fixed: readme

README.md

@@ -1,319 +1,319 @@
----
-license: llama3.2
-library_name: transformers
-pipeline_tag: text-generation
-language:
-- en
-- ko
-- fr
-- zh
-- es
----
-
-<div align="center">
-
-![License](https://img.shields.io/badge/License-Llama%203.2-blue.svg)
-![Model](https://img.shields.io/badge/Model-3B%20Parameters-green.svg)
-![Library](https://img.shields.io/badge/Library-Transformers-orange.svg)
-![Pipeline](https://img.shields.io/badge/Pipeline-Text%20Generation-red.svg)
-![Languages](https://img.shields.io/badge/Languages-30+-purple.svg)
-![Context](https://img.shields.io/badge/Context%20Window-128k-brightgreen.svg)
-
-<img src="images/sagea-logo.png" alt="SAGE Logo" width="75%">
-
-# SAGE Reasoning 8B
-
-*Advanced Hybrid Reasoning Model with Tool-Calling Capabilities*
-
-[![Open in HuggingFace](https://img.shields.io/badge/🤗%20Hugging%20Face-Open%20Model-yellow)](https://huggingface.co/)
-[![License](https://img.shields.io/badge/License-Llama%203.2%20Community-blue)](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE)
-
-</div>
-
----
-
-## Table of Contents
-
-- [Overview](#overview)
-- [Key Features](#key-features)
-- [Evaluations](#evaluations)
-- [License](#license)
-- [Contact](#contact)
-
----
-
-## Overview
-
-SAGE Reasoning Family Models are instruction-tuned, text-in/text-out generative systems released under a permissive open license for commercial use.
-
-## Key Features
-
-### **Hybrid Reasoning Architecture**
-- **Dual Mode Operation**: Capable of producing fast direct responses in standard LLM mode, or applying self-reflection before answering in reasoning mode
-- **Advanced Training**: Uses **Iterated Distillation and Amplification (IDA)** - a scalable alignment method based on iterative self-improvement
-
-### **Specialized Capabilities**
-- **Code Generation**: Optimized for programming tasks with strong coding abilities
-- **STEM Excellence**: Enhanced performance on science, technology, engineering, and mathematics problems
-- **Instruction Following**: Superior adherence to complex instructions and prompts
-- **Tool Calling**: Notable strength in tool-calling ability compared to similar-sized models
-
-### **Global Reach**
-- **Multilingual Support**: Over 30 languages supported
-- **Extended Context**: 128k context window for handling large documents and conversations
-- **Consistent Performance**: Both standard and reasoning variants consistently outperform other models in the same parameter class on public benchmarks
-
-## Evaluations
-
-We compare our models against state-of-the-art size-equivalent models in both direct mode and reasoning mode. For direct mode, we compare against Llama/Qwen instruct counterparts. For reasoning, we use Deepseek's R1 distilled counterparts and Qwen's QwQ model.
-
-### Overall Performance Benchmarks
-
-<div align="center">
-    <img src="images/3b_benchmarks.png" alt="Overall Performance Benchmarks" width="85%">
-    <p><em>Comprehensive benchmark results showing SAGE Reasoning 3B performance across multiple evaluation metrics</em></p>
-</div>
-
-### Livebench Global Average
-
-<div align="center">
-    <img src="images/3b_8b_tools.png" alt="Livebench Global Average Performance" width="75%">
-    <p><em>Livebench global performance comparison demonstrating consistent superiority</em></p>
-</div>
-
-### Tool Calling Performance
-
-<div align="center">
-    <img src="images/3b_8b_tool_calling_benchmarks.png" alt="Tool Calling Benchmarks" width="85%">
-    <p><em>Tool calling capabilities comparison showing enhanced performance in function calling and tool utilization</em></p>
-</div>
-
----
-
-
-# Usage
-Here is a snippet below for usage with Transformers:
-
-```python
-import transformers
-import torch
-
-model_id = "sagea-ai/sage-reasoning-3b"
-
-pipeline = transformers.pipeline(
-    "text-generation",
-    model=model_id,
-    model_kwargs={"torch_dtype": torch.bfloat16},
-    device_map="auto",
-)
-
-messages = [
-    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
-    {"role": "user", "content": "Give me a short introduction to LLMs."},
-]
-
-outputs = pipeline(
-    messages,
-    max_new_tokens=512,
-)
-
-print(outputs[0]["generated_text"][-1])
-```
-
-
-
-## Implementing extended thinking
-- By default, the model will answer in the standard mode. 
