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

Libraries

How to use mradermacher/gemma-2b-tamil-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/gemma-2b-tamil-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

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

Use Docker

docker model run hf.co/mradermacher/gemma-2b-tamil-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use mradermacher/gemma-2b-tamil-GGUF with Ollama:
```
ollama run hf.co/mradermacher/gemma-2b-tamil-GGUF:Q4_K_M
```

Unsloth Studio

How to use mradermacher/gemma-2b-tamil-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 mradermacher/gemma-2b-tamil-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 mradermacher/gemma-2b-tamil-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mradermacher/gemma-2b-tamil-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use mradermacher/gemma-2b-tamil-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/gemma-2b-tamil-GGUF:Q4_K_M
```

Lemonade

How to use mradermacher/gemma-2b-tamil-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/gemma-2b-tamil-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.gemma-2b-tamil-GGUF-Q4_K_M

List all available models

lemonade list

mradermacher commited on Dec 17, 2024

Commit

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README.md CHANGED Viewed

@@ -38,11 +38,14 @@ more details, including on how to concatenate multi-part files.
 | [GGUF](https://huggingface.co/mradermacher/gemma-2b-tamil-GGUF/resolve/main/gemma-2b-tamil.Q3_K_S.gguf) | Q3_K_S | 1.4 |  |
 | [GGUF](https://huggingface.co/mradermacher/gemma-2b-tamil-GGUF/resolve/main/gemma-2b-tamil.Q3_K_M.gguf) | Q3_K_M | 1.5 | lower quality |
 | [GGUF](https://huggingface.co/mradermacher/gemma-2b-tamil-GGUF/resolve/main/gemma-2b-tamil.Q3_K_L.gguf) | Q3_K_L | 1.6 |  |
 | [GGUF](https://huggingface.co/mradermacher/gemma-2b-tamil-GGUF/resolve/main/gemma-2b-tamil.Q4_K_S.gguf) | Q4_K_S | 1.7 | fast, recommended |
 | [GGUF](https://huggingface.co/mradermacher/gemma-2b-tamil-GGUF/resolve/main/gemma-2b-tamil.Q4_K_M.gguf) | Q4_K_M | 1.7 | fast, recommended |
 | [GGUF](https://huggingface.co/mradermacher/gemma-2b-tamil-GGUF/resolve/main/gemma-2b-tamil.Q5_K_S.gguf) | Q5_K_S | 1.9 |  |
 | [GGUF](https://huggingface.co/mradermacher/gemma-2b-tamil-GGUF/resolve/main/gemma-2b-tamil.Q6_K.gguf) | Q6_K | 2.2 | very good quality |
 | [GGUF](https://huggingface.co/mradermacher/gemma-2b-tamil-GGUF/resolve/main/gemma-2b-tamil.Q8_0.gguf) | Q8_0 | 2.8 | fast, best quality |
 Here is a handy graph by ikawrakow comparing some lower-quality quant
 types (lower is better):

 | [GGUF](https://huggingface.co/mradermacher/gemma-2b-tamil-GGUF/resolve/main/gemma-2b-tamil.Q3_K_S.gguf) | Q3_K_S | 1.4 |  |
 | [GGUF](https://huggingface.co/mradermacher/gemma-2b-tamil-GGUF/resolve/main/gemma-2b-tamil.Q3_K_M.gguf) | Q3_K_M | 1.5 | lower quality |
 | [GGUF](https://huggingface.co/mradermacher/gemma-2b-tamil-GGUF/resolve/main/gemma-2b-tamil.Q3_K_L.gguf) | Q3_K_L | 1.6 |  |
+| [GGUF](https://huggingface.co/mradermacher/gemma-2b-tamil-GGUF/resolve/main/gemma-2b-tamil.IQ4_XS.gguf) | IQ4_XS | 1.6 |  |
 | [GGUF](https://huggingface.co/mradermacher/gemma-2b-tamil-GGUF/resolve/main/gemma-2b-tamil.Q4_K_S.gguf) | Q4_K_S | 1.7 | fast, recommended |
 | [GGUF](https://huggingface.co/mradermacher/gemma-2b-tamil-GGUF/resolve/main/gemma-2b-tamil.Q4_K_M.gguf) | Q4_K_M | 1.7 | fast, recommended |
 | [GGUF](https://huggingface.co/mradermacher/gemma-2b-tamil-GGUF/resolve/main/gemma-2b-tamil.Q5_K_S.gguf) | Q5_K_S | 1.9 |  |
+| [GGUF](https://huggingface.co/mradermacher/gemma-2b-tamil-GGUF/resolve/main/gemma-2b-tamil.Q5_K_M.gguf) | Q5_K_M | 1.9 |  |
 | [GGUF](https://huggingface.co/mradermacher/gemma-2b-tamil-GGUF/resolve/main/gemma-2b-tamil.Q6_K.gguf) | Q6_K | 2.2 | very good quality |
 | [GGUF](https://huggingface.co/mradermacher/gemma-2b-tamil-GGUF/resolve/main/gemma-2b-tamil.Q8_0.gguf) | Q8_0 | 2.8 | fast, best quality |
+| [GGUF](https://huggingface.co/mradermacher/gemma-2b-tamil-GGUF/resolve/main/gemma-2b-tamil.f16.gguf) | f16 | 5.1 | 16 bpw, overkill |
 Here is a handy graph by ikawrakow comparing some lower-quality quant
 types (lower is better):