Instructions to use GiuLeo01/FortranCodeGen-3B-SynthData with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GiuLeo01/FortranCodeGen-3B-SynthData with Transformers:

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

pipe = pipeline("text-generation", model="GiuLeo01/FortranCodeGen-3B-SynthData")
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("GiuLeo01/FortranCodeGen-3B-SynthData")
model = AutoModelForCausalLM.from_pretrained("GiuLeo01/FortranCodeGen-3B-SynthData", 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 GiuLeo01/FortranCodeGen-3B-SynthData with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GiuLeo01/FortranCodeGen-3B-SynthData"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GiuLeo01/FortranCodeGen-3B-SynthData",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GiuLeo01/FortranCodeGen-3B-SynthData

SGLang

How to use GiuLeo01/FortranCodeGen-3B-SynthData 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 "GiuLeo01/FortranCodeGen-3B-SynthData" \
    --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": "GiuLeo01/FortranCodeGen-3B-SynthData",
		"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 "GiuLeo01/FortranCodeGen-3B-SynthData" \
        --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": "GiuLeo01/FortranCodeGen-3B-SynthData",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use GiuLeo01/FortranCodeGen-3B-SynthData with Docker Model Runner:
```
docker model run hf.co/GiuLeo01/FortranCodeGen-3B-SynthData
```

GiuLeo01 commited on May 19, 2025

Commit

4a73058

verified ·

1 Parent(s): 42e2fc0

Update README.md

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@@ -29,6 +29,13 @@ According to the current demo version of the FortranHumanEval benchmark:
 | GPT-4o mini           |    18.90%   |           43.90%        |
 | GPT-4o                |    32.31%   |        17.07%           |
 ## Uses

 | GPT-4o mini           |    18.90%   |           43.90%        |
 | GPT-4o                |    32.31%   |        17.07%           |
+Compared to its base model (Qwen 2.5 Coder 3B Instruct), FortranCodeGen 3B shows a strong improvement, increasing pass@1 accuracy from 5.48% to 23.17% and reducing the compile error rate from 63.41% to 17.68%. This highlights the effectiveness of this simple fine-tuning process, even though it was performed with limited resources: no human-labeled data, small synthetic dataset, and training on a single consumer GPU (L4 :'( ).
+When compared to GPT-4o mini, FortranCodeGen 3B outperforms it in terms of both pass@1 accuracy (23.17% vs. 18.90%) and compile reliability (17.68% vs. 43.90%). This suggests that task-specific fine-tuning can produce better results than more general, (probably) larger models.
+While it doesn't yet match the overall performance of GPT-4o, which achieves 32.31% pass@1, FortranCodeGen 3B reaches a comparable level of compilation correctness (17.68% vs. 17.07%), suggesting that its outputs are syntactically robust and close to executable, even when they don’t solve the full task.
+These results confirm that targeted specialization can significantly enhance model performance on underrepresented tasks, and suggest a promising direction for very-low-resource fine-tuning in legacy or niche programming languages.
 ## Uses