Instructions to use MalikIbrar/flan-python-expert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MalikIbrar/flan-python-expert with Transformers:

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

pipe = pipeline("text-generation", model="MalikIbrar/flan-python-expert")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("MalikIbrar/flan-python-expert")
model = AutoModelForSeq2SeqLM.from_pretrained("MalikIbrar/flan-python-expert", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MalikIbrar/flan-python-expert with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MalikIbrar/flan-python-expert"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MalikIbrar/flan-python-expert",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MalikIbrar/flan-python-expert

SGLang

How to use MalikIbrar/flan-python-expert 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 "MalikIbrar/flan-python-expert" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MalikIbrar/flan-python-expert",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "MalikIbrar/flan-python-expert" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MalikIbrar/flan-python-expert",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MalikIbrar/flan-python-expert with Docker Model Runner:
```
docker model run hf.co/MalikIbrar/flan-python-expert
```

flan-python-expert / README.md

MalikIbrar

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metadata

datasets:
  - Programming-Language/codeagent-python
language:
  - en
base_model:
  - google/flan-t5-base
pipeline_tag: text-generation
library_name: transformers
license: apache-2.0

flan-python-expert 🚀

This model is a fine-tuned version of google/flan-t5-base on the codeagent-python dataset.

It is designed to generate Python code from natural language instructions.

🧠 Model Details

Base Model: FLAN-T5 Base
Fine-tuned on: Python code dataset (codeagent-python)
Task: Text-to-code generation
Language: English
Framework: 🤗 Transformers
Library: adapter-transformers

🏋️ Training

The model was trained using the following setup:

from transformers import TrainingArguments

training_args = TrainingArguments(
    output_dir="flan-python-expert",
    evaluation_strategy="epoch",
    learning_rate=2e-6,
    per_device_train_batch_size=1,
    per_device_eval_batch_size=1,
    num_train_epochs=1,
    weight_decay=0.01,
    save_total_limit=2,
    logging_steps=1,
    push_to_hub=False,
)

Trained for 1 epoch

Optimized for low-resource fine-tuning

Training performed using Hugging Face Trainer

Example Usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model = AutoModelForSeq2SeqLM.from_pretrained("MalikIbrar/flan-python-expert")
tokenizer = AutoTokenizer.from_pretrained("MalikIbrar/flan-python-expert")

input_text = "Write a Python function to check if a number is prime."
inputs = tokenizer(input_text, return_tensors="pt")

outputs = model.generate(**inputs, max_length=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))