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
```

ada-coder-qwen2.5-7b / README.md

jgalego

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---
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
datasets:
- the-clanker-lover/steelman-sft-ada
- nuprl/MultiPL-E
language: en
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
tags:
- ada
- spark
- code
model-index:
- name: ada-coder-qwen2.5-7b
  results:
  - task:
      type: text-generation
    dataset:
      name: MultiPL-E HumanEval-Ada
      type: nuprl/MultiPL-E
      config: humaneval-adb
    metrics:
    - type: pass@1
      value: 0.414
      name: pass@1
    - type: compile_rate
      value: 0.771
      name: Compile rate
---
# ada-coder-qwen2.5-7b

<p align="center"><img src="assets/logo.svg" alt="Ada coder logo: a Jacquard punched card, after Ada Lovelace's loom analogy, whose holes form a teal check mark" width="200"></p>

[Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) fine-tuned with LoRA to write Ada 2022 and SPARK code from a task description, spec or signature.

> [!WARNING]
> **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

```python
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](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) |
| Data | `r7_dataset.jsonl` from [the-clanker-lover/steelman-sft-ada](https://huggingface.co/datasets/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](https://huggingface.co/datasets/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 | Timeouts |
|---|---|---|---|---|---|
| jgalego/ada-coder-qwen2.5-7b | 157 | 0.414 | 0.771 | 56 | 0 |
| jgalego/ada-coder-qwen2.5-7b@grpo-1 | 157 | 0.414 | 0.796 | 60 | 0 |
| jgalego/ada-coder-qwen2.5-7b@sft-2 | 157 | 0.401 | 0.771 | 58 | 0 |
| Qwen/Qwen2.5-Coder-7B-Instruct | 157 | 0.312 | 0.471 | 25 | 0 |

Reference scores reported by the Steelman authors ([model card](https://huggingface.co/the-clanker-lover/steelman-14b-ada), [report](https://huggingface.co/datasets/the-clanker-lover/steelman-sft-ada/blob/main/humaneval_ada/multipl_e_r6v3_report.md)) 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.

	---
	base_model: Qwen/Qwen2.5-Coder-7B-Instruct
	datasets:
	- the-clanker-lover/steelman-sft-ada
	- nuprl/MultiPL-E
	language: en
	library_name: transformers
	license: apache-2.0
	pipeline_tag: text-generation
	tags:
	- ada
	- spark
	- code
	model-index:
	- name: ada-coder-qwen2.5-7b
	results:
	- task:
	type: text-generation
	dataset:
	name: MultiPL-E HumanEval-Ada
	type: nuprl/MultiPL-E
	config: humaneval-adb
	metrics:
	- type: pass@1
	value: 0.414
	name: pass@1
	- type: compile_rate
	value: 0.771
	name: Compile rate
	---

	# ada-coder-qwen2.5-7b

	<p align="center"><img src="assets/logo.svg" alt="Ada coder logo: a Jacquard punched card, after Ada Lovelace's loom analogy, whose holes form a teal check mark" width="200"></p>

	[Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) fine-tuned with LoRA to write Ada 2022 and SPARK code from a task description, spec or signature.

	> [!WARNING]
	> 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

	```python
	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](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) \|
	\| Data \| `r7_dataset.jsonl` from [the-clanker-lover/steelman-sft-ada](https://huggingface.co/datasets/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](https://huggingface.co/datasets/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 \| Timeouts \|
	\|---\|---\|---\|---\|---\|---\|
	\| jgalego/ada-coder-qwen2.5-7b \| 157 \| 0.414 \| 0.771 \| 56 \| 0 \|
	\| jgalego/ada-coder-qwen2.5-7b@grpo-1 \| 157 \| 0.414 \| 0.796 \| 60 \| 0 \|
	\| jgalego/ada-coder-qwen2.5-7b@sft-2 \| 157 \| 0.401 \| 0.771 \| 58 \| 0 \|
	\| Qwen/Qwen2.5-Coder-7B-Instruct \| 157 \| 0.312 \| 0.471 \| 25 \| 0 \|

	Reference scores reported by the Steelman authors ([model card](https://huggingface.co/the-clanker-lover/steelman-14b-ada), [report](https://huggingface.co/datasets/the-clanker-lover/steelman-sft-ada/blob/main/humaneval_ada/multipl_e_r6v3_report.md)) 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.