Image-Text-to-Text
GGUF
llama.cpp
quantized
imatrix
Mixture of Experts
agent
tool-calling
reasoning
vision
multimodal
conversational
Instructions to use stepfun-ai/Step-3.7-Flash-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use stepfun-ai/Step-3.7-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 stepfun-ai/Step-3.7-Flash-GGUF:Q4_K_S # Run inference directly in the terminal: llama cli -hf stepfun-ai/Step-3.7-Flash-GGUF:Q4_K_S
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf stepfun-ai/Step-3.7-Flash-GGUF:Q4_K_S # Run inference directly in the terminal: llama cli -hf stepfun-ai/Step-3.7-Flash-GGUF:Q4_K_S
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 stepfun-ai/Step-3.7-Flash-GGUF:Q4_K_S # Run inference directly in the terminal: ./llama-cli -hf stepfun-ai/Step-3.7-Flash-GGUF:Q4_K_S
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 stepfun-ai/Step-3.7-Flash-GGUF:Q4_K_S # Run inference directly in the terminal: ./build/bin/llama-cli -hf stepfun-ai/Step-3.7-Flash-GGUF:Q4_K_S
Use Docker
docker model run hf.co/stepfun-ai/Step-3.7-Flash-GGUF:Q4_K_S
- LM Studio
- Jan
- vLLM
How to use stepfun-ai/Step-3.7-Flash-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "stepfun-ai/Step-3.7-Flash-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": "stepfun-ai/Step-3.7-Flash-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/stepfun-ai/Step-3.7-Flash-GGUF:Q4_K_S
- Ollama
How to use stepfun-ai/Step-3.7-Flash-GGUF with Ollama:
ollama run hf.co/stepfun-ai/Step-3.7-Flash-GGUF:Q4_K_S
- Unsloth Desktop
- Pi
How to use stepfun-ai/Step-3.7-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 stepfun-ai/Step-3.7-Flash-GGUF:Q4_K_S
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": "stepfun-ai/Step-3.7-Flash-GGUF:Q4_K_S" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use stepfun-ai/Step-3.7-Flash-GGUF with Docker Model Runner:
docker model run hf.co/stepfun-ai/Step-3.7-Flash-GGUF:Q4_K_S
- Lemonade
How to use stepfun-ai/Step-3.7-Flash-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull stepfun-ai/Step-3.7-Flash-GGUF:Q4_K_S
Run and chat with the model
lemonade run user.Step-3.7-Flash-GGUF-Q4_K_S
List all available models
lemonade list
- Hermes Agent
How to use stepfun-ai/Step-3.7-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 stepfun-ai/Step-3.7-Flash-GGUF:Q4_K_S
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 stepfun-ai/Step-3.7-Flash-GGUF:Q4_K_S
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use stepfun-ai/Step-3.7-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 stepfun-ai/Step-3.7-Flash-GGUF:Q4_K_S
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 "stepfun-ai/Step-3.7-Flash-GGUF:Q4_K_S" \ --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"
File size: 15,062 Bytes
aa9b3d5 509968f aa9b3d5 509968f 5cee970 31e0043 509968f f6b350e 509968f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 | ---
license: apache-2.0
base_model: stepfun-ai/Step-3.7-Flash
base_model_relation: quantized
pipeline_tag: image-text-to-text
library_name: gguf
tags:
- gguf
- llama.cpp
- quantized
- imatrix
- moe
- agent
- tool-calling
- reasoning
- vision
- multimodal
language:
- en
- zh
- ja
- ko
- ar
- hi
- de
- fr
- es
- ru
---
**[ModelPage]**: https://static.stepfun.com/blog/step-3.7-flash/
## 1. Introduction
GGUF quantizations of [`stepfun-ai/Step-3.7-Flash`](https://huggingface.co/stepfun-ai/Step-3.7-Flash).
