Instructions to use rahuldshetty/gemma-2b-gguf-quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rahuldshetty/gemma-2b-gguf-quantized with Transformers:

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rahuldshetty/gemma-2b-gguf-quantized", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use rahuldshetty/gemma-2b-gguf-quantized 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 rahuldshetty/gemma-2b-gguf-quantized:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf rahuldshetty/gemma-2b-gguf-quantized:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf rahuldshetty/gemma-2b-gguf-quantized:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf rahuldshetty/gemma-2b-gguf-quantized: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 rahuldshetty/gemma-2b-gguf-quantized:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf rahuldshetty/gemma-2b-gguf-quantized: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 rahuldshetty/gemma-2b-gguf-quantized:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf rahuldshetty/gemma-2b-gguf-quantized:Q4_K_M

Use Docker

docker model run hf.co/rahuldshetty/gemma-2b-gguf-quantized:Q4_K_M

Llama new
LM Studio
Jan
Ollama
How to use rahuldshetty/gemma-2b-gguf-quantized with Ollama:
```
ollama run hf.co/rahuldshetty/gemma-2b-gguf-quantized:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use rahuldshetty/gemma-2b-gguf-quantized with Docker Model Runner:
```
docker model run hf.co/rahuldshetty/gemma-2b-gguf-quantized:Q4_K_M
```

Lemonade

How to use rahuldshetty/gemma-2b-gguf-quantized with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull rahuldshetty/gemma-2b-gguf-quantized:Q4_K_M

Run and chat with the model

lemonade run user.gemma-2b-gguf-quantized-Q4_K_M

List all available models

lemonade list

Atomic Chat

rahuldshetty commited on Feb 21, 2024

Commit

474ea46

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Update README.md

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@@ -15,6 +15,7 @@ GGUF Quantized version of [gemma-2b](https://huggingface.co/google/gemma-2b).
 | ---- | ---- | ---- | ---- | ----- |
 | [gemma-2b-Q2_K.gguf](https://huggingface.co/rahuldshetty/gemma-2b-gguf-quantized/blob/main/gemma-2b-Q2_K.gguf) | Q2_K | 2 | 900 MB | smallest, significant quality loss - not recommended for most purposes |
 | [gemma-2b-Q4_K_M.gguf](https://huggingface.co/rahuldshetty/gemma-2b-gguf-quantized/blob/main/gemma-2b-Q4_K_M.gguf) | Q4_K_M | 4 | 1.5 GB | medium, balanced quality - recommended |
 # Gemma Model Card (Taken from Official HF Repo)

 | ---- | ---- | ---- | ---- | ----- |
 | [gemma-2b-Q2_K.gguf](https://huggingface.co/rahuldshetty/gemma-2b-gguf-quantized/blob/main/gemma-2b-Q2_K.gguf) | Q2_K | 2 | 900 MB | smallest, significant quality loss - not recommended for most purposes |
 | [gemma-2b-Q4_K_M.gguf](https://huggingface.co/rahuldshetty/gemma-2b-gguf-quantized/blob/main/gemma-2b-Q4_K_M.gguf) | Q4_K_M | 4 | 1.5 GB | medium, balanced quality - recommended |
+| [gemma-2b-Q8_0.gguf](https://huggingface.co/rahuldshetty/gemma-2b-gguf-quantized/blob/main/gemma-2b-Q8_0.gguf) | Q8_0 | 8 | 2.67 GB | very large, extremely low quality loss - not recommended|
 # Gemma Model Card (Taken from Official HF Repo)