gguf-Q8_0-smollm-135M-instruct-v0.2

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

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

gguf-Q8_0-smollm-135M-instruct-v0.2 / README.md

Felladrin

Duplicate from HuggingFaceTB/smollm-135M-instruct-v0.2-Q8_0-GGUF

710c673 verified over 1 year ago

preview code

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	---
	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
	```