Step-3.7-Flash is a 198B-parameter sparse Mixture-of-Experts vision-language model from StepFun-ai, activating ~11B parameters per token for up to 400 t/s throughput. It pairs a 196B-parameter language backbone with a 1.8B-parameter vision encoder for native image understanding, supports a 256K context window, and offers three selectable reasoning levels (low / medium / high) to balance speed, cost, and depth. Built for agentic workloads — tool calling, multi-step reasoning, code, and math — with native multilingual coverage.
A separate `mmproj` projector ships alongside the language quants for multimodal inference. With 128 GB of unified memory (Mac Studio, DGX Spark, Ryzen AI Max+ 395, etc.), you can privately host Step-3.7-Flash: Q4 quants and below run at full 256K context with high precision.
## 2. Files
| File | Quant | Size | Notes |
|---|---|---:|---|
| `Step-3.7-flash-BF16.gguf` | BF16 | 394 GB | Full-precision reference.|
| `Step-3.7-flash-Q8_0.gguf` | Q8_0 | 209 GB | Near-lossless. Does **not** use imatrix.|
| `Step-3.7-flash-Q4_K_S.gguf` | Q4_K_S | 112 GB | imatrix-calibrated. Balanced quality / size.|
| `Step-3.7-flash-IQ4_XS.gguf` | IQ4_XS | 105 GB | imatrix-calibrated. Slightly smaller than Q4_K_S, comparable quality. |
| `Step-3.7-flash-Q3_K_L.gguf` | Q3_K_L | 103 GB | imatrix-calibrated. Aggressive size reduction. |
| `Step-3.7-flash-Q3_K_M.gguf` | Q3_K_M | 94 GB | imatrix-calibrated. Use when you need to fit on a single 64-96 GB device; expect modest quality loss at low bit-widths. |
| `Step-3.7-flash-IQ3_XXS.gguf` | IQ3_XXS | 76 GB | imatrix-calibrated. Recommended only when memory is the primary constraint; offers the smallest footprint among the provided quantizations. |
| `mmproj-Step-3.7-flash-f16.gguf` | F16 | 4 GB | Vision projector. Pair with any of the language quants above for image input. |
## 3. Quickstart
Build llama.cpp and run:
```bash
# 1. Clone and build
git clone https://github.com/stepfun-ai/llama.cpp.git
cd llama.cpp
git checkout -b step3.7 origin/step3.7
cmake -B build -DLLAMA_BUILD_TOOLS=ON -DLLAMA_BUILD_SERVER=ON
cmake --build build --config Release -j$(nproc)
# 2. Test performance (benchmark)
./build/bin/llama-batched-bench \
-m Step-3.7-flash-Q4_K_S.gguf \
-c 32768 -b 2048 -ub 2048 \
-npp 0,2048,8192,16384,32768 -ntg 128 -npl 1
# 3. Text-only inference
./build/bin/llama-cli \
-m Step-3.7-flash-Q4_K_S.gguf \
-c 32768 -ngl 99 -fa on \
-p "Write a Python function to compute the n-th Fibonacci number."
# 4. With vision (image + text)
./build/bin/llama-mtmd-cli \
-m Step-3.7-flash-Q4_K_S.gguf \
--mmproj mmproj-Step-3.7-flash-f16.gguf \
-c 32768 -ngl 99 -fa on \
--image path/to/image.jpg \
-p "Describe this image."
# 5. OpenAI-compatible server (text + vision)
./build/bin/llama-server \
-m Step-3.7-flash-Q4_K_S.gguf \
--mmproj mmproj-Step-3.7-flash-f16.gguf \
-c 32768 -ngl 99 -fa on \
--host 0.0.0.0 --port 8080
```
For full CLI / server options, see the [llama.cpp README](https://github.com/ggml-org/llama.cpp/blob/master/README.md).
