Instructions to use ysn-rfd/Refact-1_6B-fim-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ysn-rfd/Refact-1_6B-fim-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ysn-rfd/Refact-1_6B-fim-GGUF")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ysn-rfd/Refact-1_6B-fim-GGUF", dtype="auto") - llama-cpp-python
How to use ysn-rfd/Refact-1_6B-fim-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="ysn-rfd/Refact-1_6B-fim-GGUF", filename="refact-1_6b-fim-q4_k_m.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use ysn-rfd/Refact-1_6B-fim-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 ysn-rfd/Refact-1_6B-fim-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf ysn-rfd/Refact-1_6B-fim-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 ysn-rfd/Refact-1_6B-fim-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf ysn-rfd/Refact-1_6B-fim-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 ysn-rfd/Refact-1_6B-fim-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf ysn-rfd/Refact-1_6B-fim-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 ysn-rfd/Refact-1_6B-fim-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf ysn-rfd/Refact-1_6B-fim-GGUF:Q4_K_M
Use Docker
docker model run hf.co/ysn-rfd/Refact-1_6B-fim-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use ysn-rfd/Refact-1_6B-fim-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ysn-rfd/Refact-1_6B-fim-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ysn-rfd/Refact-1_6B-fim-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ysn-rfd/Refact-1_6B-fim-GGUF:Q4_K_M
- SGLang
How to use ysn-rfd/Refact-1_6B-fim-GGUF with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "ysn-rfd/Refact-1_6B-fim-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ysn-rfd/Refact-1_6B-fim-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "ysn-rfd/Refact-1_6B-fim-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ysn-rfd/Refact-1_6B-fim-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use ysn-rfd/Refact-1_6B-fim-GGUF with Ollama:
ollama run hf.co/ysn-rfd/Refact-1_6B-fim-GGUF:Q4_K_M
- Unsloth Studio
How to use ysn-rfd/Refact-1_6B-fim-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 ysn-rfd/Refact-1_6B-fim-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 ysn-rfd/Refact-1_6B-fim-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ysn-rfd/Refact-1_6B-fim-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use ysn-rfd/Refact-1_6B-fim-GGUF with Docker Model Runner:
docker model run hf.co/ysn-rfd/Refact-1_6B-fim-GGUF:Q4_K_M
- Lemonade
How to use ysn-rfd/Refact-1_6B-fim-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ysn-rfd/Refact-1_6B-fim-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Refact-1_6B-fim-GGUF-Q4_K_M
