README.md

metadata

license: mit
language:
  - en
library_name: transformers
pipeline_tag: text-generation
tags:
  - text-generation
  - small-model
  - chat
  - fun
  - axon
  - qwen3
  - lora
  - not-finetuned
base_model: Qwen/Qwen3-1.7B
model_size:
  base_model: 1.7B
  adapter: 17M
  total: 1.7B

Axon OSS

A 1.7B parameter open-source chat model by Axon Labs. Built as a LoRA adapter on top of Qwen3-1.7B.

Note: This model is NOT fine-tuned for any specific task. It was created via LoRA adaptation and retains the general capabilities of the base Qwen3-1.7B model. It is not a custom-trained model from scratch.

Model Details

• Base model: Qwen/Qwen3-1.7B
• Parameters: ~1.7B (base) + LoRA adapter (r=16, alpha=32)
• Architecture: Qwen3 (transformer decoder) with LoRA adapter targeting all linear projections (q, k, v, o, gate, up, down)
• LoRA rank: 16
• LoRA alpha: 32
• Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
• Tokenizer: Qwen3 tokenizer with ChatML-style formatting (<|im_start|> / <|im_end|>)
• Context length: Up to 32K tokens (base model capability)
• License: MIT

Usage

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-1.7B", torch_dtype="auto", device_map="auto")
model = PeftModel.from_pretrained(base, "axonlabsai/axon-oss")
tokenizer = AutoTokenizer.from_pretrained("axonlabsai/axon-oss")

messages = [{"role": "user", "content": "Hello! What can you do?"}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
output = model.generate(inputs, max_new_tokens=256)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Limitations

• Not fine-tuned on domain-specific data — general purpose only
• Small model size means limited reasoning depth compared to larger models
• May hallucinate or produce incorrect information
• Not suitable for production deployments without further fine-tuning

About Axon Labs

Axon Labs builds AI models and tools. This is our open-source contribution — a small, lightweight model for experimentation and chat.