## 4. Performance
### Apple Mac Studio (M4 max, 128 GB unified memory)
**Step-3.7-flash-Q4_K_S**
```
./llama-batched-bench -m Step-3.7-flash-Q4_K_S.gguf -c 262150 -b 2048 -ub 1024 -npp 0,2048,8192,16384,32768,65536,131072,262144 -ntg 128 -npl 1
```
| PP | TG | PL | N_KV | T_PP s | S_PP t/s | T_TG s | S_TG t/s | T s | S t/s |
|---:|---:|---:|-----:|-------:|---------:|-------:|---------:|----:|------:|
| 0 | 128 | 1 | 128 | 0.000 | 0.00 | 2.500 | 51.20 | 2.500 | 51.20 |
| 2048 | 128 | 1 | 2176 | 4.873 | 420.28 | 2.639 | 48.51 | 7.512 | 289.68 |
| 8192 | 128 | 1 | 8320 | 20.292 | 403.70 | 2.757 | 46.43 | 23.049 | 360.97 |
| 16384 | 128 | 1 | 16512 | 42.854 | 382.32 | 2.924 | 43.77 | 45.779 | 360.69 |
| 32768 | 128 | 1 | 32896 | 95.168 | 344.32 | 3.223 | 39.72 | 98.391 | 334.34 |
| 65536 | 128 | 1 | 65664 | 233.885 | 280.21 | 3.909 | 32.74 | 237.794 | 276.14 |
| 131072 | 128 | 1 | 131200 | 635.499 | 206.25 | 5.759 | 22.23 | 641.258 | 204.60 |
| 262144 | 128 | 1 | 262272 | 2362.488 | 110.96 | 13.188 | 9.71 | 2375.677 | 110.40 |
**Step-3.7-flash-IQ4_XS**
```
./llama-batched-bench -m Step-3.7-flash-IQ4_XS.gguf -c 262150 -b 2048 -ub 1024 -npp 0,2048,8192,16384,32768,65536,131072,262144 -ntg 128 -npl 1
```
| PP | TG | PL | N_KV | T_PP s | S_PP t/s | T_TG s | S_TG t/s | T s | S t/s |
|---:|---:|---:|-----:|-------:|---------:|-------:|---------:|----:|------:|
| 0 | 128 | 1 | 128 | 0.000 | 0.00 | 2.582 | 49.58 | 2.582 | 49.58 |
| 2048 | 128 | 1 | 2176 | 4.835 | 423.56 | 2.679 | 47.78 | 7.514 | 289.60 |
| 8192 | 128 | 1 | 8320 | 19.954 | 410.55 | 2.803 | 45.66 | 22.757 | 365.60 |
| 16384 | 128 | 1 | 16512 | 42.142 | 388.78 | 2.957 | 43.29 | 45.098 | 366.13 |
| 32768 | 128 | 1 | 32896 | 93.489 | 350.50 | 3.288 | 38.93 | 96.777 | 339.91 |
| 65536 | 128 | 1 | 65664 | 227.088 | 288.59 | 3.945 | 32.44 | 231.033 | 284.22 |
| 131072 | 128 | 1 | 131200 | 635.047 | 206.40 | 5.791 | 22.10 | 640.838 | 204.73 |
| 262144 | 128 | 1 | 262272 | 2170.271 | 120.79 | 13.070 | 9.79 | 2183.342 | 120.12 |
**Step-3.7-flash-Q3_K_L**
```
./llama-batched-bench -m Step-3.7-flash-Q3_K_L.gguf -c 262272 -b 2048 -ub 1024 -npp 0,2048,8192,16384,32768,65536,131072,262144 -ntg 128 -npl 1
```
| PP | TG | B | N_KV | T_PP s | S_PP t/s | T_TG s | S_TG t/s | T s | S t/s |
|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
| 0 | 128 | 1 | 128 | 0.000 | 0.00 | 3.590 | 35.66 | 3.590 | 35.66 |
| 2048 | 128 | 1 | 2176 | 5.263 | 389.15 | 3.702 | 34.57 | 8.965 | 242.72 |
| 8192 | 128 | 1 | 8320 | 21.789 | 375.97 | 3.817 | 33.53 | 25.606 | 324.92 |