List all available models
lemonade list
Upload README.md with huggingface_hub
Browse files
README.md
ADDED
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|
| 1 |
+
---
|
| 2 |
+
base_model: smallcloudai/Refact-1_6B-fim
|
| 3 |
+
datasets:
|
| 4 |
+
- bigcode/the-stack-dedup
|
| 5 |
+
- rombodawg/2XUNCENSORED_MegaCodeTraining188k
|
| 6 |
+
- bigcode/commitpackft
|
| 7 |
+
language:
|
| 8 |
+
- en
|
| 9 |
+
library_name: transformers
|
| 10 |
+
license: bigscience-openrail-m
|
| 11 |
+
metrics:
|
| 12 |
+
- code_eval
|
| 13 |
+
pipeline_tag: text-generation
|
| 14 |
+
tags:
|
| 15 |
+
- code
|
| 16 |
+
- llama-cpp
|
| 17 |
+
- matrixportal
|
| 18 |
+
inference: true
|
| 19 |
+
widget:
|
| 20 |
+
- text: 'def print_hello_world():'
|
| 21 |
+
example_title: Hello world
|
| 22 |
+
group: Python
|
| 23 |
+
pretrain-datasets:
|
| 24 |
+
- books
|
| 25 |
+
- arxiv
|
| 26 |
+
- c4
|
| 27 |
+
- falcon-refinedweb
|
| 28 |
+
- wiki
|
| 29 |
+
- github-issues
|
| 30 |
+
- stack_markdown
|
| 31 |
+
- self-made dataset of permissive github code
|
| 32 |
+
model-index:
|
| 33 |
+
- name: Refact-1.6B
|
| 34 |
+
results:
|
| 35 |
+
- task:
|
| 36 |
+
type: text-generation
|
| 37 |
+
dataset:
|
| 38 |
+
name: HumanEval
|
| 39 |
+
type: openai_humaneval
|
| 40 |
+
metrics:
|
| 41 |
+
- type: pass@1
|
| 42 |
+
value: 32.0
|
| 43 |
+
name: pass@1 (T=0.01)
|
| 44 |
+
verified: false
|
| 45 |
+
- type: pass@1
|
| 46 |
+
value: 31.5
|
| 47 |
+
name: pass@1 (T=0.2)
|
| 48 |
+
verified: false
|
| 49 |
+
- type: pass@10
|
| 50 |
+
value: 53.0
|
| 51 |
+
name: pass@10 (T=0.8)
|
| 52 |
+
verified: false
|
| 53 |
+
- type: pass@100
|
| 54 |
+
value: 76.9
|
| 55 |
+
name: pass@100 (T=0.8)
|
| 56 |
+
verified: false
|
| 57 |
+
- task:
|
| 58 |
+
type: text-generation
|
| 59 |
+
dataset:
|
| 60 |
+
name: HumanEvalSynthesize Python
|
| 61 |
+
type: bigcode/humanevalpack
|
| 62 |
+
metrics:
|
| 63 |
+
- type: pass@1
|
| 64 |
+
value: 35.8
|
| 65 |
+
name: pass@1 (T=0.2)
|
| 66 |
+
verified: false
|
| 67 |
+
- type: pass@1
|
| 68 |
+
value: 31.6
|
| 69 |
+
name: pass@1 (T=0.2)
|
| 70 |
+
verified: false
|
| 71 |
+
- type: pass@1
|
| 72 |
+
value: 29.1
|
| 73 |
+
name: pass@1 (T=0.2)
|
| 74 |
+
verified: false
|
| 75 |
+
- type: pass@1
|
| 76 |
+
value: -1
|
| 77 |
+
name: pass@1 (T=0.2)
|
| 78 |
+
verified: false
|
| 79 |
+
- type: pass@1
|
| 80 |
+
value: 26.3
|
| 81 |
+
name: pass@1 (T=0.2)
|
| 82 |
+
verified: false
|
| 83 |
+
- type: pass@1
|
| 84 |
+
value: -1
|
| 85 |
+
name: pass@1 (T=0.2)
|
| 86 |
+
verified: false
|
| 87 |
+
- type: pass@1
|
| 88 |
+
value: -1
|
| 89 |
+
name: pass@1 (T=0.2)
|
| 90 |
+
verified: false
|
| 91 |
+
- type: pass@1
|
| 92 |
+
value: 18.38
|
| 93 |
+
name: pass@1 (T=0.2)
|
| 94 |
+
verified: false
|
| 95 |
+
- type: pass@1
