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

python

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:

python

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:

python

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:

bibtex

@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