Instructions to use Nexuss0781/SmolLM2-135M-Instruct-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 Nexuss0781/SmolLM2-135M-Instruct-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 Nexuss0781/SmolLM2-135M-Instruct-GGUF:Q6_K # Run inference directly in the terminal: llama cli -hf Nexuss0781/SmolLM2-135M-Instruct-GGUF:Q6_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Nexuss0781/SmolLM2-135M-Instruct-GGUF:Q6_K # Run inference directly in the terminal: llama cli -hf Nexuss0781/SmolLM2-135M-Instruct-GGUF:Q6_K
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 Nexuss0781/SmolLM2-135M-Instruct-GGUF:Q6_K # Run inference directly in the terminal: ./llama-cli -hf Nexuss0781/SmolLM2-135M-Instruct-GGUF:Q6_K
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 Nexuss0781/SmolLM2-135M-Instruct-GGUF:Q6_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf Nexuss0781/SmolLM2-135M-Instruct-GGUF:Q6_K
Use Docker
docker model run hf.co/Nexuss0781/SmolLM2-135M-Instruct-GGUF:Q6_K
- LM Studio
- Jan
- vLLM
How to use Nexuss0781/SmolLM2-135M-Instruct-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Nexuss0781/SmolLM2-135M-Instruct-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": "Nexuss0781/SmolLM2-135M-Instruct-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Nexuss0781/SmolLM2-135M-Instruct-GGUF:Q6_K
- Ollama
How to use Nexuss0781/SmolLM2-135M-Instruct-GGUF with Ollama:
ollama run hf.co/Nexuss0781/SmolLM2-135M-Instruct-GGUF:Q6_K
- Unsloth Studio
How to use Nexuss0781/SmolLM2-135M-Instruct-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 Nexuss0781/SmolLM2-135M-Instruct-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 Nexuss0781/SmolLM2-135M-Instruct-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Nexuss0781/SmolLM2-135M-Instruct-GGUF to start chatting
- Docker Model Runner
How to use Nexuss0781/SmolLM2-135M-Instruct-GGUF with Docker Model Runner:
docker model run hf.co/Nexuss0781/SmolLM2-135M-Instruct-GGUF:Q6_K
- Lemonade
How to use Nexuss0781/SmolLM2-135M-Instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Nexuss0781/SmolLM2-135M-Instruct-GGUF:Q6_K
Run and chat with the model
lemonade run user.SmolLM2-135M-Instruct-GGUF-Q6_K
List all available models
lemonade list
- Atomic Chat
SmolLM2 135M Instruct — Unofficial GGUF Conversions
Original source model and publisher: HuggingFaceTB/SmolLM2-135M-Instruct by HuggingFaceTB. This repository contains file-format conversions only. It does not claim authorship, ownership, training, or creation of the underlying SmolLM2 model.
This repository provides local CPU-oriented GGUF conversions of the upstream SmolLM2 135M Instruct model for use with llama.cpp and compatible runtimes. The source model's Apache-2.0 license is retained; no separate or replacement license is asserted for the underlying model.
Available files
| File | Format | Intended use | File size |
|---|---|---|---|
SmolLM2-135M-Instruct-f16.gguf |
F16 GGUF | Quality-preserving local inference | 258 MiB |
SmolLM2-135M-Instruct-Q6_K.gguf |
Q6_K GGUF | Lower-memory CPU inference | 132 MiB |
The F16 variant preserves the original converted weight precision. The Q6_K variant is provided for systems where memory usage is more important than retaining the F16 representation. Use the F16 file for the highest-fidelity local behavior.
Quick start with llama.cpp
./llama-cli \
--model SmolLM2-135M-Instruct-f16.gguf \
--conversation \
--n-gpu-layers 0
For persistent local serving, start llama-server once and send OpenAI-compatible requests to the local endpoint:
./llama-server \
--model SmolLM2-135M-Instruct-f16.gguf \
--host 127.0.0.1 \
--port 8080 \
--ctx-size 2048 \
--n-gpu-layers 0
Integrity verification
Verify downloaded files with:
sha256sum -c CHECKSUMS.sha256
Conversion details
The model was converted from the official Hugging Face checkpoint with the official llama.cpp Hugging Face-to-GGUF converter. The F16 GGUF output was retained as the quality-preserving variant, then quantized with llama-quantize to produce the Q6_K variant. See conversion-metadata.json for the artifact metadata.
Upstream source, ownership, and license
The underlying model is published by HuggingFaceTB at HuggingFaceTB/SmolLM2-135M-Instruct. The upstream model card identifies its license as Apache-2.0. The conversion distributor claims no ownership or authorship of the original model and does not impose a new license on it. Retain the upstream attribution and consult the source model card for limitations, training details, and citation information.
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