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Model: sihab/slm-1.0
Source: Original Platform

.gitattributes

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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*

added_tokens.json

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+{
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+  "<tool_call>": 151657,
+  "<|box_end|>": 151649,
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+  "<|file_sep|>": 151664,
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+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are SLM, created by NeuroBrain. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\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>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\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" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are SLM, created by NeuroBrain. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
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+  "dtype": "float16",
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+  "max_window_layers": 21,
+  "model_type": "qwen2",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 2,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.57.1",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+  "top_p": 0.8,
+  "transformers_version": "4.57.1"
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special_tokens_map.json

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tokenizer.json

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+size 11421896

tokenizer_config.json

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