| ---
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| license: apache-2.0
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| tags:
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| - structured-output
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| - json-schema
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| - tool-calling
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| - causal-lm
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| - slm
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| pipeline_tag: text-generation
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| library_name: transformers
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| ---
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|
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| # SLM 1.0
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| SLM 1.0 is a specialized language model trained by NeuroBrain, optimized for structured output generation, JSON schema compliance, and tool calling capabilities.
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| ## Model Details
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| ### Model Description
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| SLM 1.0 is a language model specifically trained to excel at:
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| - **Structured Output**: Generating well-formatted, structured responses
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| - **JSON Schema**: Producing outputs that strictly adhere to JSON schemas
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| - **Tool Calling**: Effectively utilizing and calling external tools and functions
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| This model has been trained by NeuroBrain to provide reliable, structured responses suitable for production applications requiring precise output formatting.
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|
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| ### Model Specifications
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| - **Architecture**: SLM1ForCausalLM
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| - **Model Type**: Causal Language Model
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| - **Context Length**: 32,768 tokens
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| - **Hidden Size**: 1,536
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|
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| - **Number of Layers**: 28
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| - **Attention Heads**: 12
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| - **Vocabulary Size**: 151,936
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|
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| ### Training Information
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| - **Trained by**: NeuroBrain
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| - **Training Method**: Trained for structured output, JSON schema compliance, and tool calling
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|
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| ## Usage
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| ### Basic Usage
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| ```python
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| from transformers import AutoModelForCausalLM, AutoTokenizer
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| model_name = "sihab/slm-1.0"
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| tokenizer = AutoTokenizer.from_pretrained(model_name)
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| model = AutoModelForCausalLM.from_pretrained(model_name)
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| # Example: Generate structured output
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| prompt = "Generate a JSON object with user information"
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| inputs = tokenizer(prompt, return_tensors="pt")
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| outputs = model.generate(**inputs, max_length=512)
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| response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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| ```
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|
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| ### Structured Output Generation
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| SLM 1.0 is particularly effective when you need structured outputs:
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| ```python
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| prompt = """
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| Generate a JSON object following this schema:
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| {
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| "name": "string",
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| "age": "number",
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| "email": "string"
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| }
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| """
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| inputs = tokenizer(prompt, return_tensors="pt")
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| outputs = model.generate(**inputs, max_length=512, temperature=0.7)
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| response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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| ```
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| ### Tool Calling
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| The model is optimized for tool calling scenarios:
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| ```python
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| prompt = """
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| Available tools:
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| - get_weather(location: str)
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| - send_email(to: str, subject: str, body: str)
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| User request: Check the weather in Paris and send me an email with the result.
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| """
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| inputs = tokenizer(prompt, return_tensors="pt")
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| outputs = model.generate(**inputs, max_length=1024)
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| response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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| ```
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| ## Model Performance
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| SLM 1.0 demonstrates strong performance in:
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| - JSON schema compliance
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| - Structured data generation
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| - Tool calling accuracy
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| - Function parameter extraction
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| ## Limitations
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| - The model may occasionally require post-processing to ensure strict JSON compliance
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| - Tool calling accuracy depends on the clarity of tool descriptions provided
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| - Maximum context length is 32,768 tokens
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| ## Citation
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| If you use SLM 1.0 in your research or applications, please cite:
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| ```bibtex
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| @misc{slm1.0,
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| title={SLM 1.0: A Language Model for Structured Output and Tool Calling},
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| author={NeuroBrain},
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| year={2025},
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| howpublished={\url{https://huggingface.co/sihab/slm-1.0}}
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| }
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| ```
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| ## License
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| This model is licensed under the Apache 2.0 license.
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| ## Contact
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| For questions, issues, or contributions, please contact NeuroBrain.
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| ---
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| *Model trained by NeuroBrain* |
