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license: apache-2.0
tags:
- structured-output
- json-schema
- tool-calling
- causal-lm
- slm
pipeline_tag: text-generation
library_name: 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
```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* |