Instructions to use ubaid0405/tinyllama-1.1b-docstring-gen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ubaid0405/tinyllama-1.1b-docstring-gen with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
model = PeftModel.from_pretrained(base_model, "ubaid0405/tinyllama-1.1b-docstring-gen")

Transformers

How to use ubaid0405/tinyllama-1.1b-docstring-gen with Transformers:

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

pipe = pipeline("text-generation", model="ubaid0405/tinyllama-1.1b-docstring-gen")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ubaid0405/tinyllama-1.1b-docstring-gen", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ubaid0405/tinyllama-1.1b-docstring-gen with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ubaid0405/tinyllama-1.1b-docstring-gen"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ubaid0405/tinyllama-1.1b-docstring-gen",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ubaid0405/tinyllama-1.1b-docstring-gen

SGLang

How to use ubaid0405/tinyllama-1.1b-docstring-gen 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 "ubaid0405/tinyllama-1.1b-docstring-gen" \
    --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": "ubaid0405/tinyllama-1.1b-docstring-gen",
		"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 "ubaid0405/tinyllama-1.1b-docstring-gen" \
        --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": "ubaid0405/tinyllama-1.1b-docstring-gen",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ubaid0405/tinyllama-1.1b-docstring-gen with Docker Model Runner:
```
docker model run hf.co/ubaid0405/tinyllama-1.1b-docstring-gen
```

Model Description

This model is a LoRA fine-tuned version of TinyLlama-1.1B-Chat-v1.0, trained to generate Python docstrings for given function code.

Base model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
Dataset: code_search_net (Python subset)
Method: Supervised Fine-Tuning (SFT) with LoRA
Developed by: Ubaid

Training Results

Epoch	Training Loss	Validation Loss	Token Accuracy
1	1.165	1.195	72.8%

How to Use

```python from peft import AutoPeftModelForCausalLM from transformers import AutoTokenizer

repo = "ubaid0405/tinyllama-1.1b-docstring-gen" model = AutoPeftModelForCausalLM.from_pretrained(repo, device_map="auto") tok = AutoTokenizer.from_pretrained(repo)

code = "def add(a, b):\n return a + b" prompt = f"[INST] Write a docstring:\n{code} [/INST]"

ids = tok(prompt, return_tensors="pt").input_ids.to(model.device) out = model.generate(ids, max_new_tokens=100) print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True)) ```

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