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", 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=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 Settings

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-python-lora

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{
    "language": "Python",
    "license": "apache-2.0",
    "library_name": "transformers",
    "tags": [
      "tinyllama",
      "lora",
      "peft",
      "code",
      "python",
      "fine-tuning",
      "mps"
    ],
    "model_type": "causal-lm",
    "pipeline_tag": "text-generation",
    "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
    "datasets": [
      "codeparrot/codeparrot-clean-valid"
    ],
    "trained_on": "Apple M3 Pro (MPS)",
    "adapter_type": "lora",
    "num_train_samples": 1000,
    "num_epochs": 1,
    "gradient_accumulation_steps": 4,
    "per_device_batch_size": 1,
    "prompt_format": "<|python|>\\n{code}",
    "inference_prompt": "<|python|>\\ndef fibonacci(n):",
    "example_output": "def fibonacci(n):\n    if n <= 1:\n        return n\n    return fibonacci(n-1) + fibonacci(n-2)"
  }