Instructions to use jaydeepb/qwen2.5-1.5b-code-run2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jaydeepb/qwen2.5-1.5b-code-run2 with Transformers:

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

pipe = pipeline("text-generation", model="jaydeepb/qwen2.5-1.5b-code-run2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jaydeepb/qwen2.5-1.5b-code-run2")
model = AutoModelForCausalLM.from_pretrained("jaydeepb/qwen2.5-1.5b-code-run2", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jaydeepb/qwen2.5-1.5b-code-run2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jaydeepb/qwen2.5-1.5b-code-run2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jaydeepb/qwen2.5-1.5b-code-run2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jaydeepb/qwen2.5-1.5b-code-run2

SGLang

How to use jaydeepb/qwen2.5-1.5b-code-run2 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 "jaydeepb/qwen2.5-1.5b-code-run2" \
    --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": "jaydeepb/qwen2.5-1.5b-code-run2",
		"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 "jaydeepb/qwen2.5-1.5b-code-run2" \
        --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": "jaydeepb/qwen2.5-1.5b-code-run2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jaydeepb/qwen2.5-1.5b-code-run2 with Docker Model Runner:
```
docker model run hf.co/jaydeepb/qwen2.5-1.5b-code-run2
```

qwen2.5-1.5b fine-tuned on The Stack (code) (Run 2)

Research artifact from Predicting Memorization Before Fine-Tuning Language Models.

This is Qwen/Qwen2.5-1.5B fine-tuned for one epoch on a 1M-sequence sample of The Stack (code) — the held-out "Run 2" fine-tuning run used for evaluation in the paper. The trained classifiers in that work predict which sequences this fine-tuned model memorizes from pre-fine-tuning (base-model) features; i.e., the verbatim memorization of training data in this model is what those classifiers forecast.

Note. This model was fine-tuned to study memorization and can reproduce portions of its fine-tuning data verbatim. It is released as a research artifact for reproducibility.