Instructions to use backpack-run/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

How to use backpack-run/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 backpack-run/SmolLM2-135M-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf backpack-run/SmolLM2-135M-Instruct-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 backpack-run/SmolLM2-135M-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf backpack-run/SmolLM2-135M-Instruct-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 backpack-run/SmolLM2-135M-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf backpack-run/SmolLM2-135M-Instruct-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 backpack-run/SmolLM2-135M-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf backpack-run/SmolLM2-135M-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/backpack-run/SmolLM2-135M-Instruct-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use backpack-run/SmolLM2-135M-Instruct-GGUF with Ollama:
```
ollama run hf.co/backpack-run/SmolLM2-135M-Instruct-GGUF:Q4_K_M
```

Unsloth Studio

How to use backpack-run/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 backpack-run/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 backpack-run/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 backpack-run/SmolLM2-135M-Instruct-GGUF to start chatting

Docker Model Runner
How to use backpack-run/SmolLM2-135M-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/backpack-run/SmolLM2-135M-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use backpack-run/SmolLM2-135M-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull backpack-run/SmolLM2-135M-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.SmolLM2-135M-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

SmolLM2-135M-Instruct — Backpack GGUF

🎒 Backpack Verified

GGUF quantizations of HuggingFaceTB/SmolLM2-135M-Instruct, tested for llama.cpp-compatible inference and packaged for Backpack.

Model

Property	Value
Original model	`HuggingFaceTB/SmolLM2-135M-Instruct`
Original publisher	HuggingFaceTB
Upstream revision	`12fd25f77366fa6b3b4b768ec3050bf629380bac`
Architecture	LlamaForCausalLM
Parameters	134,515,008
Context length	8,192
License	apache-2.0

Available packages

Quantization	Size	Approx. RAM	Recommended for
Q4_K_M	100.6 MiB	1.14 GB	Most users
Q5_K_M	106.9 MiB	1.15 GB	Higher quality
Q8_0	138.1 MiB	1.2 GB	Plenty of memory

Memory values are estimates, not guarantees. Runtime configuration and context length change actual use.

Backpack recommendation

Recommended: Q4_K_M. It usually offers a practical quality, size, and speed balance for local inference.

Run with llama.cpp

Using the llama.cpp revision recorded below:

llama-cli --model SmolLM2-135M-Instruct-Q4_K_M.gguf --conversation

Run with Backpack

These artifacts and backpack-model.yaml are prepared for the Backpack AI workspace.

Validation

Package	Integrity	Load	Inference	Tokenizer
Q4_K_M	passed	passed	passed	passed
Q5_K_M	passed	passed	passed	passed
Q8_0	passed	passed	passed	passed

Packaged: 2026-08-20T20:32:10.805207+00:00
llama.cpp revision: de699957b92f490efebad149665b0dccf127eaff
SHA-256 checksums: see checksums.sha256
SmolLM2-135M-Instruct-Q4_K_M.gguf: dd18a11b8634d1684448986b8c166f75319f52082d759654aaa8fe5bd2f057e3
SmolLM2-135M-Instruct-Q5_K_M.gguf: 00680963c363ba10593daf7568dd6e1ee4c4771a608fe1b4e43f86b564d9b823
SmolLM2-135M-Instruct-Q8_0.gguf: ee785d9b4836ddb57207ae6daa630206a756c99fc52e19696f1e2ea2e8a41b99

Provenance

The source model was resolved to immutable revision 12fd25f77366fa6b3b4b768ec3050bf629380bac. It was converted with llama.cpp's convert_hf_to_gguf.py and quantized with llama-quantize; the exact tested revision is recorded above and in backpack-model.yaml.

License and attribution

Upstream declares apache-2.0. Review the upstream model card and comply with all applicable terms.

Backpack does not claim ownership of the original model. These artifacts are packaged and quantized distributions of the upstream model.

Disclaimer

Quantization can alter output quality. Memory estimates vary with runtime configuration, context length, and hardware.

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GGUF

Model size

0.1B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

8-bit

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Model tree for backpack-run/SmolLM2-135M-Instruct-GGUF

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HuggingFaceTB/SmolLM2-135M

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HuggingFaceTB/SmolLM2-135M-Instruct

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