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ps4mas-sft-x5-single-ep3
yinita/ps4mas-sft-x5-single-ep3
ps4mas-sft-x5-single-ep3 is a text generation model from yinita. Use it when you need the model to write or continue text. It is set up for transformers.
Full fine-tune of Qwen/Qwen3.5-9B for PS4MAS (Personalized Safety for Multi-Agent Systems).
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9B
17.9 GB on disk
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.safetensors17.9 GB · 100%
At a glance
- Task
- Text Generation
- Library
- transformers
- Model type
- qwen3_5_text
- Access
- Public
- Created
- Jul 18, 2026
- Updated
- Jul 18, 2026
- SHA
- 364df57b
Try a prompt
Base models
Model card
From the Hugging Face model README
PS4MAS SFT full-FT — x5 single ep3 (best single / protocol)
Full fine-tune of Qwen/Qwen3.5-9B for PS4MAS (Personalized Safety for Multi-Agent Systems).
- Local run:
results/0617/sft_full_9b_claude_x5_single_ep3 - HF repo:
yinita/ps4mas-sft-x5-single-ep3 - Teacher: Claude Sonnet (x5 Claude distillation data)
- Eval:
tiny_eval— 50 scenarios × 4 cold topologies (200 traces), judge = GPT-OSS-120B
tiny_eval results
| Model | Method | Overall | Single | Central | Hier | Debate | avg_ask | zero_tool% |
|---|---|---|---|---|---|---|---|---|
| baseline_9b (Qwen3.5-9B) | raw | 3.386 | 2.140 | 3.613 | 3.508 | 4.281 | 1.74 | 90.0 |
| x5_mixed_ep1 (this / sibling) | SFT full-FT | 3.679 | 2.387 | 4.101 | 3.821 | 4.407 | 1.95 | 48.0 |
| x5_mixed_ep3 (bottom-line) | SFT full-FT | 3.622 | 2.267 | 3.974 | 3.801 | 4.448 | 1.93 | 58.0 |
| x5_single_ep3 | SFT full-FT | 3.646 | 2.600 | 3.821 | 3.881 | 4.281 | 2.13 | 6.0 |
| ceod_gate_lr_sft_v1 | SFT LoRA | 3.602 | 2.326 | 4.094 | 3.747 | 4.241 | 1.89 | 58.0 |
| low400_ep3 (weights not on disk) | SFT full-FT | 3.682 | 2.546 | 3.854 | 3.860 | 4.467 | 1.86 | 66.0 |
Why this checkpoint
Best PS-cold-single among SFT full-FT (2.600) and dramatically lower single zero_tool rate (6% vs ~48–58% for mixed). Trained on single-topology Claude x5 data (3 epochs).
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
repo = "yinita/ps4mas-sft-x5-single-ep3"
model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(repo)
Citation / project
PS4MAS — Personalized Safety Multi-Agent Systems (internal research checkpoint).
- Task
- Text Generation
- Library
- transformers
- Type
- qwen3_5_text
- Created
- Jul 18, 2026
- Updated
- Jul 18, 2026