Instructions to use ijktech/ByteGPT-r1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ijktech/ByteGPT-r1 with Transformers:

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

pipe = pipeline("text-generation", model="ijktech/ByteGPT-r1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ijktech/ByteGPT-r1")
model = AutoModelForCausalLM.from_pretrained("ijktech/ByteGPT-r1", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ijktech/ByteGPT-r1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ijktech/ByteGPT-r1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ijktech/ByteGPT-r1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ijktech/ByteGPT-r1

SGLang

How to use ijktech/ByteGPT-r1 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 "ijktech/ByteGPT-r1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ijktech/ByteGPT-r1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "ijktech/ByteGPT-r1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ijktech/ByteGPT-r1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ijktech/ByteGPT-r1 with Docker Model Runner:
```
docker model run hf.co/ijktech/ByteGPT-r1
```

ByteGPT-r1 / README.md

ijktech-jk

Update with README

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	---
	library_name: transformers
	tags:
	- gpt
	- distillation
	- mobile
	- embedded
	- onnx
	license: cc-by-nc-4.0
	datasets:
	- custom
	- web
	language: en
	widget:
	- text: "In order to make pancakes, you need to"
	- text: "Once upon a time"
	---

	<p align="center">
	<img src="logo.png" alt="IJK Technology" width="150">
	</p>

	<h1 align="center">IJK Technology – ByteGPT-r1</h1>


	ByteGPT-r1 is a distilled version of DeepSeek's QWEN 1.5B model, optimized specifically for mobile and edge computing environments. It maintains impressive language capabilities while being designed for compute- and memory-constrained devices.

	## 🚀 Overview
	- Model Type: Distilled GPT-style causal language model
	- Base Model: DeepSeek's QWEN 1.5B
	- Intended Use: Edge devices, mobile phones, embedded systems
	- Size: Optimized for mobile deployment
	- Training: Knowledge distillation from QWEN 1.5B

	## 🧠 Why ByteGPT-r1?
	ByteGPT-r1 offers several advantages for mobile and edge deployment:

	1. Efficient Knowledge Distillation:
	Carefully distilled from DeepSeek's QWEN 1.5B model to preserve capabilities while reducing computational requirements.

	2. Mobile-First Design:
	Architected specifically for the constraints of mobile devices, with optimizations for both inference speed and memory usage.

	3. Balanced Performance:
	Maintains a good balance between model size and language generation capabilities, making it practical for real-world mobile applications.

	## 💡 Future Plans
	This model is part of our ongoing effort to bring powerful language models to edge devices. Upcoming releases will include:

	- Specialized Variants: Domain-specific versions optimized for particular use cases
	- Further Optimizations: Continued improvements in efficiency and performance
	- Benchmark Results: Comparative performance on various mobile devices
	- Integration Examples: More code samples for popular mobile frameworks

	## 💻 Usage

	### Quick Start (with `transformers`):
	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model = AutoModelForCausalLM.from_pretrained("ijktech/ByteGPT-r1", trust_remote_code=True)
	tokenizer = AutoTokenizer.from_pretrained("ijktech/ByteGPT-r1")

	input_text = "What is the capital of France?"
	inputs = tokenizer(input_text, return_tensors="pt")
	outputs = model.generate(**inputs, max_new_tokens=100)

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

	### Tokenizer

	The tokenizer is compatible with AutoTokenizer from Hugging Face:

	```python
	tokenizer = AutoTokenizer.from_pretrained("ijktech/ByteGPT-r1")
	```

	### ONNX

	The model is also available in ONNX format, and can be used with the ONNX Runtime:

	```python
	import onnxruntime as ort
	import numpy as np

	# Create ONNX Runtime session
	ort_session = ort.InferenceSession("model.onnx")

	# Helper function to generate text using the ONNX model
	def generate_with_onnx(prompt_ids, max_new_tokens=50, temperature=1.0):
	input_ids = prompt_ids.clone()

	for _ in range(max_new_tokens):
	# Get the last block_size tokens if input is too long
	if input_ids.shape[1] > model.block_size:
	input_ids = input_ids[:, -model.block_size:]

	# Run inference
	ort_inputs = {
	'input': input_ids.cpu().numpy()
	}
	logits = ort_session.run(None, ort_inputs)[0]

	# Get predictions for the next token
	logits = torch.from_numpy(logits)
	logits = logits[:, -1, :] # Only take the last token's predictions

	# Apply temperature
	if temperature != 1.0:
	logits = logits / temperature

	# Sample from the distribution
	probs = torch.nn.functional.softmax(logits, dim=-1)
	next_token = torch.multinomial(probs, num_samples=1)

	# Append the new token
	input_ids = torch.cat([input_ids, next_token], dim=1)

	return input_ids

	# Test the generation
	prompt = "Hello"
	prompt_ids = tok(prompt, return_tensors="pt")["input_ids"]
	generated_ids = generate_with_onnx(prompt_ids)
	generated_text = tok.decode(generated_ids[0], skip_special_tokens=True)
	print(f"Generated text: {generated_text}")
	#Generated text: Hello there! How can I assist you today? I'm a helpful AI assistant trained to provide information and answer questions on a wide range of topics.
	```

	### Android Usage

	Coming Soon!


	### iOS Usage

	Coming Soon!


	## 📜 License
	📍 CC-BY-NC-4.0: Free for non-commercial use.

	💼 Commercial Use: Contact IJK Technology Ltd for licensing at [james@ijktech.com](mailto:james@ijktech.com).

	## 🛠️ About IJK Technology Ltd
	IJK Technology Ltd (IJKTech) develops innovative machine learning models optimized for on-device inference. Our focus is on efficiency, privacy, and usability across mobile and embedded platforms.