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sagea-ai/
sage-reasoning-14b

Name: sage-reasoning-14b
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text-generation-inference

endpoints_compatible

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SAGE Reasoning 14B

Advanced Hybrid Reasoning Model with Tool-Calling Capabilities

Overview
Key Features
Evaluations
License
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

Comprehensive benchmark results showing SAGE Reasoning 14B performance across multiple evaluation metrics

Usage

Here is a snippet below for usage with Transformers:

python

import transformers
import torch

model_id = "sagea-ai/sage-reasoning-14b"

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.

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-14b"

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-14b"

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:

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 (Llama models' default license agreement).

License

Contact

Get in Touch with Our Team

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

Email: founders@sagea.space

SAGE Reasoning 14B
Advancing the frontier of hybrid reasoning models

Made by SAGEA

sagea-ai/sage-reasoning-14b

SAGE Reasoning 14B

Table of Contents

Overview

Key Features

Hybrid Reasoning Architecture

Specialized Capabilities

Global Reach

Evaluations

Overall Performance Benchmarks

Usage

Implementing extended thinking

Method 1 - Add a specific system prompt.

Method 2 - Set enable_thinking=True in the tokenizer

Tool Calling

License

Contact

sagea-ai/
sage-reasoning-14b