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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Llama-SmolTalk-3.2-1B-Instruct")
model = AutoModelForCausalLM.from_pretrained("prithivMLmods/Llama-SmolTalk-3.2-1B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Install from pip and serve model

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

Use Docker

docker model run hf.co/prithivMLmods/Llama-SmolTalk-3.2-1B-Instruct

SGLang

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

Docker Model Runner
How to use prithivMLmods/Llama-SmolTalk-3.2-1B-Instruct with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Llama-SmolTalk-3.2-1B-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Llama-SmolTalk-3.2-1B-Instruct Model File

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.

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

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.