Instructions to use flyingfishinwater/good_and_small_models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use flyingfishinwater/good_and_small_models 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 flyingfishinwater/good_and_small_models:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf flyingfishinwater/good_and_small_models:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf flyingfishinwater/good_and_small_models:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf flyingfishinwater/good_and_small_models: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 flyingfishinwater/good_and_small_models:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf flyingfishinwater/good_and_small_models: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 flyingfishinwater/good_and_small_models:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf flyingfishinwater/good_and_small_models:Q4_K_M

Use Docker

docker model run hf.co/flyingfishinwater/good_and_small_models:Q4_K_M

LM Studio
Jan
Ollama
How to use flyingfishinwater/good_and_small_models with Ollama:
```
ollama run hf.co/flyingfishinwater/good_and_small_models:Q4_K_M
```

Unsloth Studio

How to use flyingfishinwater/good_and_small_models 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 flyingfishinwater/good_and_small_models 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 flyingfishinwater/good_and_small_models to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for flyingfishinwater/good_and_small_models to start chatting

Docker Model Runner
How to use flyingfishinwater/good_and_small_models with Docker Model Runner:
```
docker model run hf.co/flyingfishinwater/good_and_small_models:Q4_K_M
```

Lemonade

How to use flyingfishinwater/good_and_small_models with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull flyingfishinwater/good_and_small_models:Q4_K_M

Run and chat with the model

lemonade run user.good_and_small_models-Q4_K_M

List all available models

lemonade list

Atomic Chat

flyingfishinwater commited on Nov 3, 2025

Commit

a37005f

verified ·

1 Parent(s): fc155c5

Upload mmproj-Qwen3-VL-2B-F16.gguf

Browse files

Files changed (2) hide show

.gitattributes +1 -0
mmproj/mmproj-Qwen3-VL-2B-F16.gguf +3 -0

.gitattributes CHANGED Viewed

@@ -124,3 +124,4 @@ mmproj/mmproj-Qwen3-VL-F16.gguf filter=lfs diff=lfs merge=lfs -text
 Qwen3-VL-2B-Instruct-Q4_0.gguf filter=lfs diff=lfs merge=lfs -text
 Qwen3-VL-4B-Instruct-Q4_0.gguf filter=lfs diff=lfs merge=lfs -text
 Qwen3-VL-4B-Thinking-Q4_0.gguf filter=lfs diff=lfs merge=lfs -text

 Qwen3-VL-2B-Instruct-Q4_0.gguf filter=lfs diff=lfs merge=lfs -text
 Qwen3-VL-4B-Instruct-Q4_0.gguf filter=lfs diff=lfs merge=lfs -text
 Qwen3-VL-4B-Thinking-Q4_0.gguf filter=lfs diff=lfs merge=lfs -text
+mmproj/mmproj-Qwen3-VL-2B-F16.gguf filter=lfs diff=lfs merge=lfs -text

mmproj/mmproj-Qwen3-VL-2B-F16.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:cd5a851d3928697fa1bd76d459d2cc409b6cf40c9d9682b2f5c8e7c6a9f9630f
+size 819395232