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
- llama.cpp
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
| base_model: HuggingFaceTB/SmolLM2-1.7B-Instruct | |
| license: apache-2.0 | |
| library_name: gguf | |
| tags: | |
| - gguf | |
| - llama.cpp | |
| - backpack | |
| # SmolLM2-1.7B-Instruct — Backpack GGUF | |
| 🎒 **Backpack Verified** | |
| GGUF quantizations of [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/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: | |
| ```bash | |
| 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](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: 2026-08-22T16:19:59.672602+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](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-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. | |