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

Libraries

How to use ntyazh/llm-course-hw3-dora with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ntyazh/llm-course-hw3-dora")
model = AutoModelForCausalLM.from_pretrained("ntyazh/llm-course-hw3-dora")
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

vLLM

How to use ntyazh/llm-course-hw3-dora with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ntyazh/llm-course-hw3-dora

SGLang

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

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

Model Card for Model ID

The model was created for the task of tweet classification with 3 classes: positive tweet, neutral or negative. It is an adapter for the default TinyLlama/TinyLlama-1.1B-Chat-v1.0 and it improves f1-score on the task from 0.21 to 0.51.

Training Details

Training Data

The model was trained on cardiffnlp/tweet_eval that was created exactly for the given task -- tweet classification.

Training Procedure

The model was trained for 3 epochs, standard number for training the adapters. LR was 1e-4, batch size was the maximum possible 24. The rank for the matricies was standard 8, alpha -- 16. AdamW was used as the optimizer. DoRA layers were adapted only for v_proj and k_proj layers.

Results

f1 score was improved from 0.21 to 0.51 in only 3 epochs and with updating only ~0.15% weights of the model. Cool!

Downloads last month: 1

Safetensors

Model size

0.3B params

Tensor type

F32