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Model: sihab/slm-1.0 Source: Original Platform
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README.md
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---
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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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# 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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### 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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- **Number of Layers**: 28
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- **Attention Heads**: 12
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- **Vocabulary Size**: 151,936
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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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## 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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### 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*
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added_tokens.json
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are SLM, created by NeuroBrain. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nYou are SLM, created by NeuroBrain. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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config.json
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"max_window_layers": 21,
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"model_type": "qwen2",
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"num_attention_heads": 12,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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generation_config.json
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}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user