Instructions to use rootxhacker/llama3-diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rootxhacker/llama3-diffusion with Transformers:

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

pipe = pipeline("text-generation", model="rootxhacker/llama3-diffusion")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rootxhacker/llama3-diffusion")
model = AutoModelForCausalLM.from_pretrained("rootxhacker/llama3-diffusion", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rootxhacker/llama3-diffusion with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rootxhacker/llama3-diffusion"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rootxhacker/llama3-diffusion",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/rootxhacker/llama3-diffusion

SGLang

How to use rootxhacker/llama3-diffusion 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 "rootxhacker/llama3-diffusion" \
    --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": "rootxhacker/llama3-diffusion",
		"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 "rootxhacker/llama3-diffusion" \
        --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": "rootxhacker/llama3-diffusion",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use rootxhacker/llama3-diffusion with Docker Model Runner:
```
docker model run hf.co/rootxhacker/llama3-diffusion
```

llama3-diffusion / README.md

rootxhacker

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	---
	license: apache-2.0
	base_model: unsloth/Meta-Llama-3.1-8B-Instruct
	tags:
	- diffusion
	- language-model
	- llama
	- text-generation
	library_name: transformers
	pipeline_tag: text-generation
	---

	# Llama-3.1-8B Diffusion Model (LAD)

	This is a Language Autoregressive Diffusion (LAD) model based on Llama-3.1-8B-Instruct.

	## Features
	- 🎯 Dual mode: Autoregressive + Diffusion generation
	- 🚀 Cosine noise schedule with 1000 timesteps
	- 🧠 LoRA fine-tuning (rank 32)
	- ⚡ Custom diffusion components

	## Usage

	```python
	from transformers import AutoTokenizer, AutoModelForCausalLM

	model = AutoModelForCausalLM.from_pretrained("rootxhacker/llama3-diffusion")
	tokenizer = AutoTokenizer.from_pretrained("rootxhacker/llama3-diffusion")

	# Generate text
	inputs = tokenizer("The future of AI", return_tensors="pt")
	outputs = model.generate(**inputs, max_length=100)
	print(tokenizer.decode(outputs[0]))
	```

	## Training Details
	- Base: Meta-Llama-3.1-8B-Instruct
	- Dataset: PatrickHaller/cosmopedia-v2-1B
	- Framework: Unsloth + Custom Diffusion
	- Context: 256 tokens
	- Training: 60% AR + 40% Diffusion

	Uploaded: 2025-06-08 23:13