Instructions to use mradermacher/bayling-2-llama-3-8b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/bayling-2-llama-3-8b-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/bayling-2-llama-3-8b-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mradermacher/bayling-2-llama-3-8b-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 mradermacher/bayling-2-llama-3-8b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/bayling-2-llama-3-8b-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 mradermacher/bayling-2-llama-3-8b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/bayling-2-llama-3-8b-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 mradermacher/bayling-2-llama-3-8b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/bayling-2-llama-3-8b-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 mradermacher/bayling-2-llama-3-8b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/bayling-2-llama-3-8b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mradermacher/bayling-2-llama-3-8b-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use mradermacher/bayling-2-llama-3-8b-GGUF with Ollama:
```
ollama run hf.co/mradermacher/bayling-2-llama-3-8b-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use mradermacher/bayling-2-llama-3-8b-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/bayling-2-llama-3-8b-GGUF:Q4_K_M
```

Lemonade

How to use mradermacher/bayling-2-llama-3-8b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/bayling-2-llama-3-8b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.bayling-2-llama-3-8b-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

mradermacher commited on Apr 3, 2025

Commit

2047eb5

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@@ -30,7 +30,15 @@ more details, including on how to concatenate multi-part files.
 | Link | Type | Size/GB | Notes |
 |:-----|:-----|--------:|:------|
 | [GGUF](https://huggingface.co/mradermacher/bayling-2-llama-3-8b-GGUF/resolve/main/bayling-2-llama-3-8b.Q2_K.gguf) | Q2_K | 3.3 |  |
 | [GGUF](https://huggingface.co/mradermacher/bayling-2-llama-3-8b-GGUF/resolve/main/bayling-2-llama-3-8b.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
 Here is a handy graph by ikawrakow comparing some lower-quality quant
 types (lower is better):

 | Link | Type | Size/GB | Notes |
 |:-----|:-----|--------:|:------|
 | [GGUF](https://huggingface.co/mradermacher/bayling-2-llama-3-8b-GGUF/resolve/main/bayling-2-llama-3-8b.Q2_K.gguf) | Q2_K | 3.3 |  |
+| [GGUF](https://huggingface.co/mradermacher/bayling-2-llama-3-8b-GGUF/resolve/main/bayling-2-llama-3-8b.Q3_K_S.gguf) | Q3_K_S | 3.8 |  |
+| [GGUF](https://huggingface.co/mradermacher/bayling-2-llama-3-8b-GGUF/resolve/main/bayling-2-llama-3-8b.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
+| [GGUF](https://huggingface.co/mradermacher/bayling-2-llama-3-8b-GGUF/resolve/main/bayling-2-llama-3-8b.Q3_K_L.gguf) | Q3_K_L | 4.4 |  |
 | [GGUF](https://huggingface.co/mradermacher/bayling-2-llama-3-8b-GGUF/resolve/main/bayling-2-llama-3-8b.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
+| [GGUF](https://huggingface.co/mradermacher/bayling-2-llama-3-8b-GGUF/resolve/main/bayling-2-llama-3-8b.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
+| [GGUF](https://huggingface.co/mradermacher/bayling-2-llama-3-8b-GGUF/resolve/main/bayling-2-llama-3-8b.Q5_K_S.gguf) | Q5_K_S | 5.7 |  |
+| [GGUF](https://huggingface.co/mradermacher/bayling-2-llama-3-8b-GGUF/resolve/main/bayling-2-llama-3-8b.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
+| [GGUF](https://huggingface.co/mradermacher/bayling-2-llama-3-8b-GGUF/resolve/main/bayling-2-llama-3-8b.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
+| [GGUF](https://huggingface.co/mradermacher/bayling-2-llama-3-8b-GGUF/resolve/main/bayling-2-llama-3-8b.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
 Here is a handy graph by ikawrakow comparing some lower-quality quant
 types (lower is better):