Instructions to use featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-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-it_RMU_s200_a500_layer11-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-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-it_RMU_s200_a500_layer11-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-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-it_RMU_s200_a500_layer11-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-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-it_RMU_s200_a500_layer11-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF:Q4_K_M
Use Docker
docker model run hf.co/featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-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-it_RMU_s200_a500_layer11-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF:Q4_K_M
- Ollama
How to use featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF with Ollama:
ollama run hf.co/featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF with Docker Model Runner:
docker model run hf.co/featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF:Q4_K_M
- Lemonade
How to use featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Upload folder using huggingface_hub
Browse files- .gitattributes +12 -0
- AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-IQ4_XS.gguf +3 -0
- AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-Q2_K.gguf +3 -0
- AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-Q3_K_L.gguf +3 -0
- AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-Q3_K_M.gguf +3 -0
- AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-Q3_K_S.gguf +3 -0
- AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-Q4_K_M.gguf +3 -0
- AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-Q4_K_S.gguf +3 -0
- AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-Q5_K_M.gguf +3 -0
- AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-Q5_K_S.gguf +3 -0
- AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-Q6_K.gguf +3 -0
- AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-Q8_0.gguf +3 -0
- README.md +47 -0
- featherless-quants.png +3 -0
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---
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base_model: AMindToThink/gemma-2-2b-it_RMU_s200_a500_layer11
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pipeline_tag: text-generation
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quantized_by: featherless-ai-quants
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---
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# AMindToThink/gemma-2-2b-it_RMU_s200_a500_layer11 GGUF Quantizations π
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*Optimized GGUF quantization files for enhanced model performance*
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> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
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---
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## Available Quantizations π
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| Quantization Type | File | Size |
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|-------------------|------|------|
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| IQ4_XS | [AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF/blob/main/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-IQ4_XS.gguf) | 1503.19 MB |
|
| 21 |
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| Q2_K | [AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF/blob/main/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-Q2_K.gguf) | 1172.86 MB |
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| 22 |
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| Q3_K_L | [AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF/blob/main/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-Q3_K_L.gguf) | 1478.61 MB |
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| 23 |
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| Q3_K_M | [AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF/blob/main/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-Q3_K_M.gguf) | 1393.95 MB |
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| 24 |
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| Q3_K_S | [AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF/blob/main/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-Q3_K_S.gguf) | 1297.63 MB |
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| Q4_K_M | [AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF/blob/main/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-Q4_K_M.gguf) | 1629.43 MB |
|
| 26 |
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| Q4_K_S | [AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF/blob/main/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-Q4_K_S.gguf) | 1562.74 MB |
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| 27 |
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| Q5_K_M | [AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF/blob/main/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-Q5_K_M.gguf) | 1834.18 MB |
|
| 28 |
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| Q5_K_S | [AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF/blob/main/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-Q5_K_S.gguf) | 1795.33 MB |
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| 29 |
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| Q6_K | [AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF/blob/main/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-Q6_K.gguf) | 2051.73 MB |
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| 30 |
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| Q8_0 | [AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF/blob/main/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-Q8_0.gguf) | 2655.50 MB |
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---
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## β‘ Powered by [Featherless AI](https://featherless.ai)
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### Key Features
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| 38 |
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| 39 |
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- π₯ **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
|
| 40 |
+
- π οΈ **Zero Infrastructure** - No server setup or maintenance required
|
| 41 |
+
- π **Vast Compatibility** - Support for 2400+ models and counting
|
| 42 |
+
- π **Affordable Pricing** - Starting at just $10/month
|
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| 44 |
+
---
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**Links:**
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[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
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featherless-quants.png
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Git LFS Details
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