| 16384 | 128 | 1 | 16512 | 45.819 | 357.58 | 3.977 | 32.18 | 49.796 | 331.59 |
| 32768 | 128 | 1 | 32896 | 100.827 | 324.99 | 4.308 | 29.71 | 105.135 | 312.89 |
| 65536 | 128 | 1 | 65664 | 242.172 | 270.62 | 4.977 | 25.72 | 247.149 | 265.69 |
|131072 | 128 | 1 | 131200 | 659.645 | 198.70 | 6.764 | 18.92 | 666.409 | 196.88 |
|262144 | 128 | 1 | 262272 | 2200.370 | 119.14 | 14.008 | 9.14 | 2214.378 | 118.44 |
### NVIDIA DGX Spark (GB10, 128 GB unified memory)
**Step-3.7-flash-Q4_K_S**
```
./llama-batched-bench -m Step-3.7-flash-Q4_K_S.gguf -c 131300 -b 2048 -ub 1024 -npp 0,2048,8192,16384,32768,65536,131072 -ntg 128 -npl 1
```
| PP | TG | B | N_KV | T_PP s | S_PP t/s | T_TG s | S_TG t/s | T s | S t/s |
|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
| 0 | 128 | 1 | 128 | 0.000 | 0.00 | 5.157 | 24.82 | 5.157 | 24.82 |
| 2048 | 128 | 1 | 2176 | 8.021 | 255.33 | 4.907 | 26.08 | 12.929 | 168.31 |
| 8192 | 128 | 1 | 8320 | 10.866 | 753.89 | 5.169 | 24.76 | 16.035 | 518.86 |
| 16384 | 128 | 1 | 16512 | 29.389 | 557.49 | 6.215 | 20.60 | 35.603 | 463.78 |
| 32768 | 128 | 1 | 32896 | 52.501 | 624.14 | 6.931 | 18.47 | 59.432 | 553.50 |
| 65536 | 128 | 1 | 65664 | 112.321 | 583.47 | 7.769 | 16.48 | 120.090 | 546.79 |
|131072 | 128 | 1 | 131200 | 281.479 | 465.66 | 9.834 | 13.02 | 291.313 | 450.37 |
**Step-3.7-flash-IQ4_XS**
```
./llama-batched-bench -m Step-3.7-flash-IQ4_XS.gguf -c 262272 -b 2048 -ub 1024 -npp 0,2048,8192,16384,32768,65536,131072,262144 -ntg 128 -npl 1
```
| PP | TG | PL | N_KV | T_PP s | S_PP t/s | T_TG s | S_TG t/s | T s | S t/s |
|---:|---:|---:|-----:|-------:|---------:|-------:|---------:|----:|------:|
| 0 | 128 | 1 | 128 | 0.000 | 0.00 | 5.368 | 23.85 | 5.368 | 23.85 |
| 2048 | 128 | 1 | 2176 | 4.250 | 481.87 | 5.311 | 24.10 | 9.561 | 227.58 |
| 8192 | 128 | 1 | 8320 | 12.531 | 653.73 | 5.817 | 22.01 | 18.348 | 453.46 |
| 16384 | 128 | 1 | 16512 | 24.474 | 669.44 | 5.915 | 21.64 | 30.389 | 543.35 |
| 32768 | 128 | 1 | 32896 | 51.976 | 630.44 | 6.531 | 19.60 | 58.508 | 562.25 |
| 65536 | 128 | 1 | 65664 | 116.305 | 563.48 | 7.934 | 16.13 | 124.239 | 528.53 |
| 131072 | 128 | 1 | 131200 | 298.746 | 438.74 | 10.263 | 12.47 | 309.009 | 424.58 |
| 262144 | 128 | 1 | 262272 | 924.872 | 283.44 | 14.862 | 8.61 | 939.734 | 279.09 |
**Step-3.7-flash-Q3_K_L**
```
./llama-batched-bench -m Step-3.7-flash-Q3_K_L.gguf -c 262272 -b 2048 -ub 1024 -npp 0,2048,8192,16384,32768,65536,131072,262144 -ntg 128 -npl 1
```
| PP | TG | PL | N_KV | T_PP s | S_PP t/s | T_TG s | S_TG t/s | T s | S t/s |
|---:|---:|---:|-----:|-------:|---------:|-------:|---------:|----:|------:|
