Instructions to use backpack-run/SmolLM2-1.7B-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/SmolLM2-1.7B-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/SmolLM2-1.7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf backpack-run/SmolLM2-1.7B-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/SmolLM2-1.7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf backpack-run/SmolLM2-1.7B-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/SmolLM2-1.7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf backpack-run/SmolLM2-1.7B-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/SmolLM2-1.7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf backpack-run/SmolLM2-1.7B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/backpack-run/SmolLM2-1.7B-Instruct-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use backpack-run/SmolLM2-1.7B-Instruct-GGUF with Ollama:
```
ollama run hf.co/backpack-run/SmolLM2-1.7B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio

How to use backpack-run/SmolLM2-1.7B-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/SmolLM2-1.7B-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/SmolLM2-1.7B-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/SmolLM2-1.7B-Instruct-GGUF to start chatting

Docker Model Runner
How to use backpack-run/SmolLM2-1.7B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/backpack-run/SmolLM2-1.7B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use backpack-run/SmolLM2-1.7B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull backpack-run/SmolLM2-1.7B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.SmolLM2-1.7B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

SmolLM2-1.7B-Instruct — Backpack GGUF

🎒 Backpack Verified

GGUF quantizations of HuggingFaceTB/SmolLM2-1.7B-Instruct, tested for llama.cpp-compatible inference and packaged for Backpack.

Model

Property	Value
Original model	`HuggingFaceTB/SmolLM2-1.7B-Instruct`
Original publisher	HuggingFaceTB
Upstream revision	`31b70e2e869a7173562077fd711b654946d38674`
Architecture	LlamaForCausalLM
Parameters	1,711,376,384
Context length	8,192
License	apache-2.0

Available packages

Quantization	Size	Approx. RAM	Recommended for
Q4_K_M	1006.7 MiB	2.43 GB	Most users
Q5_K_M	1.1 GiB	2.65 GB	Higher quality
Q8_0	1.7 GiB	3.46 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 SmolLM2-1.7B-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: 2026-08-22T16:19:59.672602+00:00
llama.cpp revision: de699957b92f490efebad149665b0dccf127eaff
SHA-256 checksums: see checksums.sha256
SmolLM2-1.7B-Instruct-Q4_K_M.gguf: 5ac93b9ab47d7b3069282f1cef65e1534e68b2c97d1ec129288bc792fa33ec5c
SmolLM2-1.7B-Instruct-Q5_K_M.gguf: a2fdd913de171cea397f3c876e9b23af9a899491e0dd32958affa2ec44c5d99c
SmolLM2-1.7B-Instruct-Q8_0.gguf: af66f269a1a77bd0b0df4f5a123b96582f1dac700b7846e0880b3eb54fa4f563

Provenance

The source model was resolved to immutable revision 31b70e2e869a7173562077fd711b654946d38674. 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

2B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

8-bit

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Model tree for backpack-run/SmolLM2-1.7B-Instruct-GGUF

Base model

HuggingFaceTB/SmolLM2-1.7B

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HuggingFaceTB/SmolLM2-1.7B-Instruct

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