592584

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 false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- else %}
+        {{- '<think>\n' }}
+    {%- endif %}
+{%- endif %}

tokenizer.json

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+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|>"
+}