Instructions to use shahidul034/KUETLLM_zephyr_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shahidul034/KUETLLM_zephyr_base with Transformers:

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

pipe = pipeline("text-generation", model="shahidul034/KUETLLM_zephyr_base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("shahidul034/KUETLLM_zephyr_base")
model = AutoModelForCausalLM.from_pretrained("shahidul034/KUETLLM_zephyr_base")
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 shahidul034/KUETLLM_zephyr_base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shahidul034/KUETLLM_zephyr_base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shahidul034/KUETLLM_zephyr_base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/shahidul034/KUETLLM_zephyr_base

SGLang

How to use shahidul034/KUETLLM_zephyr_base 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 "shahidul034/KUETLLM_zephyr_base" \
    --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": "shahidul034/KUETLLM_zephyr_base",
		"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 "shahidul034/KUETLLM_zephyr_base" \
        --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": "shahidul034/KUETLLM_zephyr_base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use shahidul034/KUETLLM_zephyr_base with Docker Model Runner:
```
docker model run hf.co/shahidul034/KUETLLM_zephyr_base
```

KUETLLM is a zephyr7b-beta finetune, using a dataset with prompts and answers about Khulna University of Engineering and Technology. It was loaded in 8 bit quantization using bitsandbytes. LORA was used to finetune an adapter, which was leter merged with the base unquantized model.

datasets:

University information (collected from website, https://kuet.ac.bd/)

Below are the training configurations for the fine-tuning process:

LoraConfig:
r=16,
lora_alpha=16,
target_modules=["q_proj", "v_proj","k_proj","o_proj","gate_proj","up_proj","down_proj"],
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM"

TrainingArguments:
per_device_train_batch_size=12,
gradient_accumulation_steps=1,
optim='paged_adamw_8bit',
learning_rate=5e-06 ,
fp16=True,            
logging_steps=10,
num_train_epochs = 1,
output_dir=zephyr_lora_output,
remove_unused_columns=False,

Inferencing:

def process_data_sample(example):
    processed_example = "<|system|>\nYou are a KUET authority managed chatbot, help users by answering their queries about KUET.\n<|user|>\n" + example + "\n<|assistant|>\n"
    return processed_example

inp_str = process_data_sample("Tell me about KUET.")
inputs = tokenizer(inp_str, return_tensors="pt")
generation_config = GenerationConfig(
    do_sample=True,
    top_k=1,
    temperature=0.1,
    max_new_tokens=256,
    pad_token_id=tokenizer.eos_token_id
)

outputs = model.generate(**inputs, generation_config=generation_config)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Safetensors

Model size

7B params

Tensor type

F32