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skylenage-ai/SkyJM-Gen-9B
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The model replies with JSON only:
json
{ "eval_points": [ {"question": "Is the bus yellow?", "dimension": "Prompt Alignment", "score": 1} ], "dimension_summary": { "Prompt Alignment": "...", "Aesthetics": "...", "Technical Quality": "..." }, "score_reason": "...", "final_score": 3.5}eval_points[].dimension is exactly one of Prompt Alignment, Aesthetics,
Technical Quality.eval_points[].score is 0 or 1.final_score is on the 0–10 scale (decimals allowed); typical checklist is
8–14 points, weighted toward Prompt Alignment by construction.Reward for RL = final_score / 10 in [0, 1].
The full request/response contract (system prompt, user template, failure modes)
is shipped as reward_protocol/ inside this repository, and mirrored in the
accompanying code release (Flow-Factory reward adapter for this judge); the
system prompt used at training time is reproduced verbatim there.
bash
# ms-swift + vLLM (recommended: this checkpoint uses Qwen3.5's linear-attention# layers, and ms-swift's deploy path is the one it was validated with)CUDA_VISIBLE_DEVICES=0,1 python -m swift.cli.deploy \ --model skylenage-ai/SkyJM-Gen-9B-pointwise \ --infer_backend vllm --served_model_name stage6-judge \ --host 0.0.0.0 --port 8000 \ --vllm_tensor_parallel_size 2 --vllm_gpu_memory_utilization 0.85 \ --vllm_max_model_len 16384 --vllm_max_num_seqs 64 \ --vllm_limit_mm_per_prompt '{"image": 1}'python
from openai import OpenAI client = OpenAI(base_url="http://127.0.0.1:8000/v1", api_key="EMPTY")reply = client.chat.completions.create( model="stage6-judge", messages=[ {"role": "system", "content": SYSTEM_PROMPT}, {"role": "user", "content": [ {"type": "image_url", "image_url": {"url": data_url}}, {"type": "text", "text": USER_TEMPLATE.format(instruction=prompt)}, ]}, ], temperature=0.0, max_tokens=4096, # NOTE: do NOT set frequency_penalty / presence_penalty — see Limitations.)This checkpoint was used as the reward model for a GRPO fine-tune of SD3.5-medium on GenEval prompts, through Flow-Factory:
yaml
rewards: - name: "stage6_judge" reward_model: "stage6_judge_t2i" # reward_protocol/overlay adapter api_base_url: "http://127.0.0.1:8000/v1" vlm_model: "stage6-judge" max_tokens: 4096 temperature: 0.0 frequency_penalty: 0.0 image_max_pixels: 1048576 fallback_reward: 0.0 async_reward: true num_workers: 4Starting from the stage-6 SFT checkpoint of the RubricRM pipeline
(SkyJM-Gen-9B lineage), fine-tuned to emit the fixed 3-dimension pointwise
checklist above:
| Item | Value |
|---|---|
| Backbone | Qwen3.5-9B VL (same as SkyJM-Gen-9B) |
| Adapter | LoRA, r=32, alpha=64, attention + MLP projections (vision tower frozen) |
| Training data | prompt + single generated image + checklist labels with 0/1 point scores and a 0–10 final_score |
| Context | system prompt + user text + image + JSON reply (<= ~2.5k completion tokens) |
| Released checkpoint | stage-6 SFT checkpoint-250, merged into full weights (best of 250/500/552, see below) |
checkpoint-250The three saved checkpoints of the stage-6 run were evaluated with the same
protocol (pointwise scoring -> pairwise comparison, temperature=0, tie gap
0.25):
| checkpoint | GenAI-Bench (image_generation, 1305 pairs) | MMRB2 (t2i, 1000 pairs) | mean |
|---|---|---|---|
| 250 | 0.6100 (no-tie 0.7032, tie 0.1326) | 0.5210 (no-tie 0.6570, tie 0.2070) | 0.5655 |
| 500 | 0.5893 (no-tie 0.6947, tie 0.1517) | 0.5180 (no-tie 0.6607, tie 0.2160) | 0.5536 |
| 552 | 0.5904 (no-tie 0.6971, tie 0.1531) | 0.5210 (no-tie 0.6688, tie 0.2210) | 0.5557 |
checkpoint-250 also has the lowest tie rate, and it is the checkpoint the
downstream RL recipes and benchmark reruns standardised on.
Reproduce the ranking with sd35_rl/scripts/pick_best_stage6_ckpt.py.
frequency_penalty=1.2
the model penalises JSON syntax tokens and aborts mid-JSON
(finish_reason="stop", no final_score). Keep it at 0 and control runaway
checklists with a length cap instead.final_score.temperature=0 when the checklist changes; pin temperature=0 and
average over repeats if you need stability.bibtex
@misc{skyjm_gen_9b_pointwise, title = {SkyJM-Gen-9B-pointwise: a pointwise checklist judge for text-to-image generation}, year = {2026}}See also: SkyJM-Gen-9B, RubricRM-Data.
{ "eval_points": [ {"question": "Is the bus yellow?", "dimension": "Prompt Alignment", "score": 1} ], "dimension_summary": { "Prompt Alignment": "...", "Aesthetics": "...", "Technical Quality": "..." }, "score_reason": "...", "final_score": 3.5}# ms-swift + vLLM (recommended: this checkpoint uses Qwen3.5's linear-attention# layers, and ms-swift's deploy path is the one it was validated with)CUDA_VISIBLE_DEVICES=0,1 python -m swift.cli.deploy \ --model skylenage-ai/SkyJM-Gen-9B-pointwise \ --infer_backend vllm --served_model_name stage6-judge \ --host 0.0.0.0 --port 8000 \ --vllm_tensor_parallel_size 2 --vllm_gpu_memory_utilization 0.85 \ --vllm_max_model_len 16384 --vllm_max_num_seqs 64 \ --vllm_limit_mm_per_prompt '{"image": 1}'from openai import OpenAI client = OpenAI(base_url="http://127.0.0.1:8000/v1", api_key="EMPTY")reply = client.chat.completions.create( model="stage6-judge", messages=[ {"role": "system", "content": SYSTEM_PROMPT}, {"role": "user", "content": [ {"type": "image_url", "image_url": {"url": data_url}}, {"type": "text", "text": USER_TEMPLATE.format(instruction=prompt)}, ]}, ], temperature=0.0, max_tokens=4096, # NOTE: do NOT set frequency_penalty / presence_penalty — see Limitations.)rewards: - name: "stage6_judge" reward_model: "stage6_judge_t2i" # reward_protocol/overlay adapter api_base_url: "http://127.0.0.1:8000/v1" vlm_model: "stage6-judge" max_tokens: 4096 temperature: 0.0 frequency_penalty: 0.0 image_max_pixels: 1048576 fallback_reward: 0.0 async_reward: true num_workers: 4@misc{skyjm_gen_9b_pointwise, title = {SkyJM-Gen-9B-pointwise: a pointwise checklist judge for text-to-image generation}, year = {2026}}