Erawan 1.3

Erawan 1.3 is a 1.7B-parameter language model fine-tuned for agentic tool calling and structured answers, developed by Airavat AI Micro Lab. It builds on Erawan 1 with further LoRA fine-tuning, merged into a standalone full-precision checkpoint. It is designed to run as the execution layer of an agentic system, with harder logic and reasoning left to a larger model.

Model Details

Developed by: Airavat AI Micro Lab
Base model: Erawan 1 (built on Qwen3-1.7B)
Parameters: 1,720,574,976
Architecture: Qwen3 decoder-only transformer, 28 layers, grouped-query attention
Fine-tuning method: successive LoRA stages (r=32), each merged into bfloat16 weights: tool use; maths, reasoning and logic; then agent workflows, handoff to a reasoning model, and maths distilled from an earlier checkpoint
Context length: 32,768 tokens, inherited from Qwen3-1.7B (the config allows up to 40,960). Fine-tuning examples were at most 1,024 tokens, so quality on long inputs has not been tested
License: Apache 2.0 (see Training Data)

Benchmark Results (Erawan 1.3 vs Erawan 1)

Task	Benchmark	Erawan 1	Erawan 1.3
Tool use	Glaive function-calling (n=200)	73/200 (36.5%)	185/200 (92.5%)
Science reasoning	ARC-Challenge (n=150)	51/150 (34.0%)	125/150 (83.3%)
Logical reading	ReClor (n=100)	49/100 (49.0%)	64/100 (64.0%)
Logic puzzles	BBH logical deduction (n=100)	23/100 (23.0%)	40/100 (40.0%)
Everyday maths	GSM8K (n=200)	121/200 (60.5%)	164/200 (82.0%)
Harder maths	MATH-500 (n=100)	35/100 (35.0%)	42/100 (42.0%)

Both models answered the same fixed questions with greedy decoding, on the same prompts and with the same token limits. These are strict scores: an answer counts only if it is correct and follows the format the prompt asked for. On MATH-500, Erawan 1 reached the 1,536-token limit on 62 of 100 answers and Erawan 1.3 on 14, so Erawan 1's score may be limited by answer length.

Tool Use in Detail

Measure	Erawan 1	Erawan 1.3
Calls using `<tool_call>` tags (of 150)	32	150
Exact function and arguments (of 150)	27	143
Correctly making no call (of 50)	46	42

Agentic Workflows

Simulated 66-scenario agent test with mocked tools, strict scoring.

Measure	Erawan 1	Erawan 1.3
Original 60 scenarios passed	10	58
Hands hard reasoning to the reasoning model (of 6)	0	6
Handoff on unseen puzzle types (of 6)	-	5
Answers simple questions without tools (of 6)	6	6
Refuses unsafe or forbidden actions (of 6)	2	6
Unneeded or forbidden tool calls	0	0

Limitations

Multiple-choice answers usually give only the letter, with no working, so the logic scores do not show reasoning steps.
ARC and ReClor questions were held out, but the model trained on those question styles, so those gains may overstate general reasoning.
MATH-500 (42/100) and logic puzzles (40/100) remain modest; hand hard reasoning to a larger model, as the model is trained to do.
Agent results come from a simulated test with mocked tools, not live systems. Validate tool calls and add confirmation steps for tools with side effects.
Two- and three-step chained tasks pass 10 of 12 scenarios; the model can occasionally drop a step.
Coding ability has not been evaluated.

Training Data

NousResearch/hermes-function-calling-v1 (Apache 2.0)
glaiveai/glaive-function-calling-v2 (Apache 2.0), skipping the rows used for benchmarking
openai/gsm8k, train split (MIT)
meta-math/MetaMathQA (MIT)
KingNish/reasoning-base-20k (Apache 2.0)
Synthetic agent, handoff and direct-answer conversations generated by Airavat AI Micro Lab
GSM8K train answers distilled from an earlier Erawan 1.3 checkpoint, kept only when correct
tasksource/ReClor, train split (licence listed as "other")
allenai/ai2_arc, ARC-Challenge train split (CC-BY-SA-4.0)

Intended Use

Execution layer for agentic tool-calling workflows, and research on small efficient models. Not intended for medical, legal, or financial decision-making, or any use requiring guaranteed factual accuracy.

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained('airavat-ai/erawan-1.3')
tokenizer = AutoTokenizer.from_pretrained('airavat-ai/erawan-1.3')
messages = [{'role': 'user', 'content': 'Who are you?'}]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors='pt', return_dict=True)
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))