Add the step-110 adapter and its 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 +1,135 @@
+---
+license: apache-2.0
+base_model: Qwen/Qwen3.5-35B-A3B
+library_name: peft
+tags:
+- grpo
+- trl
+- reinforcement-learning
+- rl-environment
+- openenv
+- p5js
+- generative-art
+- lora
+datasets:
+- HuggingEnvs/watercolour-reference-pool
+- HuggingEnvs/watercolour-rollouts-hps-led
+---
+
+# watercolour-grpo-hps-led
+
+A LoRA adapter for `Qwen/Qwen3.5-35B-A3B`, trained with GRPO to paint watercolours by
+writing [p5.brush](https://github.com/acamposuribe/p5.brush) sketches. This is the
+middle point of the project's three reward mixes: the generic aesthetic preference model
+(HPSv3) holds most of the weight, and the pairwise judge, the term that carries the
+hand-rated reference pool, holds the rest.
+
+## Loading it, because the obvious way fails silently
+
+`Qwen3.5-35B-A3B` declares `Qwen3_5MoeForConditionalGeneration` and carries a vision tower,
+so its layers live at `model.language_model.layers`. `AutoModelForCausalLM` resolves to the
+**text-only** variant, whose layers sit at `model.layers`, and 700 of the adapter's 920
+tensors then fail to match. PEFT reports that as a `UserWarning`, not an error, so you get
+the base model back and nothing tells you.
+
+```python
+from transformers import Qwen3_5MoeForConditionalGeneration, AutoTokenizer
+from peft import PeftModel
+
+base = Qwen3_5MoeForConditionalGeneration.from_pretrained(
+    "Qwen/Qwen3.5-35B-A3B", dtype="bfloat16", device_map="auto"
+)
+model = PeftModel.from_pretrained(base, "HuggingEnvs/watercolour-grpo-hps-led")
+tok = AutoTokenizer.from_pretrained("Qwen/Qwen3.5-35B-A3B")
+```
+
+If you see `Found missing adapter keys while loading the checkpoint`, the adapter did not
+load and you are running the base model.
+
+## Training
+
+110 steps in 32h15m on one H200. Launched for 200 steps; the job ran a few steps past 110
+before it was cancelled, and this adapter is the step-110 checkpoint, the last one saved.
+The published rollouts dataset is trimmed to the same 110 steps so the numbers match.
+
+| | |
+|---|---|
+| reward | `gate` 0.05 + `length` 0.05 + pairwise judge **0.30** + [HPSv3](https://huggingface.co/MizzenAI/HPSv3) **0.60** |
+| learning rate | 5e-5, `constant_with_warmup`, 5 warmup steps |
+| `scale_rewards` | `none` |
+| LoRA | `all-linear`, r16, alpha 32. 30,431,360 trainable, 0.0866% |
+| batch | 8 generations per step, `per_device_batch_size` 1, `grad_accum` 8 |
+| sampling | `top_p` 0.95, `top_k` 20, `max_completion_length` 8192 |
+
+```bash
+WATERCOLOUR_JUDGE_WEIGHT=0.30 WATERCOLOUR_QUALITY_WEIGHT=0.60 \
+hf jobs uv run examples/watercolour_grpo.py --flavor h200 --timeout 96h --secrets HF_TOKEN -- \
+  --env-url https://YOURORG-watercolour-env.hf.space \
+  --model Qwen/Qwen3.5-35B-A3B --lora --all-linear --bf16 --gradient-checkpointing \
+  --subject 'a peach hibiscus' --references 4 \
+  --top-p 0.95 --top-k 20 \
+  --lr 5e-5 --lr-scheduler constant_with_warmup --warmup-steps 5 \
+  --scale-rewards none \
+  --steps 200 --num-generations 8 \
+  --per-device-batch-size 1 --gradient-accumulation-steps 8 \
+  --max-completion-length 8192 --probe-samples 40 --film
+```
+
+The `uv` header pins no versions (`trl`, `peft`, `transformers`, `torch`), so a run today
+will resolve different ones. That is a real reproducibility gap, stated rather than hidden.
+
+## Results
+
+| | first third | second | third | slope t |
+|---|---|---|---|---|
+| reward | 0.573 | 0.740 | **0.815** | **+15.6** |
+| pairwise judge term | 0.43 | | **0.83** | |
+| HPSv3 term | 0.63 | | 0.82 | |
+| paint coverage | 0.128 | | **0.298** | |
+
+Absolute rewards are not comparable across reward mixes: each run optimises a different
+blend. This was the smoothest climb of the three runs, still inching upward when it was
+stopped (+0.0023/step over the last 30 steps).
+
+Best group mean 0.885, at step 72, and the best single rollout of the run is 0.91. The
+pairwise judge term climbed from 0.43 to 0.83 even at 0.30 weight, and paint coverage
+more than doubled, from 0.128 to 0.298, where the judge-free `hps-only` run barely moved
+it.
+
+The base model's probe before training: reward 0.464, judge term 0.285, paint coverage
+0.083, over 40 samples. Every training number here is recomputable from
+[`watercolour-rollouts-hps-led`](https://huggingface.co/datasets/HuggingEnvs/watercolour-rollouts-hps-led).
+
+## Siblings
+
+| run | judge | HPSv3 |
+|---|---|---|
+| `judge-led` | 0.60 | 0.30 |
+| **`hps-led`** | 0.30 | 0.60 |
+| `hps-only` | 0.00 | 0.90 |
+
+## Limitations
+
+- One subject, `a peach hibiscus`, and one library. It does not generalise to other drawing tasks.
+- The pairwise judge is the noisiest reward term, and its consistency (scoring the same
+  image twice) has not been tested.
+- Reproducing it needs an H200, an a100-large Space for HPSv3, a cpu-upgrade Space for the
+  environment and inference quota for the judge. It is not cheap.
+
+Method reproduced from [Surya Narreddi's "RL'ing Qwen to paint with
+code"](https://surya.website/rling-qwen-to-paint-with-code). Internally this run is `v22c`,
+HF job `6a95468c0718b0f6d890881b`, adapter at revision `39f9fa0f32` of the training repo.
+
+## Where this comes from
+
+Part of **[Paint with Code](https://huggingface.co/collections/HuggingEnvs/paint-with-code-6a955b79d63f67f1631d9be6)**, a complete recipe: the environment, the pool
+that defines the reward, the trainer, the curves and every rollout.
+
+| | |
+|---|---|
+| the recipe, and how to reproduce it | [`02-watercolour/`](https://github.com/adithya-s-k/HuggingEnvs/tree/main/02-watercolour) |
+| the environment | [`envs/watercolour/`](https://github.com/adithya-s-k/HuggingEnvs/tree/main/02-watercolour/envs/watercolour) |
+| the trainer | [`train/watercolour_grpo.py`](https://github.com/adithya-s-k/HuggingEnvs/tree/main/02-watercolour/train/watercolour_grpo.py) |
+| the reference pool | [`watercolour-reference-pool`](https://huggingface.co/datasets/HuggingEnvs/watercolour-reference-pool) |
+| the trained adapter | [`watercolour-grpo-hps-led`](https://huggingface.co/HuggingEnvs/watercolour-grpo-hps-led) |
+| every rollout | [`watercolour-rollouts-hps-led`](https://huggingface.co/datasets/HuggingEnvs/watercolour-rollouts-hps-led) |

