Instructions to use Felladrin/gguf-Q8_0-smollm-135M-instruct-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Felladrin/gguf-Q8_0-smollm-135M-instruct-v0.2 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Felladrin/gguf-Q8_0-smollm-135M-instruct-v0.2", filename="smollm-135m-instruct-add-basics-q8_0.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use Felladrin/gguf-Q8_0-smollm-135M-instruct-v0.2 with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Felladrin/gguf-Q8_0-smollm-135M-instruct-v0.2:Q8_0 # Run inference directly in the terminal: llama-cli -hf Felladrin/gguf-Q8_0-smollm-135M-instruct-v0.2:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Felladrin/gguf-Q8_0-smollm-135M-instruct-v0.2:Q8_0 # Run inference directly in the terminal: llama-cli -hf Felladrin/gguf-Q8_0-smollm-135M-instruct-v0.2:Q8_0
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 Felladrin/gguf-Q8_0-smollm-135M-instruct-v0.2:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf Felladrin/gguf-Q8_0-smollm-135M-instruct-v0.2:Q8_0
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 Felladrin/gguf-Q8_0-smollm-135M-instruct-v0.2:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Felladrin/gguf-Q8_0-smollm-135M-instruct-v0.2:Q8_0
Use Docker
docker model run hf.co/Felladrin/gguf-Q8_0-smollm-135M-instruct-v0.2:Q8_0
- LM Studio
- Jan
- Ollama
How to use Felladrin/gguf-Q8_0-smollm-135M-instruct-v0.2 with Ollama:
ollama run hf.co/Felladrin/gguf-Q8_0-smollm-135M-instruct-v0.2:Q8_0
- Unsloth Studio new
How to use Felladrin/gguf-Q8_0-smollm-135M-instruct-v0.2 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 Felladrin/gguf-Q8_0-smollm-135M-instruct-v0.2 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 Felladrin/gguf-Q8_0-smollm-135M-instruct-v0.2 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Felladrin/gguf-Q8_0-smollm-135M-instruct-v0.2 to start chatting
- Docker Model Runner
How to use Felladrin/gguf-Q8_0-smollm-135M-instruct-v0.2 with Docker Model Runner:
docker model run hf.co/Felladrin/gguf-Q8_0-smollm-135M-instruct-v0.2:Q8_0
- Lemonade
How to use Felladrin/gguf-Q8_0-smollm-135M-instruct-v0.2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Felladrin/gguf-Q8_0-smollm-135M-instruct-v0.2:Q8_0
Run and chat with the model
lemonade run user.gguf-Q8_0-smollm-135M-instruct-v0.2-Q8_0
List all available models
lemonade list
File size: 2,251 Bytes
710c673 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | ---
base_model: HuggingFaceTB/SmolLM-135M-Instruct
datasets:
- Magpie-Align/Magpie-Pro-300K-Filtered
- bigcode/self-oss-instruct-sc2-exec-filter-50k
- teknium/OpenHermes-2.5
- HuggingFaceTB/everyday-conversations-llama3.1-2k
- HuggingFaceTB/instruct-data-basics-H4
license: apache-2.0
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
- llama-cpp
- gguf-my-repo
model-index:
- name: smollm-135M-instruct-add-basics
results: []
---
# smollm-135M-instruct-add-basics-Q8_0-GGUF
This model was converted to GGUF format from [`HuggingFaceTB/SmolLM-135M-Instruct`](https://huggingface.co/HuggingFaceTB/SmolLM-135M-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/HuggingFaceTB/SmolLM-135M-Instruct) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo HuggingFaceTB/smollm-135M-instruct-add-basics-Q8_0-GGUF --hf-file smollm-135m-instruct-add-basics-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo HuggingFaceTB/smollm-135M-instruct-add-basics-Q8_0-GGUF --hf-file smollm-135m-instruct-add-basics-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo HuggingFaceTB/smollm-135M-instruct-add-basics-Q8_0-GGUF --hf-file smollm-135m-instruct-add-basics-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo HuggingFaceTB/smollm-135M-instruct-add-basics-Q8_0-GGUF --hf-file smollm-135m-instruct-add-basics-q8_0.gguf -c 2048
```
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