Erawan 1
Erawan 1 is a 1.7B-parameter language model fine-tuned for reasoning and long-horizon agentic tasks, developed by Airavat AI Micro Lab. Built on Qwen3-1.7B, fine-tuned with QLoRA and merged into a standalone full-precision checkpoint.
Model Details
- Developed by: Airavat AI Micro Lab
- Base model: Qwen3-1.7B (1,755,440,128 parameters)
- Architecture: Qwen3 decoder-only transformer, 28 layers, grouped-query attention
- Fine-tuning method: QLoRA (4-bit base, LoRA r=32) via Unsloth, merged into fp16 weights
- Training steps: 260 cumulative fine-tune steps
- Context length: 4096 tokens
- License: Apache 2.0
Benchmark Results (Erawan 1 vs base Qwen3-1.7B)
| Task | Benchmark | Base | Erawan 1 |
|---|---|---|---|
| Reasoning / Maths | GSM8K (n=10) | 1/10 | 3/10 |
| Coding / Logic | HumanEval (n=6) | 4/6 | 4/6 |
| Tool-use | Glaive function-calling (n=6) | 6/6 | 6/6 |
HumanEval vs Public Small Models
| Model | Params | HumanEval |
|---|---|---|
| Llama-3.2-1B-Instruct | 1B | 33.5% |
| SmolLM2-1.7B-Instruct | 1.7B | 28.1% |
| Qwen2.5-1.5B-Instruct | 1.5B | 30.5% |
| Qwen3-1.7B (base) | 1.7B | 67% |
| Erawan 1 | 1.7B | 67% |
Training Data
- unsloth/OpenMathReasoning-mini (~600 rows)
- NousResearch/hermes-function-calling-v1 (~900 rows)
- mlabonne/FineTome-100k (~200 rows)
- Custom identity examples
Intended Use
Agentic tool-calling workflows, step-by-step reasoning, and research on small efficient reasoning 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')
tokenizer = AutoTokenizer.from_pretrained('airavat-ai/erawan-1')
messages = [{'role': 'user', 'content': 'Who are you?'}]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors='pt')
outputs = model.generate(inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Credits
Base model: Qwen3-1.7B by the Qwen Team, Alibaba Cloud. Fine-tuning tooling: Unsloth. License: Apache 2.0.