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

AVAILABLE ON OLLAMA: https://ollama.com/unclemusclez/smollm-135m-instruct-devinator

Model Trained Using AutoTrain

Problem type: Sentence Transformers

Validation Metrics

No validation metrics available

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the Hugging Face Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'search_query: autotrain',
    'search_query: auto train',
    'search_query: i love autotrain',
]
embeddings = model.encode(sentences)
print(embeddings.shape)

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)