Instructions to use nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor",
	filename="SmolLM2-360M.Q4_K_M.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor: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 nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor: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 nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor:Q4_K_M

Use Docker

docker model run hf.co/nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor:Q4_K_M

LM Studio
Jan
Ollama
How to use nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor with Ollama:
```
ollama run hf.co/nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor:Q4_K_M
```

Unsloth Studio new

How to use nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor 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 nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor 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 nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor to start chatting

Docker Model Runner
How to use nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor with Docker Model Runner:
```
docker model run hf.co/nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor:Q4_K_M
```

Lemonade

How to use nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nimendraai/SmolLM2-360M-Assignment-Metadata-Extractor:Q4_K_M

Run and chat with the model

lemonade run user.SmolLM2-360M-Assignment-Metadata-Extractor-Q4_K_M

List all available models

lemonade list

nimendraai commited on 30 days ago

Commit

0da34e3

verified ·

1 Parent(s): 7f82ce5

Add Github Repo URL

Browse files

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README.md +2 -0

README.md CHANGED Viewed

@@ -18,6 +18,8 @@ This is a highly specialized, lightweight (360M parameter) model fine-tuned spec
 It was finetuned and converted to 4-bit GGUF format using [Unsloth](https://github.com/unslothai/unsloth) for maximum CPU/GPU efficiency and rapid deployment via Ollama or `llama.cpp`.
 ## 📌 Model Capabilities
 Unlike generic LLMs, this model has been purposefully overfit on a highly mutated dataset to act as a **Zero-Shot Data Extractor**. It excels at:

 It was finetuned and converted to 4-bit GGUF format using [Unsloth](https://github.com/unslothai/unsloth) for maximum CPU/GPU efficiency and rapid deployment via Ollama or `llama.cpp`.
+**Github Repo**: *https://github.com/nmdra/Assignment-Metadata-Extractor*
 ## 📌 Model Capabilities
 Unlike generic LLMs, this model has been purposefully overfit on a highly mutated dataset to act as a **Zero-Shot Data Extractor**. It excels at: