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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use QuantPasture/Step-3.5-Flash-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantPasture/Step-3.5-Flash-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantPasture/Step-3.5-Flash-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantPasture/Step-3.5-Flash-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantPasture/Step-3.5-Flash-GGUF:Q4_K_M

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 QuantPasture/Step-3.5-Flash-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantPasture/Step-3.5-Flash-GGUF:Q4_K_M

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 QuantPasture/Step-3.5-Flash-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantPasture/Step-3.5-Flash-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantPasture/Step-3.5-Flash-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantPasture/Step-3.5-Flash-GGUF with Ollama:
```
ollama run hf.co/QuantPasture/Step-3.5-Flash-GGUF:Q4_K_M
```
Unsloth Desktop

How to use QuantPasture/Step-3.5-Flash-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QuantPasture/Step-3.5-Flash-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "QuantPasture/Step-3.5-Flash-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use QuantPasture/Step-3.5-Flash-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantPasture/Step-3.5-Flash-GGUF:Q4_K_M
```

Lemonade

How to use QuantPasture/Step-3.5-Flash-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantPasture/Step-3.5-Flash-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Step-3.5-Flash-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use QuantPasture/Step-3.5-Flash-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QuantPasture/Step-3.5-Flash-GGUF:Q4_K_M

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 QuantPasture/Step-3.5-Flash-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use QuantPasture/Step-3.5-Flash-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QuantPasture/Step-3.5-Flash-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "QuantPasture/Step-3.5-Flash-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Some benchmarks for IQ4_XS

by tarruda - opened Mar 3

Discussion

tarruda

Mar 3

Strangely some of these seem higher than the official results. I've asked a question about it here: https://huggingface.co/stepfun-ai/Step-3.5-Flash-Base-Midtrain/discussions/5

|                 Tasks                 |Version|Filter|n-shot|Metric|   |Value |   |Stderr|
|---------------------------------------|------:|------|-----:|------|---|-----:|---|-----:|
|mmlu                                   |      2|none  |      |acc   |   |0.8238|±  |0.0031|
| - humanities                          |      2|none  |     0|acc   |↑  |0.7543|±  |0.0060|
|  - formal_logic                       |      1|none  |     0|acc   |↑  |0.7063|±  |0.0407|
|  - high_school_european_history       |      1|none  |     0|acc   |↑  |0.8788|±  |0.0255|
|  - high_school_us_history             |      1|none  |     0|acc   |↑  |0.9314|±  |0.0177|
|  - high_school_world_history          |      1|none  |     0|acc   |↑  |0.9283|±  |0.0168|
|  - international_law                  |      1|none  |     0|acc   |↑  |0.9008|±  |0.0273|
|  - jurisprudence                      |      1|none  |     0|acc   |↑  |0.8796|±  |0.0315|
|  - logical_fallacies                  |      1|none  |     0|acc   |↑  |0.8466|±  |0.0283|
|  - moral_disputes                     |      1|none  |     0|acc   |↑  |0.8295|±  |0.0202|
|  - moral_scenarios                    |      1|none  |     0|acc   |↑  |0.6011|±  |0.0164|
|  - philosophy                         |      1|none  |     0|acc   |↑  |0.8778|±  |0.0186|
|  - prehistory                         |      1|none  |     0|acc   |↑  |0.8889|±  |0.0175|
|  - professional_law                   |      1|none  |     0|acc   |↑  |0.6656|±  |0.0120|
|  - world_religions                    |      1|none  |     0|acc   |↑  |0.9123|±  |0.0217|
| - other                               |      2|none  |     0|acc   |↑  |0.8626|±  |0.0060|
|  - business_ethics                    |      1|none  |     0|acc   |↑  |0.8100|±  |0.0394|
|  - clinical_knowledge                 |      1|none  |     0|acc   |↑  |0.8943|±  |0.0189|
|  - college_medicine                   |      1|none  |     0|acc   |↑  |0.7919|±  |0.0310|
|  - global_facts                       |      1|none  |     0|acc   |↑  |0.6900|±  |0.0465|
|  - human_aging                        |      1|none  |     0|acc   |↑  |0.8251|±  |0.0255|
|  - management                         |      1|none  |     0|acc   |↑  |0.8641|±  |0.0339|
|  - marketing                          |      1|none  |     0|acc   |↑  |0.9573|±  |0.0133|
|  - medical_genetics                   |      1|none  |     0|acc   |↑  |0.8900|±  |0.0314|
|  - miscellaneous                      |      1|none  |     0|acc   |↑  |0.9298|±  |0.0091|
|  - nutrition                          |      1|none  |     0|acc   |↑  |0.9052|±  |0.0168|
|  - professional_accounting            |      1|none  |     0|acc   |↑  |0.7979|±  |0.0240|
|  - professional_medicine              |      1|none  |     0|acc   |↑  |0.8897|±  |0.0190|
|  - virology                           |      1|none  |     0|acc   |↑  |0.5904|±  |0.0383|
| - social sciences                     |      2|none  |     0|acc   |↑  |0.9012|±  |0.0053|
|  - econometrics                       |      1|none  |     0|acc   |↑  |0.7544|±  |0.0405|
