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-GGUF / backpack-model.yaml

amirthakatesh

Add files using upload-large-folder tool

4dfc1dd verified 3 days ago

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	schema_version: 1
	model:
	id: smollm2-1-7b-instruct
	display_name: SmolLM2-1.7B-Instruct
	architecture: LlamaForCausalLM
	model_type: llama
	parameter_count: 1711376384
	context_length: 8192
	tokenizer_type: GPT2Tokenizer
	tasks:
	- text-generation
	chat_template: true
	multimodal: false
	custom_code: false
	safetensors: true
	upstream:
	repo: HuggingFaceTB/SmolLM2-1.7B-Instruct
	revision: 31b70e2e869a7173562077fd711b654946d38674
	license:
	identifier: apache-2.0
	redistribution: allowed
	reason: Upstream declares a recognized license that permits redistribution; retain
	its terms and attribution.
	gated: false
	private: false
	source_url: https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct
	packages:
	- id: gguf-q4-k-m
	format: gguf
	precision: Q4_K_M
	filename: SmolLM2-1.7B-Instruct-Q4_K_M.gguf
	sha256: 5ac93b9ab47d7b3069282f1cef65e1534e68b2c97d1ec129288bc792fa33ec5c
	size_bytes: 1055609344
	bits_per_weight: 4.83
	runtime:
	provider: llama.cpp
	tested_revision: de699957b92f490efebad149665b0dccf127eaff
	hardware:
	estimated_ram_gb: 1.27
	estimated_vram_gb: 1.16
	recommended_ram_gb: 2.43
	note: Estimate; runtime use varies with context length and configuration.
	validation:
	integrity: passed
	metadata: passed
	load: passed
	inference: passed
	tokenizer: passed
	tested_at: '2026-08-22T16:19:42.516982Z'
	details:
	version: '3'
	tensor_count: '218'
	metadata_count: '30'
	- id: gguf-q5-k-m
	format: gguf
	precision: Q5_K_M
	filename: SmolLM2-1.7B-Instruct-Q5_K_M.gguf
	sha256: a2fdd913de171cea397f3c876e9b23af9a899491e0dd32958affa2ec44c5d99c
	size_bytes: 1225478656
	bits_per_weight: 5.67
	runtime:
	provider: llama.cpp
	tested_revision: de699957b92f490efebad149665b0dccf127eaff
	hardware:
	estimated_ram_gb: 1.47
	estimated_vram_gb: 1.35
	recommended_ram_gb: 2.65
	note: Estimate; runtime use varies with context length and configuration.
	validation:
	integrity: passed
	metadata: passed
	load: passed
	inference: passed
	tokenizer: passed
	tested_at: '2026-08-22T16:19:49.471593Z'
	details:
	version: '3'
	tensor_count: '218'
	metadata_count: '30'
	- id: gguf-q8-0
	format: gguf
	precision: Q8_0
	filename: SmolLM2-1.7B-Instruct-Q8_0.gguf
	sha256: af66f269a1a77bd0b0df4f5a123b96582f1dac700b7846e0880b3eb54fa4f563
	size_bytes: 1820414464
	bits_per_weight: 8.5
	runtime:
	provider: llama.cpp
	tested_revision: de699957b92f490efebad149665b0dccf127eaff
	hardware:
	estimated_ram_gb: 2.18
	estimated_vram_gb: 2.0
	recommended_ram_gb: 3.46
	note: Estimate; runtime use varies with context length and configuration.
	validation:
	integrity: passed
	metadata: passed
	load: passed
	inference: passed
	tokenizer: passed
	tested_at: '2026-08-22T16:19:57.607061Z'
	details:
	version: '3'
	tensor_count: '218'
	metadata_count: '30'
	generated_at: '2026-08-22T16:19:59.672602Z'