Instructions to use Amir337/llm-course-hw3-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Amir337/llm-course-hw3-lora with Transformers:

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

pipe = pipeline("text-generation", model="Amir337/llm-course-hw3-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Amir337/llm-course-hw3-lora")
model = AutoModelForCausalLM.from_pretrained("Amir337/llm-course-hw3-lora", 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 Amir337/llm-course-hw3-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Amir337/llm-course-hw3-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Amir337/llm-course-hw3-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Amir337/llm-course-hw3-lora

SGLang

How to use Amir337/llm-course-hw3-lora 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 "Amir337/llm-course-hw3-lora" \
    --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": "Amir337/llm-course-hw3-lora",
		"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 "Amir337/llm-course-hw3-lora" \
        --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": "Amir337/llm-course-hw3-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Amir337/llm-course-hw3-lora with Docker Model Runner:
```
docker model run hf.co/Amir337/llm-course-hw3-lora
```

Dora - https://huggingface.co/Amir337/llm-course-hw3-dora Qlora - https://huggingface.co/Amir337/llm-course-hw3-tinyllamma-qlora

Model Description

Lora

Examples

"QT @user In the original draft of the 7th book, Remus Lupin survived the Battle of Hogwarts. #HappyBirthdayRemusLupin" positive positive positive positive pos

"Ben Smith / Smith (concussion) remains out of the lineup Thursday, Curtis #NHL #SJ" neutral neutral neutral neutral neut

Sorry bout the stream last night I crashed out but will be on tonight for sure. Then back to Minecraft in pc tomorrow night. neutral positive positive positive pos

Chase Headley's RBI double in the 8th inning off David Price snapped a Yankees streak of 33 consecutive scoreless innings against Blue Jays neutral neutral neutral neutral neut

@user Alciato: Bee will invest 150 million in January, another 200 in the Summer and plans to bring Messi by 2017" positive neutral neutral neutral neut

Metrics

After LoRA Macro F1: 0.52

Downloads last month: 5

Safetensors

Model size

0.3B params

Tensor type

F32