Instructions to use QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-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 QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-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 QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-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 QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-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": "QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF:Q4_K_M

SGLang

How to use QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-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 "QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-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": "QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-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 "QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-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": "QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-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 QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-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 QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF to start chatting

How to use QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama-SmolTalk-3.2-1B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF

This is quantized version of prithivMLmods/Llama-SmolTalk-3.2-1B-Instruct created using llama.cpp

Original Model Card

Updated Files for Model Uploads 🤗

File Name [ Updated Files ]	Size	Description	Upload Status
`.gitattributes`	1.57 kB	Git attributes configuration file	Uploaded
`README.md`	42 Bytes	Initial README	Uploaded
`config.json`	1.03 kB	Configuration file	Uploaded
`generation_config.json`	248 Bytes	Configuration for text generation	Uploaded
`pytorch_model.bin`	2.47 GB	PyTorch model weights	Uploaded (LFS)
`special_tokens_map.json`	477 Bytes	Special token mappings	Uploaded
`tokenizer.json`	17.2 MB	Tokenizer configuration	Uploaded (LFS)
`tokenizer_config.json`	57.4 kB	Additional tokenizer settings	Uploaded

Model Type	Size	Context Length	Link
GGUF	1B	-	🤗 Llama-SmolTalk-3.2-1B-Instruct-GGUF

The Llama-SmolTalk-3.2-1B-Instruct model is a lightweight, instruction-tuned model designed for efficient text generation and conversational AI tasks. With a 1B parameter architecture, this model strikes a balance between performance and resource efficiency, making it ideal for applications requiring concise, contextually relevant outputs. The model has been fine-tuned to deliver robust instruction-following capabilities, catering to both structured and open-ended queries.

Key Features:

Instruction-Tuned Performance: Optimized to understand and execute user-provided instructions across diverse domains.
Lightweight Architecture: With just 1 billion parameters, the model provides efficient computation and storage without compromising output quality.
Versatile Use Cases: Suitable for tasks like content generation, conversational interfaces, and basic problem-solving.

Intended Applications:

Conversational AI: Engage users with dynamic and contextually aware dialogue.
Content Generation: Produce summaries, explanations, or other creative text outputs efficiently.
Instruction Execution: Follow user commands to generate precise and relevant responses.

Technical Details:

The model leverages PyTorch for training and inference, with a tokenizer optimized for seamless text input processing. It comes with essential configuration files, including config.json, generation_config.json, and tokenization files (tokenizer.json and special_tokens_map.json). The primary weights are stored in a PyTorch binary format (pytorch_model.bin), ensuring easy integration with existing workflows.

Model Type: GGUF
Size: 1B Parameters

The Llama-SmolTalk-3.2-1B-Instruct model is an excellent choice for lightweight text generation tasks, offering a blend of efficiency and effectiveness for a wide range of applications.

Downloads last month: 301

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF

Base model

meta-llama/Llama-3.2-1B-Instruct

Quantized

(422)

this model

QuantFactory
/

Llama-SmolTalk-3.2-1B-Instruct-GGUF

QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF

Original Model Card

Updated Files for Model Uploads 🤗

Key Features:

Intended Applications:

Technical Details:

Model tree for QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF

Dataset used to train QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF