Instructions to use Mitchins/smollm2-helpbot-135M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Mitchins/smollm2-helpbot-135M-GGUF 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 Mitchins/smollm2-helpbot-135M-GGUF # Run inference directly in the terminal: llama cli -hf Mitchins/smollm2-helpbot-135M-GGUF
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Mitchins/smollm2-helpbot-135M-GGUF # Run inference directly in the terminal: llama cli -hf Mitchins/smollm2-helpbot-135M-GGUF
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 Mitchins/smollm2-helpbot-135M-GGUF # Run inference directly in the terminal: ./llama-cli -hf Mitchins/smollm2-helpbot-135M-GGUF
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 Mitchins/smollm2-helpbot-135M-GGUF # Run inference directly in the terminal: ./build/bin/llama-cli -hf Mitchins/smollm2-helpbot-135M-GGUF
Use Docker
docker model run hf.co/Mitchins/smollm2-helpbot-135M-GGUF
- LM Studio
- Jan
- Ollama
How to use Mitchins/smollm2-helpbot-135M-GGUF with Ollama:
ollama run hf.co/Mitchins/smollm2-helpbot-135M-GGUF
- Unsloth Desktop
- Docker Model Runner
How to use Mitchins/smollm2-helpbot-135M-GGUF with Docker Model Runner:
docker model run hf.co/Mitchins/smollm2-helpbot-135M-GGUF
- Lemonade
How to use Mitchins/smollm2-helpbot-135M-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Mitchins/smollm2-helpbot-135M-GGUF
Run and chat with the model
lemonade run user.smollm2-helpbot-135M-GGUF-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
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Check out the documentation for more information.
SmolLM2 HelpBot 135M - GGUF
GGUF format of Mitchins/smollm2-helpbot-135M for use with Ollama and LM Studio.
Model Details
- Base Model: HuggingFaceTB/SmolLM2-135M
- Fine-tuned on: Self-help and conversational tasks
- License: Apache 2.0
- Architecture: LLaMA
- Context Length: 8192 tokens
- Embedding Dimension: 576
- Heads: 9 (with 3 key-value heads)
- Parameters: 135M
Model Format
| Format | Size | Notes |
|---|---|---|
smollm2-helpbot-135m.gguf |
258 MB | F16 (Full Precision) - Best quality, optimal for LM Studio & Ollama |
Usage with Ollama
# Download the model and create a Modelfile
cat > Modelfile << EOF
FROM ./smollm2-helpbot-135m.gguf
EOF
# Import the model
ollama create smollm2-helpbot-135m -f Modelfile
# Run the model
ollama run smollm2-helpbot-135m
Usage with LM Studio
- Download
smollm2-helpbot-135m.gguf - Open LM Studio
- Click "Load Model" and select the downloaded file
- Start chatting!
The model loads instantly and maintains coherency due to F16 precision.
Inference Example
from llama_cpp import Llama
llm = Llama(model_path="smollm2-helpbot-135m.gguf", n_ctx=8192)
prompt = "Human: How can I improve my confidence?\n\nAssistant:"
output = llm(prompt, max_tokens=512)
print(output['choices'][0]['text'])
Installation (llama-cpp-python)
pip install llama-cpp-python
Model Performance
F16 precision provides:
- โ Full coherency - No compression loss
- โ Fast inference - Optimized GGUF format
- โ Small file size - 258 MB (tiny model)
- โ Universal compatibility - Works with any GGUF loader
Perfect for:
- Local inference on consumer hardware
- Running in Ollama on all systems
- LM Studio with excellent quality
- Embedded applications with 256 MB+ available RAM
Original Model Information
This is a GGUF conversion of Mitchins/smollm2-helpbot-135M.
For more details about the original fine-tuning, please visit the original model card.
License
Apache License 2.0 - See LICENSE for details
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