-- To enable thinking, you can do any one of the two methods:
-  - Add a specific system prompt, or 
-  - Set `enable_thinking=True` while applying the chat template.
-
-> **_NOTE:_**  For the SAGE reasoning 3b model, we suggest using `repetition_penalty=1.1` while implementing extended thinking.
-
-### Method 1 - Add a specific system prompt.
-To enable thinking, simply use this in the system prompt `system_instruction = 'Enable deep thinking subroutine.'`
-
-If you already have a system_instruction, then use `system_instruction = 'Enable deep thinking subroutine.' + '\n\n' + system_instruction`.
-
-Here is an example - 
-
-```python
-import transformers
-import torch
-
-model_id = "sagea-ai/sage-reasoning-3b"
-
-pipeline = transformers.pipeline(
-    "text-generation",
-    model=model_id,
-    model_kwargs={"torch_dtype": torch.bfloat16},
-    device_map="auto",
-)
-
-DEEP_THINKING_INSTRUCTION = "Enable deep thinking subroutine."
-
-messages = [
-    {"role": "system", "content": DEEP_THINKING_INSTRUCTION},
-    {"role": "user", "content": "Write a bash script that takes a matrix represented as a string with format '[1,2],[3,4],[5,6]' and prints the transpose in the same format."},
-]
-
-outputs = pipeline(
-    messages,
-    max_new_tokens=512,
-)
-
-print(outputs[0]["generated_text"][-1])
-```
-
-
-Similarly, if you have a system prompt, you can append the `DEEP_THINKING_INSTRUCTION` to the beginning in this way - 
-
-```python
-DEEP_THINKING_INSTRUCTION = "Enable deep thinking subroutine."
-
-system_prompt = "Reply to each prompt with only the actual code - no explanations."
-prompt = "Write a bash script that takes a matrix represented as a string with format '[1,2],[3,4],[5,6]' and prints the transpose in the same format."
-
-messages = [
-    {"role": "system", "content": DEEP_THINKING_INSTRUCTION + '\n\n' + system_prompt},
-    {"role": "user", "content": prompt}
-]
-```
-
-### Method 2 - Set enable_thinking=True in the tokenizer
-If you are using Huggingface tokenizers, then you can simply use add the argument `enable_thinking=True` to the tokenization (this option is added to the chat template).
-
-Here is an example - 
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_name = "sagea-ai/sage-reasoning-3b"
-
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype="auto",
-    device_map="auto"
-)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-prompt = "Give me a short introduction to LLMs."
-messages = [
-    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
-    {"role": "user", "content": prompt}
-]
-
-text = tokenizer.apply_chat_template(
-    messages,
-    tokenize=False,
-    add_generation_prompt=True,
-    enable_thinking=True
-)
-model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
-
-generated_ids = model.generate(
-    **model_inputs,
-    max_new_tokens=512
-)
-generated_ids = [
-    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
-]
-
-response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
-print(response)
-```
-
-# Tool Calling
-SAGE reasoning 3b models support tool calling (single, parallel, multiple and parallel_multiple) both in standard and extended thinking mode.
-
-Here is a snippet -
-
-```python
-# First, define a tool
-def get_current_temperature(location: str) -> float:
-    """
-    Get the current temperature at a location.
-    
-    Args:
-        location: The location to get the temperature for, in the format "City, Country"
-    Returns:
-        The current temperature at the specified location in the specified units, as a float.
-    """
-    return 22.  # A real function should probably actually get the temperature!
