Dragon-1.5-0.5B (qwen2-0.5b-reasoning v3.1.1)
Dragon is a lightweight general reasoning model built upon the base Qwen2-0.5B-instruct model. It offers accurate and quick text generation on a variety of topics(including code related problems). It's small size (0.5B parameters) allows it to run comfortably on most laptop/commercial grade GPUs. This model also offers Q/A and subject matter expert capabilities on general and code related subjects.
The Dragon-1.5 is the next generation for the Dragon-1/1.5 series which incorporates high-end reasoning capabilities into the standard Qwen2 architecture.
The 0.5B variant has been SFT trained on general/code reasoning traces found here with further RL training carried out via. a GRPO algorithm. This endows the model with enhanced reasoning capabilities which allows it to serve higher quality and hallucination-free generations.
Estimated parameters: ~0.5B
Architecture: Qwen2
Intended use: Advanced reasoning, instruction following along with enhanced code snippet and long form code generation
Training data
Phase-1
- Source: deepseek-v4-distill-8000x dataset (https://huggingface.co/datasets/Jackrong/DeepSeek-V4-Distill-8000x)
- Rows: ~7,716 rows templated with a custom .jinja chat format
- Training: trained for 4,000 steps on an A10 (24GB VRAM)
Phase-2
- Source: deepseek-v4-reasoning-code-2500 dataset (https://huggingface.co/datasets/Banaxi-Tech/Deepseek-V4-Reasoning-Code-2500)
- Rows: ~7,716 rows templated with a custom .jinja chat format
- Training: trained via. GRPO for 350 steps on an A10 (24GB VRAM)
Usage
Install requirements:
pip install -r requirements.txt
pip install transformers datasets accelerate safetensors
Usage (Hugging Face Hub)
You can load it directly from HuggingFace:
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
device = "cuda" if torch.cuda.is_available() else "cpu"
model_id = "DireDreadlord/Dragon-1.5-0.5B"
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
dtype="auto"
)
model.to(device)
tokenizer = AutoTokenizer.from_pretrained(model_id)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=False)
prompt = "Solve the leetcode problem 1: two sum using the hash map technique"
input_ids = tokenizer.apply_chat_template(
[{"role": "user", "content": prompt}],
add_generation_prompt=True,
return_tensors="pt",
tokenize=True,
)["input_ids"].to(device)
output = model.generate(
input_ids,
do_sample=True,
temperature=0.4,
top_k=50,
repetition_penalty=1.05,
max_new_tokens=2048,
streamer=streamer,
)
For optimal long-form generation(with reasoning), set max_new_tokens=2048
Limitations
- Model for experimental use only; users should employ it as such under license.