Instructions to use jgalego/ada-coder-qwen2.5-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jgalego/ada-coder-qwen2.5-7b with Transformers:

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

pipe = pipeline("text-generation", model="jgalego/ada-coder-qwen2.5-7b")
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("jgalego/ada-coder-qwen2.5-7b")
model = AutoModelForCausalLM.from_pretrained("jgalego/ada-coder-qwen2.5-7b", 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 jgalego/ada-coder-qwen2.5-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jgalego/ada-coder-qwen2.5-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jgalego/ada-coder-qwen2.5-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jgalego/ada-coder-qwen2.5-7b

SGLang

How to use jgalego/ada-coder-qwen2.5-7b 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 "jgalego/ada-coder-qwen2.5-7b" \
    --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": "jgalego/ada-coder-qwen2.5-7b",
		"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 "jgalego/ada-coder-qwen2.5-7b" \
        --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": "jgalego/ada-coder-qwen2.5-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jgalego/ada-coder-qwen2.5-7b with Docker Model Runner:
```
docker model run hf.co/jgalego/ada-coder-qwen2.5-7b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

ada-coder-qwen2.5-7b

Qwen/Qwen2.5-Coder-7B-Instruct fine-tuned with LoRA to write Ada 2022 and SPARK code from a task description, spec or signature.

Review every line. The output is not qualified for DO-178C, EN 50128, ISO 26262 or any other certification process. Compile it, test it and, for SPARK, prove it before use.

🚀 Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "jgalego/ada-coder-qwen2.5-7b"
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, dtype="auto")

messages = [
    {"role": "system", "content": "You write Ada 2022 and SPARK code that compiles cleanly with GNAT."},
    {"role": "user", "content": "Write a function that returns the greatest common divisor of two Positive numbers."},
]
inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
)
out = model.generate(**inputs, max_new_tokens=512, do_sample=False)
print(tokenizer.decode(out[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True))

It also completes raw Ada source with no chat template, which is how it is scored below.

🏋️ Training


Base	Qwen/Qwen2.5-Coder-7B-Instruct
Data	`r7_dataset.jsonl` from the-clanker-lover/steelman-sft-ada, 6,110 instruction/code pairs that compile with GNAT
Method	LoRA, r=16, alpha=32, all linear layers, merged
Steps	378 (1.0 epochs), batch size 16, max length 2048
Learning rate	0.0002, cosine
Mean train loss	0.2128
Hardware	NVIDIA A10G, 69 min

📊 Results

nuprl/MultiPL-E HumanEval-Ada (humaneval-adb, 157 problems). Greedy decoding from the raw prompt, then built and run with the GNAT flags from MultiPL-E's eval_adb.py. A program passes when every pragma Assert holds.

Model	n	pass@1	Compile rate	Runtime errors
jgalego/ada-coder-qwen2.5-7b	157	0.414	0.771	56
jgalego/ada-coder-qwen2.5-7b@grpo-1	157	0.414	0.796	60
jgalego/ada-coder-qwen2.5-7b@sft-2	157	0.401	0.771	58
Qwen/Qwen2.5-Coder-7B-Instruct	157	0.312	0.471	25

Reference scores reported by the Steelman authors (model card, report) on the same benchmark with their own harness, temperature 0:

Model	pass@1	Compile rate
Steelman 14B v0.3 (Qwen2.5-Coder-14B + R7 data)	0.459	0.854
Steelman 14B v0.2 (Qwen2.5-Coder-14B-Instruct + R6 data)	0.471	0.752
Qwen2.5-Coder-14B, untuned	0.344	0.510

⚠️ Limitations

Scored on small HumanEval-style functions, not on full programs or packages.
Contracts are at most checked at run time (-gnata); nothing here runs GNATprove.
Trained on a single dataset; style follows that data.