-
-# Next, create a chat and apply the chat template
-messages = [
-  {"role": "user", "content": "Hey, what's the temperature in Paris right now?"}
-]
-
-model_inputs = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True)
-
-text = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True, tokenize=False)
-inputs = tokenizer(text, return_tensors="pt", add_special_tokens=False).to(model.device)
-outputs = model.generate(**inputs, max_new_tokens=512)
-output_text = tokenizer.batch_decode(outputs)[0][len(text):]
-print(output_text)
-```
-
-This will result in the output - 
-```
-<tool_call>
-{"name": "get_current_temperature", "arguments": {"location": "Paris, France"}}
-</tool_call><|eot_id|>
-```
-
-You can then generate text from this input as normal. If the model generates a tool call, you should add it to the chat like so:
-
-```python
-tool_call = {"name": "get_current_temperature", "arguments": {"location": "Paris, France"}}
-messages.append({"role": "assistant", "tool_calls": [{"type": "function", "function": tool_call}]})
-```
-
-and then call the tool and append the result, with the `tool` role, like so:
-
-```python
-messages.append({"role": "tool", "name": "get_current_temperature", "content": "22.0"})
-```
-
-After that, you can `generate()` again to let the model use the tool result in the chat:
-
-```python
-text = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True, tokenize=False)
-inputs = tokenizer(text, return_tensors="pt", add_special_tokens=False).to(model.device)
-outputs = model.generate(**inputs, max_new_tokens=512)
-output_text = tokenizer.batch_decode(outputs)[0][len(text):]
-```
-
-This should result in the string -
-
-'The current temperature in Paris is 22.0 degrees.<|eot_id|>'
-
-## License
-
-This repository and the model weights are licensed under the [**Llama 3.2 Community License Agreement**](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (Llama models' default license agreement).
-
-<div align="center">
-
-[![License](https://img.shields.io/badge/License-Llama%203.2%20Community-blue.svg)](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE)
-
-</div>
-
-## Contact
-
-<div align="center">
-
-**Get in Touch with Our Team**
-
-For inquiries, collaborations, or support, please reach out to us:
-
-**Email**: [founders@sagea.space](mailto:founders@sagea.space)
-
----
-
-<p>
-    <strong>SAGE Reasoning 3B</strong><br>
-    <em>Advancing the frontier of hybrid reasoning models</em>
-</p>
-
-![Made by SAGEA](https://img.shields.io/badge/Made%20by-SAGEA%20-red.svg)
-
+---
+license: llama3.2
+library_name: transformers
+pipeline_tag: text-generation
+language:
+- en
+- ko
+- fr
+- zh
+- es
+---
+
+<div align="center">
+
+![License](https://img.shields.io/badge/License-Llama%203.2-blue.svg)
+![Model](https://img.shields.io/badge/Model-8B%20Parameters-green.svg)
+![Library](https://img.shields.io/badge/Library-Transformers-orange.svg)
+![Pipeline](https://img.shields.io/badge/Pipeline-Text%20Generation-red.svg)
+![Languages](https://img.shields.io/badge/Languages-30+-purple.svg)
+![Context](https://img.shields.io/badge/Context%20Window-128k-brightgreen.svg)
+
+<img src="images/sagea-logo.png" alt="SAGE Logo" width="75%">
+
+# SAGE Reasoning 8B
+
+*Advanced Hybrid Reasoning Model with Tool-Calling Capabilities*
+
+[![Open in HuggingFace](https://img.shields.io/badge/🤗%20Hugging%20Face-Open%20Model-yellow)](https://huggingface.co/)
+[![License](https://img.shields.io/badge/License-Llama%203.2%20Community-blue)](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE)
+
+</div>
+
+---
+
+## Table of Contents
+
+- [Overview](#overview)
+- [Key Features](#key-features)
+- [Evaluations](#evaluations)
+- [License](#license)
+- [Contact](#contact)
+
+---
+
+## Overview
+
+SAGE Reasoning Family Models are instruction-tuned, text-in/text-out generative systems released under a permissive open license for commercial use.
