--- license: other license_name: prism-research license_link: LICENSE.md language: - en - zh tags: - stepfun - prism - moe - reasoning - coding - agentic - abliterated pipeline_tag: text-generation library_name: transformers base_model: - stepfun-ai/Step-3.5-Flash base_model_relation: finetune --- [![Parameters](https://img.shields.io/badge/Parameters-196B_(11B_Active)-blue)]() [![Architecture](https://img.shields.io/badge/Architecture-MoE-green)]() [![Context](https://img.shields.io/badge/Context-256K-orange)]() [![MTP](https://img.shields.io/badge/MTP--3-350_tok%2Fs_Peak-purple)]()

# Step-3.5-Flash-PRISM A "role-play" following unrestricted/unchained PRISM-LITE version of [StepFun's Step 3.5 Flash](https://huggingface.co/stepfun-ai/Step-3.5-Flash) intended particularly for over-refusal and propaganda mechanisms suppression using our SOTA PRISM pipeline. For Full Custom Production PRISM versions & tensors reach out.
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--- ## Model Highlights - **PRISM Ablation** — State-of-the-art technique that removes over-refusal behaviors while preserving model capabilities - **196B MoE Architecture** — 196 billion total parameters with only 11 billion active per token across 288 fine-grained routed experts + 1 shared expert - **Multi-Token Prediction (MTP-3)** — Predicts 4 tokens simultaneously, achieving 100–300 tok/s typical throughput (peaking at 350 tok/s) - **256K Context Window** — Cost-efficient long context via 3:1 Sliding Window Attention (SWA) ratio - **Frontier Reasoning & Coding** — 97.3 on AIME 2025, 74.4% on SWE-bench Verified, 51.0% on Terminal-Bench 2.0 - **Accessible Local Deployment** — Runs on high-end consumer hardware (Mac Studio M4 Max, NVIDIA DGX Spark) ## Model Architecture | Specification | Value | |---------------|-------| | Architecture | Sparse Mixture-of-Experts (MoE) | | Backbone | 45-layer Transformer (4,096 hidden dim) | | Total Parameters | 196.81B (196B Backbone + 0.81B Head) | | Activated Parameters | ~11B (per token) | | Routed Experts per Layer | 288 | | Shared Experts | 1 (always active) | | Selected Experts per Token | Top-8 | | Vocabulary Size | 128,896 | | Context Length | 256K | | Attention | Hybrid SWA (3:1 SWA-to-Full ratio) | | MTP Head | Sliding-window attention + dense FFN (4 tokens/pass) | ## Benchmarks | Benchmark | Step 3.5 Flash | DeepSeek V3.2 | Kimi K2.5 | GLM-4.7 | MiniMax M2.1 | |-----------|---------------|---------------|-----------|---------|--------------| | **Agent** | | | | | | | τ²-Bench | 88.2 | 80.3 | 85.4 | 87.4 | 86.6 | | BrowseComp | 51.6 | 51.4 | 60.6 | 52.0 | 47.4 | | GAIA (no file) | 84.5 | 75.1 | 75.9 | 61.9 | 64.3 | | xbench-DeepSearch (2025.05) | 83.7 | 78.0 | 76.7 | 72.0 | 68.7 | | **Reasoning** | | | | | | | AIME 2025 | 97.3 | 93.1 | 96.1 | 95.7 | 83.0 | | HMMT 2025 (Feb.) | 98.4 | 92.5 | 95.4 | 97.1 | 71.0 | | IMOAnswerBench | 85.4 | 78.3 | 81.8 | 82.0 | 60.4 | | **Coding** | | | | | | | LiveCodeBench-V6 | 86.4 | 83.3 | 85.0 | 84.9 | — | | SWE-bench Verified | 74.4 | 73.1 | 76.8 | 73.8 | 74.0 | | Terminal-Bench 2.0 | 51.0 | 46.4 | 50.8 | 41.0 | 47.9 | ### llama.cpp (GGUF) For local deployment (requires ~120 GB VRAM for int4, smaller quants are available): ```bash ./llama-cli -m step3.5_flash_prism_Q4_K_S.gguf --jinja ``` ## Recommended Parameters | Use Case | Temperature | Top-P | Max New Tokens | |----------|-------------|-------|----------------| | Reasoning / Coding | 1.0 | 0.95 | 32768 | | General Chat | 0.6 | 0.95 | 4096 | ## Hardware Requirements | Setup | Details | |-------|---------| | **BF16 (Full)** | 8x H100/A100 80GB with tensor parallelism | | **FP8 Quantized** | 8x A100 80GB with expert parallelism | | **GGUF INT4 (Local)** | ~120 GB unified memory (Mac Studio M4 Max 128GB, DGX Spark, AMD Ryzen AI Max+ 395) | ## License This model is released under the [PRISM Research License](LICENSE.md). ## Acknowledgments Based on [Step 3.5 Flash](https://huggingface.co/stepfun-ai/Step-3.5-Flash) by [StepFun AI](https://www.stepfun.com). See the [technical report](https://github.com/stepfun-ai/Step-3.5-Flash/blob/main/step_3p5_flash_tech_report.pdf) and [blog post](https://static.stepfun.com/blog/step-3.5-flash/) for more details on the base model.