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

Libraries

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

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

pipe = pipeline("text-generation", model="efromomr/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("efromomr/llm-course-hw3-lora")
model = AutoModelForCausalLM.from_pretrained("efromomr/llm-course-hw3-lora")
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 efromomr/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 "efromomr/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": "efromomr/llm-course-hw3-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

Model Card for Model ID

Model Details

Model Description

OuteAI/Lite-Oute-1-300M-Instruct finetuned on cardiffnlp/tweet_eval for sentiment-analysis task with custom LoRA.

How to Get Started with the Model

Use the code below to get started with the model.

model = AutoModelForCausalLM.from_pretrained(f"efromomr/llm-course-hw3-lora", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(f"efromomr/llm-course-hw3-lora")
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "left"

input_ids = tokenizer(text, return_tensors="pt").input_ids

output_ids = model.generate(input_ids, max_new_tokens=16)
generated_text = tokenizer.decode(output_ids[0][len(input_ids[0]) :], skip_special_tokens=True)
print(generated_text)
#positive

Training Details

Training Data

cardiffnlp/tweet_eval

Evaluation

Testing Data, Factors & Metrics

Metrics

F1: 0.49 on test set

Downloads last month: 3

Safetensors

Model size

0.3B params

Tensor type

F32

Model tree for efromomr/llm-course-hw3-lora

Base model

OuteAI/Lite-Oute-1-300M-Instruct

Finetuned

(29)

this model

Dataset used to train efromomr/llm-course-hw3-lora

Collection including efromomr/llm-course-hw3-lora

LLM_course_hw3

Collection

3 items • Updated Mar 2