+
+## Key Features
+
+### **Hybrid Reasoning Architecture**
+- **Dual Mode Operation**: Capable of producing fast direct responses in standard LLM mode, or applying self-reflection before answering in reasoning mode
+- **Advanced Training**: Uses **Iterated Distillation and Amplification (IDA)** - a scalable alignment method based on iterative self-improvement
+
+### **Specialized Capabilities**
+- **Code Generation**: Optimized for programming tasks with strong coding abilities
+- **STEM Excellence**: Enhanced performance on science, technology, engineering, and mathematics problems
+- **Instruction Following**: Superior adherence to complex instructions and prompts
+- **Tool Calling**: Notable strength in tool-calling ability compared to similar-sized models
+
+### **Global Reach**
+- **Multilingual Support**: Over 30 languages supported
+- **Extended Context**: 128k context window for handling large documents and conversations
+- **Consistent Performance**: Both standard and reasoning variants consistently outperform other models in the same parameter class on public benchmarks
+
+## Evaluations
+
+We compare our models against state-of-the-art size-equivalent models in both direct mode and reasoning mode. For direct mode, we compare against Llama/Qwen instruct counterparts. For reasoning, we use Deepseek's R1 distilled counterparts and Qwen's QwQ model.
+
+### Overall Performance Benchmarks
+
+<div align="center">
+    <img src="images/8b_benchmarks.png" alt="Overall Performance Benchmarks" width="85%">
+    <p><em>Comprehensive benchmark results showing SAGE Reasoning 3B performance across multiple evaluation metrics</em></p>
+</div>
+
+### Livebench Global Average
+
+<div align="center">
+    <img src="images/3b_8b_tools.png" alt="Livebench Global Average Performance" width="75%">
+    <p><em>Livebench global performance comparison demonstrating consistent superiority</em></p>
+</div>
+
+### Tool Calling Performance
+
+<div align="center">
+    <img src="images/3b_8b_tool_calling_benchmarks (1).png" alt="Tool Calling Benchmarks" width="85%">
+    <p><em>Tool calling capabilities comparison showing enhanced performance in function calling and tool utilization</em></p>
+</div>
+
+---
+
+
+# Usage
+Here is a snippet below for usage with Transformers:
+
+```python
+import transformers
+import torch
+
+model_id = "sagea-ai/sage-reasoning-8b"
+
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model_id,
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device_map="auto",
+)
+
+messages = [
+    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
+    {"role": "user", "content": "Give me a short introduction to LLMs."},
+]
+
+outputs = pipeline(
+    messages,
+    max_new_tokens=512,
+)
+
+print(outputs[0]["generated_text"][-1])
+```
+
+
+
+## Implementing extended thinking
+- By default, the model will answer in the standard mode. 
+- To enable thinking, you can do any one of the two methods:
+  - Add a specific system prompt, or 
+  - Set `enable_thinking=True` while applying the chat template.
+
+> **_NOTE:_**  For the SAGE reasoning 3b model, we suggest using `repetition_penalty=1.1` while implementing extended thinking.
+
+### Method 1 - Add a specific system prompt.
+To enable thinking, simply use this in the system prompt `system_instruction = 'Enable deep thinking subroutine.'`
+
+If you already have a system_instruction, then use `system_instruction = 'Enable deep thinking subroutine.' + '\n\n' + system_instruction`.
+
+Here is an example - 
+
+```python
+import transformers
+import torch
+
+model_id = "sagea-ai/sage-reasoning-8b"
+
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model_id,
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device_map="auto",
+)
+
+DEEP_THINKING_INSTRUCTION = "Enable deep thinking subroutine."
+
+messages = [
+    {"role": "system", "content": DEEP_THINKING_INSTRUCTION},
+    {"role": "user", "content": "Write a bash script that takes a matrix represented as a string with format '[1,2],[3,4],[5,6]' and prints the transpose in the same format."},
+]
+
+outputs = pipeline(
+    messages,
+    max_new_tokens=512,
+)
+
+print(outputs[0]["generated_text"][-1])
+```
+
+
+Similarly, if you have a system prompt, you can append the `DEEP_THINKING_INSTRUCTION` to the beginning in this way - 
+
+```python
+DEEP_THINKING_INSTRUCTION = "Enable deep thinking subroutine."
