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Model: Shahansha/Manthan-1.5B Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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license: apache-2.0
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base_model: Qwen/Qwen2.5-1.5B-Instruct
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base_model_relation: finetune
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- genesis-agi
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- manthan
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- qwen2
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- tool-calling
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- agent
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- reasoning
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- grpo
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- qlora
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- chatml
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- smolagents
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datasets:
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- Shahansha/manthan-tool-reasoning-v1
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- glaiveai/glaive-function-calling-v2
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- NousResearch/hermes-function-calling-v1
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metrics:
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- accuracy
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- pass@1
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model-index:
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-
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name: Manthan-1.5B
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results:
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-
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task:
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type: text-generation
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name: Tool-Augmented Generation
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dataset:
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name: GSM8K
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type: gsm8k
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metrics:
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-
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name: Tool-Augmented Accuracy
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type: accuracy
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value: 65.0
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-
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task:
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type: text-generation
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name: Code Generation
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dataset:
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name: MBPP
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type: mbpp
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metrics:
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-
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name: pass@1
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type: pass@1
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value: 50.0
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---
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# Genesis Manthan - 1.5B
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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.
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## Model Summary
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- Base model: `Qwen/Qwen2.5-1.5B-Instruct`
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- Published model: `Shahansha/Manthan-1.5B`
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- Training recipe: QLoRA SFT -> GRPO with tool-execution rewards -> budget forcing at inference time
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- Primary behavior: emit structured tool calls before final answers
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- Intended ecosystem: Hugging Face Transformers, Gradio Spaces, smolagents, local agent runners
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## Why this model exists
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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.
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spaces:
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- Shahansha/Manthan-Demo
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## Benchmark Snapshot
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| Benchmark | Metric | Reported Result |
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|---|---:|---:|
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| GSM8K | Tool-augmented accuracy | 65.0 |
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| MBPP | pass@1 | 50.0 |
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*Reported benchmark numbers are early project metrics and should be independently reproduced before strong claims are made.
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## Quickstart
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "Shahansha/Manthan-1.5B"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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dtype=torch.float16,
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device_map="auto",
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)
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model.generation_config.max_length = None
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messages = [
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{
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"role": "system",
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"content": (
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"You are Genesis Manthan, an AI agent that solves problems by calling tools. "
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"Never reason verbally - always reason through tool execution."
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),
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},
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{"role": "user", "content": "What is 144 + 256?"},
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]
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prompt = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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)
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(
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**inputs,
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max_new_tokens=256,
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do_sample=True,
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temperature=0.2,
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)
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print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False))
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```
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Expected behavior: the completion should include a `<tool_call>` block before the final answer.
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## Prompting Guidance
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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.
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Recommended system message:
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```text
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You are Genesis Manthan, an AI agent that solves problems by calling tools. Never reason verbally - always reason through tool execution.
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```
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## Training Details
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- Base checkpoint: `Qwen/Qwen2.5-1.5B-Instruct`
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- Fine-tuning method: QLoRA SFT
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- Reinforcement learning: GRPO with composable rewards for tool execution, answer correctness, and format compliance
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- Data format: ChatML with custom tool roles and structured `<tool_call>` blocks
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- Primary training data: `Shahansha/manthan-tool-reasoning-v1` plus function-calling traces derived from Glaive and Hermes datasets
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## Intended Use
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- Agentic math and reasoning tasks where external execution is available
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- Tool-augmented code and debugging workflows
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- Research experiments around small-model tool use
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- Gradio demos and Hugging Face Spaces showcasing action-first reasoning
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## Limitations
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- This is a research model, not a general factual authority
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- Reported benchmark numbers are early project metrics and should be independently reproduced before strong claims are made
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- The model relies heavily on the surrounding prompt and tool scaffolding
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- Small models can still emit malformed tool calls or conclude too early without budget forcing or downstream validation
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## Safety and Responsible Use
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- Do not treat tool-call output as inherently safe to execute without sandboxing
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- Validate JSON arguments and restrict available tools in production
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- Review outputs carefully in coding, shell, or data-execution environments
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- This model was not trained for high-stakes legal, medical, or safety-critical decisions
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## Project Links
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- Model: https://huggingface.co/Shahansha/Manthan-1.5B
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- Dataset: https://huggingface.co/datasets/Shahansha/manthan-tool-reasoning-v1
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- Code: https://github.com/shaik-shahansha/manthan
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- Deployment guide: https://github.com/shaik-shahansha/manthan/blob/main/docs/HUGGINGFACE_DEPLOY.md
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- Author: https://shahansha.com
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- Org: https://genesisagi.in
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## Citation
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```bibtex
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@misc{shaik2026manthan,
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title={Genesis Manthan-1.5B: Tool-Mediated Reasoning for Small Language Models},
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author={Shahansha Shaik},
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year={2026},
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url={https://huggingface.co/Shahansha/Manthan-1.5B}
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}
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```
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---
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