|
| 96 |
+
value: 12.28
|
| 97 |
+
name: pass@1 (T=0.2)
|
| 98 |
+
verified: false
|
| 99 |
+
- type: pass@1
|
| 100 |
+
value: 15.12
|
| 101 |
+
name: pass@1 (T=0.2)
|
| 102 |
+
verified: false
|
| 103 |
+
- type: pass@1
|
| 104 |
+
value: -1
|
| 105 |
+
name: pass@1 (T=0.2)
|
| 106 |
+
verified: false
|
| 107 |
+
- type: pass@1
|
| 108 |
+
value: 13.17
|
| 109 |
+
name: pass@1 (T=0.2)
|
| 110 |
+
verified: false
|
| 111 |
+
- type: pass@1
|
| 112 |
+
value: 2.8
|
| 113 |
+
name: pass@1 (T=0.2)
|
| 114 |
+
verified: false
|
| 115 |
+
- type: pass@1
|
| 116 |
+
value: -1
|
| 117 |
+
name: pass@1 (T=0.2)
|
| 118 |
+
verified: false
|
| 119 |
+
- type: pass@1
|
| 120 |
+
value: 26.92
|
| 121 |
+
name: pass@1 (T=0.2)
|
| 122 |
+
verified: false
|
| 123 |
+
- type: pass@1
|
| 124 |
+
value: 26.85
|
| 125 |
+
name: pass@1 (T=0.2)
|
| 126 |
+
verified: false
|
| 127 |
+
- type: pass@1
|
| 128 |
+
value: 30.76
|
| 129 |
+
name: pass@1 (T=0.2)
|
| 130 |
+
verified: false
|
| 131 |
+
- type: pass@1
|
| 132 |
+
value: -1
|
| 133 |
+
name: pass@1 (T=0.2)
|
| 134 |
+
verified: false
|
| 135 |
+
- type: pass@1
|
| 136 |
+
value: 25.94
|
| 137 |
+
name: pass@1 (T=0.2)
|
| 138 |
+
verified: false
|
| 139 |
+
- type: pass@1
|
| 140 |
+
value: 8.44
|
| 141 |
+
name: pass@1 (T=0.2)
|
| 142 |
+
verified: false
|
| 143 |
+
- type: pass@1
|
| 144 |
+
value: -1
|
| 145 |
+
name: pass@1 (T=0.2)
|
| 146 |
+
verified: false
|
| 147 |
+
- type: pass@1
|
| 148 |
+
value: 26.46
|
| 149 |
+
name: pass@1 (T=0.2)
|
| 150 |
+
verified: false
|
| 151 |
+
- type: pass@1
|
| 152 |
+
value: 17.86
|
| 153 |
+
name: pass@1 (T=0.2)
|
| 154 |
+
verified: false
|
| 155 |
+
- type: pass@1
|
| 156 |
+
value: 20.94
|
| 157 |
+
name: pass@1 (T=0.2)
|
| 158 |
+
verified: false
|
| 159 |
+
- type: pass@1
|
| 160 |
+
value: -1
|
| 161 |
+
name: pass@1 (T=0.2)
|
| 162 |
+
verified: false
|
| 163 |
+
- type: pass@1
|
| 164 |
+
value: 18.78
|
| 165 |
+
name: pass@1 (T=0.2)
|
| 166 |
+
verified: false
|
| 167 |
+
- type: pass@1
|
| 168 |
+
value: -1
|
| 169 |
+
name: pass@1 (T=0.2)
|
| 170 |
+
verified: false
|
| 171 |
+
- type: pass@1
|
| 172 |
+
value: -1
|
| 173 |
+
name: pass@1 (T=0.2)
|
| 174 |
+
verified: false
|
| 175 |
+
- task:
|
| 176 |
+
type: text-generation
|
| 177 |
+
dataset:
|
| 178 |
+
name: MBPP
|
| 179 |
+
type: mbpp
|
| 180 |
+
metrics:
|
| 181 |
+
- type: pass@1
|
| 182 |
+
value: 31.15
|
| 183 |
+
name: pass@1 (T=0.01)
|
| 184 |
+
verified: false
|
| 185 |
+
- task:
|
| 186 |
+
type: text-generation
|
| 187 |
+
dataset:
|
| 188 |
+
name: DS-1000 (Overall Completion)
|
| 189 |
+
type: ds1000
|
| 190 |
+
metrics:
|
| 191 |
+
- type: pass@1
|
| 192 |
+
value: 10.1
|
| 193 |
+
name: pass@1 (T=0.2)
|
| 194 |
+
verified: false
|
| 195 |
+
- task:
|
| 196 |
+
type: text-generation
|
| 197 |
+
dataset:
|
| 198 |
+
name: MultiPL-HumanEval (C++)
|
| 199 |
+
type: nuprl/MultiPL-E
|
| 200 |
+
metrics:
|
| 201 |
+
- type: pass@1
|
| 202 |
+
value: 21.61
|
| 203 |
+
name: pass@1 (T=0.2)
|
| 204 |
+
verified: false
|
| 205 |
+
- type: pass@1
|
| 206 |
+
value: 13.91
|
| 207 |
+
name: pass@1 (T=0.2)
|
| 208 |
+
verified: false
|
| 209 |
+
- type: pass@1
|
| 210 |
+
value: 9.5
|
| 211 |
+
name: pass@1 (T=0.2)
|
| 212 |
+
verified: false
|
| 213 |
+
- type: pass@1
|
| 214 |
+
value: 53.57
|
| 215 |
+
name: pass@1 (T=0.2)
|
| 216 |
+
verified: false
|
| 217 |
+
- type: pass@1
|
| 218 |
+
value: 21.58
|
| 219 |
+
name: pass@1 (T=0.2)
|
| 220 |
+
verified: false
|
| 221 |
+
- type: pass@1
|
| 222 |
+
value: 13.75
|
| 223 |
+
name: pass@1 (T=0.2)
|
| 224 |
+
verified: false
|
| 225 |
+
- type: pass@1
|
| 226 |
+
value: 26.88
|
| 227 |
+
name: pass@1 (T=0.2)
|
| 228 |
+
verified: false
|
| 229 |
+
- type: pass@1
|
| 230 |
+
value: 15.26
|
| 231 |
+
name: pass@1 (T=0.2)
|
| 232 |
+
verified: false
|
| 233 |
+
- type: pass@1
|
| 234 |
+
value: 23.04
|
| 235 |
+
name: pass@1 (T=0.2)
|
| 236 |
+
verified: false
|
| 237 |
+
- type: pass@1
|
| 238 |
+
value: 12.1
|
| 239 |
+
name: pass@1 (T=0.2)
|
| 240 |
+
verified: false
|
| 241 |
+
- type: pass@1
|
| 242 |
+
value: 29.6
|
| 243 |
+
name: pass@1 (T=0.2)
|
| 244 |
+
verified: false
|
| 245 |
+
- type: pass@1
|
| 246 |
+
value: 13.77
|
| 247 |
+
name: pass@1 (T=0.2)
|
| 248 |
+
verified: false
|
| 249 |
+
- type: pass@1
|
| 250 |
+
value: 12.68
|
| 251 |
+
name: pass@1 (T=0.2)
|
| 252 |
+
verified: false
|
| 253 |
+
- type: pass@1
|
| 254 |
+
value: 4.29
|
| 255 |
+
name: pass@1 (T=0.2)
|
| 256 |
+
verified: false
|
| 257 |
+
- type: pass@1
|
| 258 |
+
value: 19.54
|
| 259 |
+
name: pass@1 (T=0.2)
|
| 260 |
+
verified: false
|
| 261 |
+
- type: pass@1
|
| 262 |
+
value: 18.33
|
| 263 |
+
name: pass@1 (T=0.2)
|
| 264 |
+
verified: false
|
| 265 |
+
- type: pass@1
|
| 266 |
+
value: 5.7
|
| 267 |
+
name: pass@1 (T=0.2)
|
| 268 |
+
verified: false
|
| 269 |
+
- type: pass@1
|
| 270 |
+
value: 17.68
|
| 271 |
+
name: pass@1 (T=0.2)
|
| 272 |
+
verified: false
|
| 273 |
+
- type: pass@1
|
| 274 |
+
value: 25
|
| 275 |
+
name: pass@1 (T=0.2)
|
| 276 |
+
verified: false
|
| 277 |
+
---
|
| 278 |
+
|
| 279 |
+
# ysn-rfd/Refact-1_6B-fim-GGUF
|
| 280 |
+
This model was converted to GGUF format from [`smallcloudai/Refact-1_6B-fim`](https://huggingface.co/smallcloudai/Refact-1_6B-fim) using llama.cpp via the ggml.ai's [all-gguf-same-where](https://huggingface.co/spaces/matrixportal/all-gguf-same-where) space.
|
| 281 |
+
Refer to the [original model card](https://huggingface.co/smallcloudai/Refact-1_6B-fim) for more details on the model.
