Instructions to use UX4567/King-AI-Chat-v3.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use UX4567/King-AI-Chat-v3.0 with Transformers:

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

pipe = pipeline("text-generation", model="UX4567/King-AI-Chat-v3.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("UX4567/King-AI-Chat-v3.0")
model = AutoModelForCausalLM.from_pretrained("UX4567/King-AI-Chat-v3.0", 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 UX4567/King-AI-Chat-v3.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "UX4567/King-AI-Chat-v3.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "UX4567/King-AI-Chat-v3.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/UX4567/King-AI-Chat-v3.0

SGLang

How to use UX4567/King-AI-Chat-v3.0 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 "UX4567/King-AI-Chat-v3.0" \
    --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": "UX4567/King-AI-Chat-v3.0",
		"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 "UX4567/King-AI-Chat-v3.0" \
        --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": "UX4567/King-AI-Chat-v3.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use UX4567/King-AI-Chat-v3.0 with Docker Model Runner:
```
docker model run hf.co/UX4567/King-AI-Chat-v3.0
```

King-AI-Chat-v3.0 👑

King-AI-Chat-v3.0 is a powerful 3B conversational model fine-tuned from Qwen2-3B-Instruct for natural, helpful, and Hinglish-friendly chats.

Built by UX4567 with a focus on fast, low-VRAM inference and real-world chat performance.

Model Details

Developed by: UX4567
Base Model: Qwen/Qwen2-3B-Instruct
Model Size: 3.02B Parameters (BF16)
Model Type: Causal Language Model (Decoder-only)
Languages: English + Hindi/Hinglish (understands Roman Hindi perfectly)
License: Apache-2.0
Training Framework: Unsloth + Transformers + TRL

How to Use

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "UX4567/King-AI-Chat-v3.0",
    max_seq_length = 8192,
    dtype = None,
    load_in_4bit = True,
)

FastLanguageModel.for_inference(model)

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Safetensors

Model size

3B params

Tensor type

BF16

Model tree for UX4567/King-AI-Chat-v3.0

Merges

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