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Model: sihab/slm-1.0
Source: Original Platform

README.md

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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*