|
| 282 |
+
|
| 283 |
+
## ✅ Quantized Models Download List
|
| 284 |
+
|
| 285 |
+
### 🔍 Recommended Quantizations
|
| 286 |
+
- **✨ General CPU Use:** [`Q4_K_M`](https://huggingface.co/ysn-rfd/Refact-1_6B-fim-GGUF/resolve/main/refact-1_6b-fim-q4_k_m.gguf) (Best balance of speed/quality)
|
| 287 |
+
- **📱 ARM Devices:** [`Q4_0`](https://huggingface.co/ysn-rfd/Refact-1_6B-fim-GGUF/resolve/main/refact-1_6b-fim-q4_0.gguf) (Optimized for ARM CPUs)
|
| 288 |
+
- **🏆 Maximum Quality:** [`Q8_0`](https://huggingface.co/ysn-rfd/Refact-1_6B-fim-GGUF/resolve/main/refact-1_6b-fim-q8_0.gguf) (Near-original quality)
|
| 289 |
+
|
| 290 |
+
### 📦 Full Quantization Options
|
| 291 |
+
| 🚀 Download | 🔢 Type | 📝 Notes |
|
| 292 |
+
|:---------|:-----|:------|
|
| 293 |
+
| [Download](https://huggingface.co/ysn-rfd/Refact-1_6B-fim-GGUF/resolve/main/refact-1_6b-fim-q2_k.gguf) |  | Basic quantization |
|
| 294 |
+
| [Download](https://huggingface.co/ysn-rfd/Refact-1_6B-fim-GGUF/resolve/main/refact-1_6b-fim-q3_k_s.gguf) |  | Small size |
|
| 295 |
+
| [Download](https://huggingface.co/ysn-rfd/Refact-1_6B-fim-GGUF/resolve/main/refact-1_6b-fim-q3_k_m.gguf) |  | Balanced quality |
|
| 296 |
+
| [Download](https://huggingface.co/ysn-rfd/Refact-1_6B-fim-GGUF/resolve/main/refact-1_6b-fim-q3_k_l.gguf) |  | Better quality |
|
| 297 |
+
| [Download](https://huggingface.co/ysn-rfd/Refact-1_6B-fim-GGUF/resolve/main/refact-1_6b-fim-q4_0.gguf) |  | Fast on ARM |
|
| 298 |
+
| [Download](https://huggingface.co/ysn-rfd/Refact-1_6B-fim-GGUF/resolve/main/refact-1_6b-fim-q4_k_s.gguf) |  | Fast, recommended |
|
| 299 |
+
| [Download](https://huggingface.co/ysn-rfd/Refact-1_6B-fim-GGUF/resolve/main/refact-1_6b-fim-q4_k_m.gguf) |  ⭐ | Best balance |
|
| 300 |
+
| [Download](https://huggingface.co/ysn-rfd/Refact-1_6B-fim-GGUF/resolve/main/refact-1_6b-fim-q5_0.gguf) |  | Good quality |
|
| 301 |
+
| [Download](https://huggingface.co/ysn-rfd/Refact-1_6B-fim-GGUF/resolve/main/refact-1_6b-fim-q5_k_s.gguf) |  | Balanced |
|
| 302 |
+
| [Download](https://huggingface.co/ysn-rfd/Refact-1_6B-fim-GGUF/resolve/main/refact-1_6b-fim-q5_k_m.gguf) |  | High quality |
|
| 303 |
+
| [Download](https://huggingface.co/ysn-rfd/Refact-1_6B-fim-GGUF/resolve/main/refact-1_6b-fim-q6_k.gguf) |  🏆 | Very good quality |
|
| 304 |
+
| [Download](https://huggingface.co/ysn-rfd/Refact-1_6B-fim-GGUF/resolve/main/refact-1_6b-fim-q8_0.gguf) |  ⚡ | Fast, best quality |
|
| 305 |
+
| [Download](https://huggingface.co/ysn-rfd/Refact-1_6B-fim-GGUF/resolve/main/refact-1_6b-fim-f16.gguf) |  | Maximum accuracy |
|
| 306 |
+
|
| 307 |
+
💡 **Tip:** Use `F16` for maximum precision when quality is critical
|
| 308 |
+
|
| 309 |
+
|
| 310 |
+
---
|
| 311 |
+
# 🚀 Applications and Tools for Locally Quantized LLMs
|
| 312 |
+
## 🖥️ Desktop Applications
|
| 313 |
+
|
| 314 |
+
| Application | Description | Download Link |
|
| 315 |
+
|-----------------|----------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------|
|
| 316 |
+
