Instructions to use unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2 with sentence-transformers:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2")

sentences = [
    "search_query: i love autotrain",
    "search_query: huggingface auto train",
    "search_query: hugging face auto train",
    "search_query: i love autotrain"
]
embeddings = model.encode(sentences)

similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4]

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2 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 unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2
# Run inference directly in the terminal:
llama cli -hf unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2
# Run inference directly in the terminal:
llama cli -hf unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2

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 unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2
# Run inference directly in the terminal:
./llama-cli -hf unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2

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 unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2
# Run inference directly in the terminal:
./build/bin/llama-cli -hf unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2

Use Docker

docker model run hf.co/unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2

LM Studio
Jan
Ollama
How to use unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2 with Ollama:
```
ollama run hf.co/unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2
```
Unsloth Desktop
Docker Model Runner
How to use unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2 with Docker Model Runner:
```
docker model run hf.co/unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2
```

Lemonade

How to use unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull unclemusclez/SmolLM-135M-Instruct-DEVINator-v0.2

Run and chat with the model

lemonade run user.SmolLM-135M-Instruct-DEVINator-v0.2-{{QUANT_TAG}}

List all available models

lemonade list

Atomic Chat

SmolLM-135M-Instruct-DEVINator-v0.2 / model.safetensors

unclemusclez

Upload folder using huggingface_hub

cf454d2 verified about 2 years ago

Download

History Blame Contribute Delete

269 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 5eed0c1a25ad856635888edc49298e39898143f3eb5bcd6e93573c2cacd9c1de
Size of remote file:: 269 MB
SHA256:: 46253581b13dac361204b102496ce1abb317c75483f8e9f9df4bba999186c715

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.