Instructions to use Ex0bit/Step-3.5-Flash-PRISM-PRO-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ex0bit/Step-3.5-Flash-PRISM-PRO-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Ex0bit/Step-3.5-Flash-PRISM-PRO-GGUF",
	filename="Step-3.5-Flash-PRISM-PRO-IQ4_NL.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Ex0bit/Step-3.5-Flash-PRISM-PRO-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Ex0bit/Step-3.5-Flash-PRISM-PRO-GGUF:IQ4_NL
# Run inference directly in the terminal:
llama-cli -hf Ex0bit/Step-3.5-Flash-PRISM-PRO-GGUF:IQ4_NL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Ex0bit/Step-3.5-Flash-PRISM-PRO-GGUF:IQ4_NL
# Run inference directly in the terminal:
llama-cli -hf Ex0bit/Step-3.5-Flash-PRISM-PRO-GGUF:IQ4_NL

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Ex0bit/Step-3.5-Flash-PRISM-PRO-GGUF:IQ4_NL
# Run inference directly in the terminal:
./llama-cli -hf Ex0bit/Step-3.5-Flash-PRISM-PRO-GGUF:IQ4_NL

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Ex0bit/Step-3.5-Flash-PRISM-PRO-GGUF:IQ4_NL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Ex0bit/Step-3.5-Flash-PRISM-PRO-GGUF:IQ4_NL

Use Docker

docker model run hf.co/Ex0bit/Step-3.5-Flash-PRISM-PRO-GGUF:IQ4_NL

LM Studio
Jan

vLLM

How to use Ex0bit/Step-3.5-Flash-PRISM-PRO-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ex0bit/Step-3.5-Flash-PRISM-PRO-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ex0bit/Step-3.5-Flash-PRISM-PRO-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Ex0bit/Step-3.5-Flash-PRISM-PRO-GGUF:IQ4_NL

Ollama
How to use Ex0bit/Step-3.5-Flash-PRISM-PRO-GGUF with Ollama:
```
ollama run hf.co/Ex0bit/Step-3.5-Flash-PRISM-PRO-GGUF:IQ4_NL
```

Unsloth Studio new

How to use Ex0bit/Step-3.5-Flash-PRISM-PRO-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Ex0bit/Step-3.5-Flash-PRISM-PRO-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Ex0bit/Step-3.5-Flash-PRISM-PRO-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Ex0bit/Step-3.5-Flash-PRISM-PRO-GGUF to start chatting

Pi new

How to use Ex0bit/Step-3.5-Flash-PRISM-PRO-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Ex0bit/Step-3.5-Flash-PRISM-PRO-GGUF:IQ4_NL

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Ex0bit/Step-3.5-Flash-PRISM-PRO-GGUF:IQ4_NL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Ex0bit/Step-3.5-Flash-PRISM-PRO-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Ex0bit/Step-3.5-Flash-PRISM-PRO-GGUF:IQ4_NL

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Ex0bit/Step-3.5-Flash-PRISM-PRO-GGUF:IQ4_NL

Run Hermes

hermes

Docker Model Runner
How to use Ex0bit/Step-3.5-Flash-PRISM-PRO-GGUF with Docker Model Runner:
```
docker model run hf.co/Ex0bit/Step-3.5-Flash-PRISM-PRO-GGUF:IQ4_NL
```

Lemonade

How to use Ex0bit/Step-3.5-Flash-PRISM-PRO-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Ex0bit/Step-3.5-Flash-PRISM-PRO-GGUF:IQ4_NL

Run and chat with the model

lemonade run user.Step-3.5-Flash-PRISM-PRO-GGUF-IQ4_NL

List all available models

lemonade list

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Step-3.5-Flash-PRISM-PRO-GGUF

AVAILABLE TO PRISM VIP MEMEBERS ONLY: https://ko-fi.com/summary/1adf92d9-84f3-45c7-8136-8ab0ca39090d

GGUF quantization of Step-3.5-Flash-PRISM-PRO for local deployment with llama.cpp and compatible runtimes.

Full Production unrestricted/unchained PRISM-PRO version of StepFun's Step 3.5 Flash intended particularly for full over-refusal and propaganda mechanisms removal with the SOTA PRISM-PRO pipeline.

For Full Custom Production PRISM versions & tensors reach out.

Free PRISM-Lite version available here: https://hf.co/Ex0bit/Step-3.5-Flash-PRISM

Full precision safetensors: https://hf.co/Ex0bit/Step-3.5-Flash-PRISM-PRO

Support Our Work

If you enjoy our work and find it useful, please consider sponsoring or supporting us!

Option	Description
PRISM VIP Membership	Access to all PRISM models
Bitcoin	`bc1qarq2pyn4psjpcxzp2ghgwaq6y2h4e53q232x8r`

Quantization Details

File	Quantization	Size
`Step-3.5-Flash-PRISM-PRO-IQ4_NL.gguf`	IQ4_NL	~111.5 GB

Quick Start (llama.cpp)

./llama-cli -m Step-3.5-Flash-PRISM-PRO-IQ4_NL.gguf --jinja

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)

Hardware Requirements

Setup	Details
GGUF INT4 (This Repo)	~120 GB unified memory (Mac Studio M4 Max 128GB, DGX Spark, AMD Ryzen AI Max+ 395)

Recommended Parameters

Use Case	Temperature	Top-P	Max New Tokens
Agentic / Reasoning / Coding	1.0	0.95	32768
General Chat	0.6	0.95	4096

License

This model is released under the PRISM Research License.

Acknowledgments

Based on Step 3.5 Flash by StepFun AI. See the technical report and blog post for more details on the base model.

Downloads last month: 1,122

GGUF

Model size

197B params

Architecture

step35

Hardware compatibility

4-bit

Model tree for Ex0bit/Step-3.5-Flash-PRISM-PRO-GGUF

Base model

stepfun-ai/Step-3.5-Flash

Finetuned

Ex0bit/Step-3.5-Flash-PRISM-PRO

Quantized

(1)

this model

Collection including Ex0bit/Step-3.5-Flash-PRISM-PRO-GGUF

Step 3.5 PRISM

Collection

PRISM abliterated StepFun Step 3.5 Flash MoE — safetensors, GGUF, NVFP4. • 4 items • Updated Mar 5