sage-reasoning-8b

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<img src="images/sagea-logo.png" alt="SAGE Logo" width="75%">

SAGE Reasoning 8B

Advanced Hybrid Reasoning Model with Tool-Calling Capabilities

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Table of Contents

• Overview
• Key Features
• Evaluations
• License
• Contact

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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>

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Usage

Here is a snippet below for usage with Transformers:

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 8b 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 -

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 -

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 -

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 -

# 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:

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:

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:

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 (Llama models' default license agreement).

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Contact

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Get in Touch with Our Team

For inquiries, collaborations, or support, please reach out to us:

Email: [email protected]

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<p> <strong>SAGE Reasoning 8B</strong><br> <em>Advancing the frontier of hybrid reasoning models</em> </p>

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