Instructions to use sihab/slm-1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sihab/slm-1.0 with Transformers:

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

pipe = pipeline("text-generation", model="sihab/slm-1.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sihab/slm-1.0")
model = AutoModelForCausalLM.from_pretrained("sihab/slm-1.0")
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 sihab/slm-1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sihab/slm-1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sihab/slm-1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sihab/slm-1.0

SGLang

How to use sihab/slm-1.0 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 "sihab/slm-1.0" \
    --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": "sihab/slm-1.0",
		"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 "sihab/slm-1.0" \
        --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": "sihab/slm-1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sihab/slm-1.0 with Docker Model Runner:
```
docker model run hf.co/sihab/slm-1.0
```

SLM 1.0

SLM 1.0 is a specialized language model trained by NeuroBrain, optimized for structured output generation, JSON schema compliance, and tool calling capabilities.

Model Details

Model Description

SLM 1.0 is a language model specifically trained to excel at:

Structured Output: Generating well-formatted, structured responses
JSON Schema: Producing outputs that strictly adhere to JSON schemas
Tool Calling: Effectively utilizing and calling external tools and functions

This model has been trained by NeuroBrain to provide reliable, structured responses suitable for production applications requiring precise output formatting.

Model Specifications

Architecture: SLM1ForCausalLM
Model Type: Causal Language Model
Context Length: 32,768 tokens
Hidden Size: 1,536
Number of Layers: 28
Attention Heads: 12
Vocabulary Size: 151,936

Training Information

Trained by: NeuroBrain
Training Method: Trained for structured output, JSON schema compliance, and tool calling

Usage

Basic Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "sihab/slm-1.0"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Example: Generate structured output
prompt = "Generate a JSON object with user information"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=512)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

Structured Output Generation

SLM 1.0 is particularly effective when you need structured outputs:

prompt = """
Generate a JSON object following this schema:
{
  "name": "string",
  "age": "number",
  "email": "string"
}
"""

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=512, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

Tool Calling

The model is optimized for tool calling scenarios:

prompt = """
Available tools:
- get_weather(location: str)
- send_email(to: str, subject: str, body: str)

User request: Check the weather in Paris and send me an email with the result.
"""

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=1024)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

Model Performance

SLM 1.0 demonstrates strong performance in:

JSON schema compliance
Structured data generation
Tool calling accuracy
Function parameter extraction

Limitations

The model may occasionally require post-processing to ensure strict JSON compliance
Tool calling accuracy depends on the clarity of tool descriptions provided
Maximum context length is 32,768 tokens

Citation

If you use SLM 1.0 in your research or applications, please cite:

@misc{slm1.0,
  title={SLM 1.0: A Language Model for Structured Output and Tool Calling},
  author={NeuroBrain},
  year={2025},
  howpublished={\url{https://huggingface.co/sihab/slm-1.0}}
}

License

This model is licensed under the Apache 2.0 license.

Contact

For questions, issues, or contributions, please contact NeuroBrain.

Model trained by NeuroBrain

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Safetensors

Model size

2B params

Tensor type

F16