Instructions to use featherless-ai-quants/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use featherless-ai-quants/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-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 featherless-ai-quants/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf featherless-ai-quants/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-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 featherless-ai-quants/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf featherless-ai-quants/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-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 featherless-ai-quants/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf featherless-ai-quants/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-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 featherless-ai-quants/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf featherless-ai-quants/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-GGUF:Q4_K_M

Use Docker

docker model run hf.co/featherless-ai-quants/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use featherless-ai-quants/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "featherless-ai-quants/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "featherless-ai-quants/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/featherless-ai-quants/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-GGUF:Q4_K_M

Ollama
How to use featherless-ai-quants/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-GGUF with Ollama:
```
ollama run hf.co/featherless-ai-quants/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use featherless-ai-quants/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-GGUF with Docker Model Runner:
```
docker model run hf.co/featherless-ai-quants/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-GGUF:Q4_K_M
```

Lemonade

How to use featherless-ai-quants/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull featherless-ai-quants/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-GGUF

File size: 3,761 Bytes

138b379

---
base_model: AMindToThink/gemma-2-2b_RMU_s100_a100_layer7
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---

# AMindToThink/gemma-2-2b_RMU_s100_a100_layer7 GGUF Quantizations 🚀

![Featherless AI Quants](./featherless-quants.png)

*Optimized GGUF quantization files for enhanced model performance*

> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---

## Available Quantizations 📊

| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-GGUF/blob/main/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-IQ4_XS.gguf) | 1503.18 MB |
| Q2_K | [AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-GGUF/blob/main/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-Q2_K.gguf) | 1172.86 MB |
| Q3_K_L | [AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-GGUF/blob/main/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-Q3_K_L.gguf) | 1478.61 MB |
| Q3_K_M | [AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-GGUF/blob/main/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-Q3_K_M.gguf) | 1393.95 MB |
| Q3_K_S | [AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-GGUF/blob/main/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-Q3_K_S.gguf) | 1297.63 MB |
| Q4_K_M | [AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-GGUF/blob/main/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-Q4_K_M.gguf) | 1629.43 MB |
| Q4_K_S | [AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-GGUF/blob/main/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-Q4_K_S.gguf) | 1562.74 MB |
| Q5_K_M | [AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-GGUF/blob/main/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-Q5_K_M.gguf) | 1834.18 MB |
| Q5_K_S | [AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-GGUF/blob/main/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-Q5_K_S.gguf) | 1795.33 MB |
| Q6_K | [AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-GGUF/blob/main/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-Q6_K.gguf) | 2051.73 MB |
| Q8_0 | [AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-GGUF/blob/main/AMindToThink-gemma-2-2b_RMU_s100_a100_layer7-Q8_0.gguf) | 2655.50 MB |


---

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