Instructions to use mo7amed-3bdalla7/tinyllama-python-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mo7amed-3bdalla7/tinyllama-python-lora with Transformers:

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

pipe = pipeline("text-generation", model="mo7amed-3bdalla7/tinyllama-python-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mo7amed-3bdalla7/tinyllama-python-lora")
model = AutoModelForCausalLM.from_pretrained("mo7amed-3bdalla7/tinyllama-python-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]:]))

PEFT
How to use mo7amed-3bdalla7/tinyllama-python-lora with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mo7amed-3bdalla7/tinyllama-python-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mo7amed-3bdalla7/tinyllama-python-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": "mo7amed-3bdalla7/tinyllama-python-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mo7amed-3bdalla7/tinyllama-python-lora

SGLang

How to use mo7amed-3bdalla7/tinyllama-python-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 "mo7amed-3bdalla7/tinyllama-python-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": "mo7amed-3bdalla7/tinyllama-python-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 "mo7amed-3bdalla7/tinyllama-python-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": "mo7amed-3bdalla7/tinyllama-python-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mo7amed-3bdalla7/tinyllama-python-lora with Docker Model Runner:
```
docker model run hf.co/mo7amed-3bdalla7/tinyllama-python-lora
```

🐍 TinyLLaMA LoRA - Fine-tuned on Python Code

This is a LoRA fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 using a subset of Python code from the codeparrot dataset. It is trained to generate Python functions and code snippets based on natural language or code-based prompts.

🔧 Training Details

Base model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
Adapter type: LoRA (PEFT)
Dataset: codeparrot/codeparrot-clean-valid[:1000]
Tokenized max length: 512
Trained on: Apple M3 Pro (MPS backend)
Epochs: 1
Batch size: 1 (with gradient accumulation)

💡 Example Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
adapter_model = "your-username/tinyllama-python-lora"

tokenizer = AutoTokenizer.from_pretrained(base_model)
model = AutoModelForCausalLM.from_pretrained(base_model)
model = PeftModel.from_pretrained(model, adapter_model)

prompt = "<|python|>\ndef fibonacci(n):"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🧠 Intended Use

Code completion for Python

Teaching LLMs Python function structure

Experimentation with LoRA on small code datasets

##⚠️ Limitations Trained on a small subset of data (1,000 samples)

May hallucinate or generate syntactically incorrect code

Not suitable for production use without further fine-tuning and evaluation

📜 License

Apache 2.0 — same as the base model.

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Model tree for mo7amed-3bdalla7/tinyllama-python-lora

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