adapter_config.json

@@ -0,0 +1,60 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "Qwen/Qwen3.5-35B-A3B",
+  "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,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "in_proj_z",
+    "qkv",
+    "out_proj",
+    "in_proj_qkv",
+    "attn.proj",
+    "shared_expert_gate",
+    "up_proj",
+    "in_proj_b",
+    "in_proj_a",
+    "linear_fc1",
+    "linear_fc2",
+    "gate_proj",
+    "k_proj",
+    "q_proj",
+    "o_proj",
+    "v_proj",
+    "down_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}

adapter_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f03670ece100fc679b27c0edf63b491b3d400d3550dad1bbf7d1c2e0f9d1b216
+size 121864672

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 %}

processor_config.json

@@ -0,0 +1,61 @@
+{
+  "image_processor": {
+    "do_convert_rgb": true,
+    "do_normalize": true,
+    "do_rescale": true,
+    "do_resize": true,
+    "image_mean": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "image_processor_type": "Qwen2VLImageProcessor",
+    "image_std": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "merge_size": 2,
+    "patch_size": 16,
+    "resample": 3,
+    "rescale_factor": 0.00392156862745098,
+    "size": {
+      "longest_edge": 16777216,
+      "shortest_edge": 65536
+    },
+    "temporal_patch_size": 2
+  },
+  "processor_class": "Qwen3VLProcessor",
+  "video_processor": {
+    "do_convert_rgb": true,
+    "do_normalize": true,
+    "do_rescale": true,
+    "do_resize": true,
+    "do_sample_frames": true,
+    "fps": 2,
+    "image_mean": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "image_std": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "max_frames": 768,
+    "max_video_tokens": 768,
+    "merge_size": 2,
+    "min_frames": 4,
+    "patch_size": 16,
+    "resample": 3,
+    "rescale_factor": 0.00392156862745098,
+    "return_metadata": false,
+    "size": {
+      "longest_edge": 25165824,
+      "shortest_edge": 4096
+    },
+    "temporal_patch_size": 2,
+    "video_processor_type": "Qwen3VLVideoProcessor"
+  }
+}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:06b9509352d2af50381ab2247e083b80d32d5c0aba91c272ca9ff729b6a0e523
+size 19989325

tokenizer_config.json

@@ -0,0 +1,35 @@
+{
+  "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": false,
+  "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|>",
+  "padding_side": "left",
+  "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+",
+  "processor_class": "Qwen3VLProcessor",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "truncation_side": "left",
+  "unk_token": null,
+  "video_token": "<|video_pad|>",
+  "vision_bos_token": "<|vision_start|>",
+  "vision_eos_token": "<|vision_end|>"
+}

training_args.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b8b98774ab0511b372d2d40d75150b9ec2a7bbb85c7af1768a3146d851dc919a
+size 7697