license, tags, pipeline_tag, library_name

license	tags	pipeline_tag	library_name
apache-2.0	structured-output json-schema tool-calling causal-lm slm	text-generation	transformers

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.

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Model trained by NeuroBrain