Instructions to use prithivMLmods/SmolLM2-135M-F32-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/SmolLM2-135M-F32-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="prithivMLmods/SmolLM2-135M-F32-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/SmolLM2-135M-F32-GGUF", dtype="auto")

llama-cpp-python

How to use prithivMLmods/SmolLM2-135M-F32-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/SmolLM2-135M-F32-GGUF",
	filename="SmolLM2-135M-Instruct.BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use prithivMLmods/SmolLM2-135M-F32-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/SmolLM2-135M-F32-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/SmolLM2-135M-F32-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/SmolLM2-135M-F32-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/SmolLM2-135M-F32-GGUF: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 prithivMLmods/SmolLM2-135M-F32-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/SmolLM2-135M-F32-GGUF: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 prithivMLmods/SmolLM2-135M-F32-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/SmolLM2-135M-F32-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/SmolLM2-135M-F32-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/SmolLM2-135M-F32-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/SmolLM2-135M-F32-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/SmolLM2-135M-F32-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/SmolLM2-135M-F32-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/SmolLM2-135M-F32-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prithivMLmods/SmolLM2-135M-F32-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/SmolLM2-135M-F32-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prithivMLmods/SmolLM2-135M-F32-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/SmolLM2-135M-F32-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use prithivMLmods/SmolLM2-135M-F32-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/SmolLM2-135M-F32-GGUF:Q4_K_M
```

Unsloth Studio new

How to use prithivMLmods/SmolLM2-135M-F32-GGUF 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 prithivMLmods/SmolLM2-135M-F32-GGUF 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 prithivMLmods/SmolLM2-135M-F32-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prithivMLmods/SmolLM2-135M-F32-GGUF to start chatting

Docker Model Runner
How to use prithivMLmods/SmolLM2-135M-F32-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/SmolLM2-135M-F32-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/SmolLM2-135M-F32-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/SmolLM2-135M-F32-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.SmolLM2-135M-F32-GGUF-Q4_K_M

List all available models

lemonade list

SmolLM2-135M-Instruct-GGUF

SmolLM2-135M-Instruct : The 135M model was trained on 2 trillion tokens using a diverse dataset combination: FineWeb-Edu, DCLM, The Stack, along with new filtered datasets we curated and will release soon. We developed the instruct version through supervised fine-tuning (SFT) using a combination of public datasets and our own curated datasets. We then applied Direct Preference Optimization (DPO) using UltraFeedback.

Model Files

File Name	Size	Format Description
SmolLM2-135M-Instruct.F32.gguf	540 MB	Full precision (32-bit floating point)
SmolLM2-135M-Instruct.BF16.gguf	271 MB	Brain floating point 16-bit
SmolLM2-135M-Instruct.F16.gguf	271 MB	Half precision (16-bit floating point)
SmolLM2-135M-Instruct.Q8_0.gguf	145 MB	8-bit quantization
SmolLM2-135M-Instruct.Q6_K.gguf	138 MB	6-bit quantization (K-quant)
SmolLM2-135M-Instruct.Q5_K_M.gguf	112 MB	5-bit quantization (K-quant, medium)
SmolLM2-135M-Instruct.Q5_K_S.gguf	110 MB	5-bit quantization (K-quant, small)
SmolLM2-135M-Instruct.Q4_K_M.gguf	105 MB	4-bit quantization (K-quant, medium)
SmolLM2-135M-Instruct.Q4_K_S.gguf	102 MB	4-bit quantization (K-quant, small)
SmolLM2-135M-Instruct.Q3_K_L.gguf	97.5 MB	3-bit quantization (K-quant, large)
SmolLM2-135M-Instruct.Q3_K_M.gguf	93.5 MB	3-bit quantization (K-quant, medium)
SmolLM2-135M-Instruct.Q3_K_S.gguf	88.2 MB	3-bit quantization (K-quant, small)
SmolLM2-135M-Instruct.Q2_K.gguf	88.2 MB	2-bit quantization (K-quant)

Quants Usage

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

Downloads last month: 123

GGUF

Model size

0.1B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

32-bit

Model tree for prithivMLmods/SmolLM2-135M-F32-GGUF

Base model

HuggingFaceTB/SmolLM2-135M

Quantized

HuggingFaceTB/SmolLM2-135M-Instruct

Quantized

(96)

this model

Collection including prithivMLmods/SmolLM2-135M-F32-GGUF

GPT-Generated Unified Format (GGUF)

Collection

ease of reading • 53 items • Updated 10 days ago • 4