+
+system_prompt = "Reply to each prompt with only the actual code - no explanations."
+prompt = "Write a bash script that takes a matrix represented as a string with format '[1,2],[3,4],[5,6]' and prints the transpose in the same format."
+
+messages = [
+    {"role": "system", "content": DEEP_THINKING_INSTRUCTION + '\n\n' + system_prompt},
+    {"role": "user", "content": prompt}
+]
+```
+
+### Method 2 - Set enable_thinking=True in the tokenizer
+If you are using Huggingface tokenizers, then you can simply use add the argument `enable_thinking=True` to the tokenization (this option is added to the chat template).
+
+Here is an example - 
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "sagea-ai/sage-reasoning-8b"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Give me a short introduction to LLMs."
+messages = [
+    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
+    {"role": "user", "content": prompt}
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+    enable_thinking=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```
+
+# Tool Calling
+SAGE reasoning models support tool calling (single, parallel, multiple and parallel_multiple) both in standard and extended thinking mode.
+
+Here is a snippet -
+
+```python
+# First, define a tool
+def get_current_temperature(location: str) -> float:
+    """
+    Get the current temperature at a location.
+    
+    Args:
+        location: The location to get the temperature for, in the format "City, Country"
+    Returns:
+        The current temperature at the specified location in the specified units, as a float.
+    """
+    return 22.  # A real function should probably actually get the temperature!
+
+# Next, create a chat and apply the chat template
+messages = [
+  {"role": "user", "content": "Hey, what's the temperature in Paris right now?"}
+]
+
+model_inputs = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True)
+
+text = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True, tokenize=False)
+inputs = tokenizer(text, return_tensors="pt", add_special_tokens=False).to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=512)
+output_text = tokenizer.batch_decode(outputs)[0][len(text):]
+print(output_text)
+```
+
+This will result in the output - 
+```
+<tool_call>
+{"name": "get_current_temperature", "arguments": {"location": "Paris, France"}}
+</tool_call><|eot_id|>
+```
+
+You can then generate text from this input as normal. If the model generates a tool call, you should add it to the chat like so:
+
+```python
+tool_call = {"name": "get_current_temperature", "arguments": {"location": "Paris, France"}}
+messages.append({"role": "assistant", "tool_calls": [{"type": "function", "function": tool_call}]})
+```
+
+and then call the tool and append the result, with the `tool` role, like so:
+
+```python
+messages.append({"role": "tool", "name": "get_current_temperature", "content": "22.0"})
+```
+
+After that, you can `generate()` again to let the model use the tool result in the chat:
+
+```python
+text = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True, tokenize=False)
+inputs = tokenizer(text, return_tensors="pt", add_special_tokens=False).to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=512)
+output_text = tokenizer.batch_decode(outputs)[0][len(text):]
+```
+
+This should result in the string -
+
+'The current temperature in Paris is 22.0 degrees.<|eot_id|>'
+
+## License
+
+This repository and the model weights are licensed under the [**Llama 3.2 Community License Agreement**](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (Llama models' default license agreement).
+
+<div align="center">
+
+[![License](https://img.shields.io/badge/License-Llama%203.2%20Community-blue.svg)](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE)
+
+</div>
+
+## Contact
+
+<div align="center">
+
+**Get in Touch with Our Team**
+
+For inquiries, collaborations, or support, please reach out to us:
+
+**Email**: [founders@sagea.space](mailto:founders@sagea.space)
+
+---
+
+<p>
+    <strong>SAGE Reasoning 3B</strong><br>
+    <em>Advancing the frontier of hybrid reasoning models</em>
+</p>
+
+![Made by SAGEA](https://img.shields.io/badge/Made%20by-SAGEA%20-red.svg)
+
 </div>