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-GGUF / README.md

amirthakatesh

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	---
	base_model: HuggingFaceTB/SmolLM2-135M-Instruct
	license: apache-2.0
	library_name: gguf
	tags:
	- gguf
	- llama.cpp
	- backpack
	---

	# SmolLM2-135M-Instruct — Backpack GGUF

	🎒 Backpack Verified

	GGUF quantizations of [HuggingFaceTB/SmolLM2-135M-Instruct](https://huggingface.co/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:

	```bash
	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](https://backpack.run).

	## 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](https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct) 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.