Instructions to use QuietImpostor/OpenELM-3B-Instruct-GGUFs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use QuietImpostor/OpenELM-3B-Instruct-GGUFs with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="QuietImpostor/OpenELM-3B-Instruct-GGUFs", filename="OpenELM-3B-Instruct-F16.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use QuietImpostor/OpenELM-3B-Instruct-GGUFs with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf QuietImpostor/OpenELM-3B-Instruct-GGUFs:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuietImpostor/OpenELM-3B-Instruct-GGUFs:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf QuietImpostor/OpenELM-3B-Instruct-GGUFs:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuietImpostor/OpenELM-3B-Instruct-GGUFs: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 QuietImpostor/OpenELM-3B-Instruct-GGUFs:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuietImpostor/OpenELM-3B-Instruct-GGUFs: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 QuietImpostor/OpenELM-3B-Instruct-GGUFs:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuietImpostor/OpenELM-3B-Instruct-GGUFs:Q4_K_M
Use Docker
docker model run hf.co/QuietImpostor/OpenELM-3B-Instruct-GGUFs:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use QuietImpostor/OpenELM-3B-Instruct-GGUFs with Ollama:
ollama run hf.co/QuietImpostor/OpenELM-3B-Instruct-GGUFs:Q4_K_M
- Unsloth Studio new
How to use QuietImpostor/OpenELM-3B-Instruct-GGUFs 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 QuietImpostor/OpenELM-3B-Instruct-GGUFs 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 QuietImpostor/OpenELM-3B-Instruct-GGUFs to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for QuietImpostor/OpenELM-3B-Instruct-GGUFs to start chatting
- Docker Model Runner
How to use QuietImpostor/OpenELM-3B-Instruct-GGUFs with Docker Model Runner:
docker model run hf.co/QuietImpostor/OpenELM-3B-Instruct-GGUFs:Q4_K_M
- Lemonade
How to use QuietImpostor/OpenELM-3B-Instruct-GGUFs with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuietImpostor/OpenELM-3B-Instruct-GGUFs:Q4_K_M
Run and chat with the model
lemonade run user.OpenELM-3B-Instruct-GGUFs-Q4_K_M
List all available models
lemonade list
OpenELM 3B Instruct GGUFs
After a long wait, Llama.cpp (b3324) finally supports OpenELM, and that means OpenELM GGUFs! iMatrix quants are here!
Download the GGUFs below!
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| OpenELM-3B-Instruct-F32.gguf | F32 | 11.31 GB | Full precision, 32-bit floating point. Largest file size, baseline quality. |
| OpenELM-3B-Instruct-F16.gguf | F16 | 5.66 GB | Full precision, 16-bit floating point. Very large file size, highest quality. |
| OpenELM-3B-Instruct-Q8_0.gguf | Q8_0 | 3.01 GB | Extremely high quality, generally unneeded but max available quant. |
| OpenELM-3B-Instruct-Q6_K.gguf | Q6_K | 2.32 GB | Very high quality, near perfect, recommended. |
| OpenELM-3B-Instruct-Q5_K_M.gguf | Q5_K_M | 2.06 GB | High quality, recommended. |
| OpenELM-3B-Instruct-Q5_K_S.gguf | Q5_K_S | 1.96 GB | High quality, recommended. |
| OpenELM-3B-Instruct-Q5_1.gguf | Q5_1 | 2.13 GB | High quality, improved 5-bit quantization. |
| OpenELM-3B-Instruct-Q5_0.gguf | Q5_0 | 1.96 GB | High quality, alternative 5-bit quantization. |
| OpenELM-3B-Instruct-Q4_K_M.gguf | Q4_K_M | 1.76 GB | Good quality, recommended. |
| OpenELM-3B-Instruct-Q4_K_S.gguf | Q4_K_S | 1.62 GB | Slightly lower quality with more space savings, recommended. |
| OpenELM-3B-Instruct-Q4_1.gguf | Q4_1 | 1.79 GB | Good quality, improved 4-bit quantization. |
| OpenELM-3B-Instruct-Q4_0.gguf | Q4_0 | 1.62 GB | Good quality, older 4-bit quantization. |
| OpenELM-3B-Instruct-IQ4_XS.gguf | IQ4_XS | 1.54 GB | Decent quality, smaller than Q4_K_S with similar performance, recommended. |
| OpenELM-3B-Instruct-Q3_K_L.gguf | Q3_K_L | 1.55 GB | Lower quality but usable, good for low RAM availability. |
| OpenELM-3B-Instruct-Q3_K_M.gguf | Q3_K_M | 1.43 GB | Even lower quality. |
| OpenELM-3B-Instruct-IQ3_M.gguf | IQ3_M | 1.34 GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| OpenELM-3B-Instruct-IQ3_XS.gguf | IQ3_XS | 1.20 GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| OpenELM-3B-Instruct-Q3_K_S.gguf | Q3_K_S | 1.25 GB | Low quality, not recommended. |
| OpenELM-3B-Instruct-IQ3_XXS.gguf | IQ3_XXS | 1.16 GB | Lower quality, new method with decent performance, comparable to Q3 quants. |
| OpenELM-3B-Instruct-Q2_K.gguf | Q2_K | 1.07 GB | Very low quality but surprisingly usable. |
| OpenELM-3B-Instruct-IQ2_M.gguf | IQ2_M | 0.97 GB | Very low quality, uses SOTA techniques to also be surprisingly usable. |
| OpenELM-3B-Instruct-IQ2_S.gguf | IQ2_S | 0.89 GB | Very low quality, uses SOTA techniques to be usable. |
| OpenELM-3B-Instruct-IQ2_XS.gguf | IQ2_XS | 0.86 GB | Very low quality, uses SOTA techniques to be usable. |
| OpenELM-3B-Instruct-IQ2_XXS.gguf | IQ2_XXS | 0.77 GB | Extremely low quality, smallest 2-bit quantization. |
| OpenELM-3B-Instruct-IQ1_M.gguf | IQ1_M | 0.68 GB | Extremely low quality, 1-bit quantization. |
| OpenELM-3B-Instruct-IQ1_S.gguf | IQ1_S | 0.62 GB | Extremely low quality, smallest possible 1-bit quantization. |
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