|  - high_school_geography              |      1|none  |     0|acc   |↑  |0.9242|±  |0.0189|
|  - high_school_government_and_politics|      1|none  |     0|acc   |↑  |0.9793|±  |0.0103|
|  - high_school_macroeconomics         |      1|none  |     0|acc   |↑  |0.8923|±  |0.0157|
|  - high_school_microeconomics         |      1|none  |     0|acc   |↑  |0.9328|±  |0.0163|
|  - high_school_psychology             |      1|none  |     0|acc   |↑  |0.9541|±  |0.0090|
|  - human_sexuality                    |      1|none  |     0|acc   |↑  |0.8855|±  |0.0279|
|  - professional_psychology            |      1|none  |     0|acc   |↑  |0.8709|±  |0.0136|
|  - public_relations                   |      1|none  |     0|acc   |↑  |0.8182|±  |0.0369|
|  - security_studies                   |      1|none  |     0|acc   |↑  |0.8490|±  |0.0229|
|  - sociology                          |      1|none  |     0|acc   |↑  |0.9055|±  |0.0207|
|  - us_foreign_policy                  |      1|none  |     0|acc   |↑  |0.9600|±  |0.0197|
| - stem                                |      2|none  |     0|acc   |↑  |0.8138|±  |0.0067|
|  - abstract_algebra                   |      1|none  |     0|acc   |↑  |0.7000|±  |0.0461|
|  - anatomy                            |      1|none  |     0|acc   |↑  |0.8444|±  |0.0313|
|  - astronomy                          |      1|none  |     0|acc   |↑  |0.9211|±  |0.0219|
|  - college_biology                    |      1|none  |     0|acc   |↑  |0.9514|±  |0.0180|
|  - college_chemistry                  |      1|none  |     0|acc   |↑  |0.6000|±  |0.0492|
|  - college_computer_science           |      1|none  |     0|acc   |↑  |0.8300|±  |0.0378|
|  - college_mathematics                |      1|none  |     0|acc   |↑  |0.6600|±  |0.0476|
|  - college_physics                    |      1|none  |     0|acc   |↑  |0.7549|±  |0.0428|
|  - computer_security                  |      1|none  |     0|acc   |↑  |0.8200|±  |0.0386|
|  - conceptual_physics                 |      1|none  |     0|acc   |↑  |0.8766|±  |0.0215|
|  - electrical_engineering             |      1|none  |     0|acc   |↑  |0.8345|±  |0.0310|
|  - elementary_mathematics             |      1|none  |     0|acc   |↑  |0.8836|±  |0.0165|
|  - high_school_biology                |      1|none  |     0|acc   |↑  |0.9258|±  |0.0149|
|  - high_school_chemistry              |      1|none  |     0|acc   |↑  |0.8079|±  |0.0277|
|  - high_school_computer_science       |      1|none  |     0|acc   |↑  |0.8900|±  |0.0314|
|  - high_school_mathematics            |      1|none  |     0|acc   |↑  |0.6481|±  |0.0291|
|  - high_school_physics                |      1|none  |     0|acc   |↑  |0.7550|±  |0.0351|
|  - high_school_statistics             |      1|none  |     0|acc   |↑  |0.8333|±  |0.0254|
|  - machine_learning                   |      1|none  |     0|acc   |↑  |0.5982|±  |0.0465|

|      Groups      |Version|Filter|n-shot|Metric|   |Value |   |Stderr|
|------------------|------:|------|-----:|------|---|-----:|---|-----:|
|mmlu              |      2|none  |      |acc   |   |0.8238|±  |0.0031|
| - humanities     |      2|none  |     0|acc   |↑  |0.7543|±  |0.0060|
| - other          |      2|none  |     0|acc   |↑  |0.8626|±  |0.0060|
| - social sciences|      2|none  |     0|acc   |↑  |0.9012|±  |0.0053|
| - stem           |      2|none  |     0|acc   |↑  |0.8138|±  |0.0067|

|        Tasks        |Version|Filter|n-shot| Metric |   |Value|   |Stderr|
|---------------------|------:|------|-----:|--------|---|----:|---|-----:|
|gpqa_diamond_zeroshot|      1|none  |     0|acc     |↑  |0.399|±  |0.0349|
|                     |       |none  |     0|acc_norm|↑  |0.399|±  |0.0349|

|          Tasks          |Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-------------------------|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gpqa_diamond_cot_zeroshot|      1|flexible-extract|     0|exact_match|↑  |0.7525|±  |0.0307|
|                         |       |strict-match    |     0|exact_match|↑  |0.6667|±  |0.0336|

|  Tasks  |Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|---------|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k_cot|      3|flexible-extract|     0|exact_match|↑  |0.9310|±  |0.0070|
|         |       |strict-match    |     0|exact_match|↑  |0.8567|±  |0.0097|

AesSedai

Aes Sedai's Quant Pasture org Mar 3

Interesting, I would not have expected that but neat to see.

tarruda

Mar 3

Yea this quant is really amazing. I can run on my 128G mac with 131072 context and swa-full without swapping. It is the best local model I have for using with coding agents.

My mind has also been blown by @ubergarm 's 2-bit quant of Qwen3.5-397b: https://huggingface.co/ubergarm/Qwen3.5-397B-A17B-GGUF/discussions/8

ilintar

Mar 4

This is not strange, this is consistent with my uses of IQ4_XS of this model under llama.cpp.

I strongly feel that the Transformers / vLLM implementation is buggy somehow. The OpenRouter API version reportedly has looping reasoning bugs all the time, yet I have never encountered looping reasoning running this model regularly daily.

eleius

Mar 20

I noticed IQ4_XS has 4.8225 ppl, which is almost double of ubergarm's IQ4_XS (2.5181 ppl), although attn_ and ffn_ seem to use the same precision... any idea why?

ilintar

Mar 20

@eleius you can only compare perplexity scores that are calculated on the same reference dataset.

BingoBird

Apr 23

•

edited Jun 26

[Edit] This comment was offtopic and I apologize. [wasn't false though]

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