| **Llama.cpp** | A fast and efficient inference engine for GGUF models. | [GitHub Repository](https://github.com/ggml-org/llama.cpp) |
|
| 317 |
+
| **Ollama** | A streamlined solution for running LLMs locally. | [Website](https://ollama.com/) |
|
| 318 |
+
| **AnythingLLM** | An AI-powered knowledge management tool. | [GitHub Repository](https://github.com/Mintplex-Labs/anything-llm) |
|
| 319 |
+
| **Open WebUI** | A user-friendly web interface for running local LLMs. | [GitHub Repository](https://github.com/open-webui/open-webui) |
|
| 320 |
+
| **GPT4All** | A user-friendly desktop application supporting various LLMs, compatible with GGUF models. | [GitHub Repository](https://github.com/nomic-ai/gpt4all) |
|
| 321 |
+
| **LM Studio** | A desktop application designed to run and manage local LLMs, supporting GGUF format. | [Website](https://lmstudio.ai/) |
|
| 322 |
+
| **GPT4All Chat**| A chat application compatible with GGUF models for local, offline interactions. | [GitHub Repository](https://github.com/nomic-ai/gpt4all) |
|
| 323 |
+
|
| 324 |
+
---
|
| 325 |
+
|
| 326 |
+
## 📱 Mobile Applications
|
| 327 |
+
|
| 328 |
+
| Application | Description | Download Link |
|
| 329 |
+
|-------------------|----------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------|
|
| 330 |
+
| **ChatterUI** | A simple and lightweight LLM app for mobile devices. | [GitHub Repository](https://github.com/Vali-98/ChatterUI) |
|
| 331 |
+
| **Maid** | Mobile Artificial Intelligence Distribution for running AI models on mobile devices. | [GitHub Repository](https://github.com/Mobile-Artificial-Intelligence/maid) |
|
| 332 |
+
| **PocketPal AI** | A mobile AI assistant powered by local models. | [GitHub Repository](https://github.com/a-ghorbani/pocketpal-ai) |
|
| 333 |
+
| **Layla** | A flexible platform for running various AI models on mobile devices. | [Website](https://www.layla-network.ai/) |
|
| 334 |
+
|
| 335 |
+
---
|
| 336 |
+
|
| 337 |
+
## 🎨 Image Generation Applications
|
| 338 |
+
|
| 339 |
+
| Application | Description | Download Link |
|
| 340 |
+
|-------------------------------------|----------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------|
|
| 341 |
+
| **Stable Diffusion** | An open-source AI model for generating images from text. | [GitHub Repository](https://github.com/CompVis/stable-diffusion) |
|
| 342 |
+
| **Stable Diffusion WebUI** | A web application providing access to Stable Diffusion models via a browser interface. | [GitHub Repository](https://github.com/AUTOMATIC1111/stable-diffusion-webui) |
|
| 343 |
+
| **Local Dream** | Android Stable Diffusion with Snapdragon NPU acceleration. Also supports CPU inference. | [GitHub Repository](https://github.com/xororz/local-dream) |
|
| 344 |
+
| **Stable-Diffusion-Android (SDAI)** | An open-source AI art application for Android devices, enabling digital art creation. | [GitHub Repository](https://github.com/ShiftHackZ/Stable-Diffusion-Android) |
|
| 345 |
+
|
| 346 |
+
---
|
| 347 |
+
|