| 0 | 128 | 1 | 128 | 0.000 | 0.00 | 5.947 | 21.52 | 5.947 | 21.52 |
| 2048 | 128 | 1 | 2176 | 4.145 | 494.08 | 5.623 | 22.76 | 9.768 | 222.77 |
| 8192 | 128 | 1 | 8320 | 14.889 | 550.20 | 5.799 | 22.07 | 20.688 | 402.17 |
| 16384 | 128 | 1 | 16512 | 29.374 | 557.78 | 6.140 | 20.85 | 35.513 | 464.95 |
| 32768 | 128 | 1 | 32896 | 54.957 | 596.25 | 6.744 | 18.98 | 61.702 | 533.15 |
| 65536 | 128 | 1 | 65664 | 129.827 | 504.79 | 8.347 | 15.33 | 138.174 | 475.23 |
| 131072 | 128 | 1 | 131200 | 315.402 | 415.57 | 10.780 | 11.87 | 326.182 | 402.23 |
| 262144 | 128 | 1 | 262272 | 910.215 | 288.00 | 15.568 | 8.22 | 925.783 | 283.30 |
### AMD Ryzen AI Max+ 395 (Strix Halo, 128 GB unified memory)
**Step-3.7-flash-Q4_K_S**
```
llama-batched-bench.exe -m Step-3.7-flash-Q4_K_S.gguf -c 65664 -b 2048 -ub 1024 -npp 0,2048,8192,16384,32768,65536 -ntg 128 -npl 1
```
| PP | TG | B | N_KV | T_PP s | S_PP t/s | T_TG s | S_TG t/s | T s | S t/s |
|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
| 0 | 128 | 1 | 128 | 0.000 | 0.00 | 4.878 | 26.24 | 4.878 | 26.24 |
| 2048 | 128 | 1 | 2176 | 9.367 | 218.63 | 5.134 | 24.93 | 14.501 | 150.06 |
| 8192 | 128 | 1 | 8320 | 43.540 | 188.15 | 5.508 | 23.24 | 49.048 | 169.63 |
| 16384 | 128 | 1 | 16512 | 111.814 | 146.53 | 5.947 | 21.53 | 117.761 | 140.22 |
| 32768 | 128 | 1 | 32896 | 357.819 | 91.58 | 6.779 | 18.88 | 364.598 | 90.23 |
| 65536 | 128 | 1 | 65664 | 1342.501 | 48.82 | 8.495 | 15.07 | 1350.996 | 48.60 |
**Step-3.7-flash-IQ4_XS**
```
./llama-batched-bench -m Step-3.7-flash-IQ4_XS.gguf -c 65664 -b 2048 -ub 1024 -npp 0,2048,8192,16384,32768,65536 -ntg 128 -npl 1
```
| PP | TG | B | N_KV | T_PP s | S_PP t/s | T_TG s | S_TG t/s | T s | S t/s |
|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
| 0 | 128 | 1 | 128 | 0.000 | 0.00 | 5.931 | 21.58 | 5.931 | 21.58 |
| 2048 | 128 | 1 | 2176 | 8.143 | 251.50 | 6.194 | 20.67 | 14.337 | 151.78 |
| 8192 | 128 | 1 | 8320 | 39.899 | 205.32 | 6.521 | 19.63 | 46.420 | 179.23 |
| 16384 | 128 | 1 | 16512 | 105.098 | 155.89 | 6.891 | 18.57 | 111.989 | 147.44 |
| 32768 | 128 | 1 | 32896 | 338.645 | 96.76 | 7.793 | 16.42 | 346.439 | 94.95 |
| 65536 | 128 | 1 | 65664 | 1310.820 | 50.00 | 9.489 | 13.49 | 1320.309 | 49.73 |
**Step-3.7-flash-Q3_K_L**
```
./llama-batched-bench -m Step-3.7-flash-Q3_K_L.gguf -c 262272 -b 2048 -ub 1024 -ctk q8_0 -ctv q8_0 -npp 0,2048,8192,16384,32768,65536,131072,262144 -ntg 128 -npl 1
```
| PP | TG | B | N_KV | T_PP s | S_PP t/s | T_TG s | S_TG t/s | T s | S t/s |
|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
