Genesis Manthan - 1.5B

Genesis Manthan is a small language model fine-tuned to reason through tool interaction instead of verbal chain-of-thought. It is built on top of Qwen2.5-1.5B-Instruct and tuned for tool-first responses, agent workflows, and smolagents-style execution loops.

Model Summary

Base model: Qwen/Qwen2.5-1.5B-Instruct
Published model: Shahansha/Manthan-1.5B
Training recipe: QLoRA SFT -> GRPO with tool-execution rewards -> budget forcing at inference time
Primary behavior: emit structured tool calls before final answers
Intended ecosystem: Hugging Face Transformers, Gradio Spaces, smolagents, local agent runners

Why this model exists

Most small open models still answer by generating verbose text, even when the task would be better solved through an external tool. Manthan is designed around a different behavior: call a tool, observe the result, and then answer. The target is not hidden verbal reasoning. The target is reliable action traces that small models can actually execute.

spaces:

Shahansha/Manthan-Demo

Benchmark Snapshot

Benchmark	Metric	Reported Result
GSM8K	Tool-augmented accuracy	65.0
MBPP	pass@1	50.0

*Reported benchmark numbers are early project metrics and should be independently reproduced before strong claims are made.

Quickstart

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Shahansha/Manthan-1.5B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    dtype=torch.float16,
    device_map="auto",
)
model.generation_config.max_length = None

messages = [
    {
        "role": "system",
        "content": (
            "You are Genesis Manthan, an AI agent that solves problems by calling tools. "
            "Never reason verbally - always reason through tool execution."
        ),
    },
    {"role": "user", "content": "What is 144 + 256?"},
]

prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    do_sample=True,
    temperature=0.2,
)

print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False))

Expected behavior: the completion should include a <tool_call> block before the final answer.

Prompting Guidance

This model performs best when the system prompt explicitly instructs it to solve problems by calling tools. If you omit that instruction, it may drift back toward plain-text assistant behavior.

Recommended system message:

You are Genesis Manthan, an AI agent that solves problems by calling tools. Never reason verbally - always reason through tool execution.

Training Details

Base checkpoint: Qwen/Qwen2.5-1.5B-Instruct
Fine-tuning method: QLoRA SFT
Reinforcement learning: GRPO with composable rewards for tool execution, answer correctness, and format compliance
Data format: ChatML with custom tool roles and structured <tool_call> blocks
Primary training data: Shahansha/manthan-tool-reasoning-v1 plus function-calling traces derived from Glaive and Hermes datasets

Intended Use

Agentic math and reasoning tasks where external execution is available
Tool-augmented code and debugging workflows
Research experiments around small-model tool use
Gradio demos and Hugging Face Spaces showcasing action-first reasoning

Limitations

This is a research model, not a general factual authority
Reported benchmark numbers are early project metrics and should be independently reproduced before strong claims are made
The model relies heavily on the surrounding prompt and tool scaffolding
Small models can still emit malformed tool calls or conclude too early without budget forcing or downstream validation

Safety and Responsible Use

Do not treat tool-call output as inherently safe to execute without sandboxing
Validate JSON arguments and restrict available tools in production
Review outputs carefully in coding, shell, or data-execution environments
This model was not trained for high-stakes legal, medical, or safety-critical decisions

Project Links

Model: https://huggingface.co/Shahansha/Manthan-1.5B
Dataset: https://huggingface.co/datasets/Shahansha/manthan-tool-reasoning-v1
Code: https://github.com/shaik-shahansha/manthan
Deployment guide: https://github.com/shaik-shahansha/manthan/blob/main/docs/HUGGINGFACE_DEPLOY.md
Author: https://shahansha.com
Org: https://genesisagi.in

Citation

bibtex

@misc{shaik2026manthan,
    title={Genesis Manthan-1.5B: Tool-Mediated Reasoning for Small Language Models},
    author={Shahansha Shaik},
    year={2026},
    url={https://huggingface.co/Shahansha/Manthan-1.5B}
}