Instructions to use AxionLab-Co/DogeAI-v2.0-4B-Reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AxionLab-Co/DogeAI-v2.0-4B-Reasoning with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="AxionLab-Co/DogeAI-v2.0-4B-Reasoning")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AxionLab-Co/DogeAI-v2.0-4B-Reasoning")
model = AutoModelForCausalLM.from_pretrained("AxionLab-Co/DogeAI-v2.0-4B-Reasoning", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AxionLab-Co/DogeAI-v2.0-4B-Reasoning with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AxionLab-Co/DogeAI-v2.0-4B-Reasoning"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AxionLab-Co/DogeAI-v2.0-4B-Reasoning",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AxionLab-Co/DogeAI-v2.0-4B-Reasoning

SGLang

How to use AxionLab-Co/DogeAI-v2.0-4B-Reasoning with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "AxionLab-Co/DogeAI-v2.0-4B-Reasoning" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AxionLab-Co/DogeAI-v2.0-4B-Reasoning",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "AxionLab-Co/DogeAI-v2.0-4B-Reasoning" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AxionLab-Co/DogeAI-v2.0-4B-Reasoning",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AxionLab-Co/DogeAI-v2.0-4B-Reasoning with Docker Model Runner:
```
docker model run hf.co/AxionLab-Co/DogeAI-v2.0-4B-Reasoning
```

DogeAI-v2.0-4B-Reasoning / README.md

AxionLab-official

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	---
	library_name: transformers
	license: apache-2.0
	language:
	- pt
	- en
	base_model:
	- unsloth/Qwen3-4B-Base
	pipeline_tag: text-generation
	datasets:
	- nvidia/OpenMathReasoning
	- openai/gsm8k
	- microsoft/rStar-Coder
	metrics:
	- accuracy
	- code_eval
	- character
	new_version: AxionLab-Co/DogeAI-v2.0-4B-Reasoning
	---
	# 🧠 DogeAI-v2.0-4B-Reasoning
	"The Small Model That Thinks Big."

	DogeAI-v2.0-4B-Reasoning is a high-efficiency model optimized for Chain-of-Thought (CoT). Built by [AxionLab-Co](https://huggingface.co), it merges a specialized reasoning LoRA onto the powerful Qwen3-4B-Base architecture, delivering structured, step-by-step analytical capabilities in a compact 4B footprint.

	### 🚀 Key Highlights
	- Architecture: Decoder-only Transformer (Qwen3 Base).
	- Core Strength: Multi-step logical reasoning and structured problem solving.
	- Hardware Friendly: Optimized for local inference (Low VRAM usage).
	- Final Merge: No LoRA dependency; ready for production or GGUF conversion.

	---

	## 🎯 Use Cases
	- Complex Problem Solving: Math, logic, and analytical tasks.
	- Detailed Explanations: When you need the "why" and "how", not just the "what".
	- Local Agents: High-performance reasoning for edge devices and local LLM setups.

	---

	## 🛠️ Quick Start

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer
	import torch

	model_id = "AxionLab-Co/DogeAI-v2.0-4B-Reasoning"

	tokenizer = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	device_map="auto",
	torch_dtype=torch.bfloat16 # Recommended for Qwen3
	)

	prompt = "Solve this step-by-step: If a train leaves at 2 PM at 60mph, and another..."
	inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

	outputs = model.generate(
	**inputs,
	max_new_tokens=512,
	temperature=0.3, # Lower temp recommended for reasoning
	do_sample=True
	)

	print(tokenizer.decode(outputs[0], skip_special_tokens=True))

	```

	🏋️ Training & Methodology
	Our goal at AxionLab was to prioritize Depth of Thought over mere textual fluency.
	Dataset: A curated mix of synthetic CoT datasets and manually pre-processed logical reasoning prompts.
	Fine-tuning: Performed on Kaggle GPUs using PEFT (LoRA) with a focus on preserving the base model's knowledge while injecting structured logic.
	Optimization: Mixed precision (fp16) with a final merge_and_unload for seamless deployment.

	📊 Evaluation Results
	In qualitative testing, DogeAI-v2.0-4B shows:
	Higher Logical Consistency compared to the stock Qwen3-4B-Base.
	Reduced Hallucination in multi-step word problems.
	Structured Verbosity: It "thinks" before it answers.

	⚠️ Limitations & Bias
	Reasoning Loops: The model might occasionally over-explain simple tasks.
	Safety: No specific safety RLHF has been applied. Use with external safety guardrails in production.
	Factuality: While logic is improved, it can still hallucinate complex facts.

	🤝 Contact & Collaboration
	Developed with ❤️ by AxionLab-Co.
	We are an independent, community-driven lab focused on efficient AI.
	Organization: AxionLab-official
	Feedback: Open a Discussion on this repo!
	Language Support: Primarily English. Portuguese support is available but may vary in reasoning depth.