| 0 | 128 | 1 | 128 | 0.000 | 0.00 | 5.015 | 25.53 | 5.015 | 25.53 |
| 2048 | 128 | 1 | 2176 | 10.246 | 199.88 | 5.073 | 25.23 | 15.319 | 142.04 |
| 8192 | 128 | 1 | 8320 | 37.229 | 220.05 | 5.341 | 23.96 | 42.570 | 195.44 |
| 16384 | 128 | 1 | 16512 | 79.234 | 206.78 | 5.489 | 23.32 | 84.723 | 194.89 |
| 32768 | 128 | 1 | 32896 | 179.697 | 182.35 | 5.810 | 22.03 | 185.507 | 177.33 |
| 65536 | 128 | 1 | 65664 | 436.593 | 150.11 | 6.577 | 19.46 | 443.169 | 148.17 |
|131072 | 128 | 1 | 131200 | 1262.377 | 103.83 | 9.124 | 14.03 | 1271.501 | 103.19 |
|262144 | 128 | 1 | 262272 | 3487.921 | 75.16 | 11.391 | 11.24 | 3499.312 | 74.95 |
## 5. Acknowledgments
This release stands on the work of the following authors and communities:
- **[bartowski](https://huggingface.co/bartowski)** — for [`calibration_datav5`](https://gist.github.com/bartowski1182/82ae9b520227f57d79ba04add13d0d0d),
the community-standard imatrix calibration anchor used by countless GGUF releases.
Used for calibration purposes only; no license has been verified for this resource.
- **[eaddario](https://huggingface.co/eaddario)** — for the [`imatrix-calibration`](https://huggingface.co/datasets/eaddario/imatrix-calibration)
dataset (MIT), providing multilingual / code / math splits that form the backbone of this release's domain balance
- **[NousResearch](https://huggingface.co/NousResearch)** — for [`hermes-function-calling-v1`](https://huggingface.co/datasets/NousResearch/hermes-function-calling-v1)
(Apache-2.0), used for agent / tool-call calibration coverage
- **[ggml-org / llama.cpp](https://github.com/ggml-org/llama.cpp)** — for the entire quantization and inference toolchain (MIT)
## 6. License
The GGUF quantization files in this repository are derivative works of
[`stepfun-ai/Step-3.7-Flash`](https://huggingface.co/stepfun-ai/Step-3.7-Flash)
and are released under the same **Apache 2.0** license.
| Component | License |
|---|---|
| Base model weights ([stepfun-ai/Step-3.7-Flash](https://huggingface.co/stepfun-ai/Step-3.7-Flash)) | Apache-2.0 |
| Calibration dataset ([eaddario/imatrix-calibration](https://huggingface.co/datasets/eaddario/imatrix-calibration)) | MIT |
| Calibration dataset ([NousResearch/hermes-function-calling-v1](https://huggingface.co/datasets/NousResearch/hermes-function-calling-v1)) | Apache-2.0 |
| Quantization toolchain ([llama.cpp](https://github.com/ggml-org/llama.cpp)) | MIT |
All calibration datasets retain their original licenses and are used strictly for quantization calibration purposes only.
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