Instructions to use ahmedheakl/gg-armv8-O0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ahmedheakl/gg-armv8-O0 with Transformers:

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

pipe = pipeline("text-generation", model="ahmedheakl/gg-armv8-O0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ahmedheakl/gg-armv8-O0")
model = AutoModelForCausalLM.from_pretrained("ahmedheakl/gg-armv8-O0")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ahmedheakl/gg-armv8-O0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ahmedheakl/gg-armv8-O0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ahmedheakl/gg-armv8-O0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ahmedheakl/gg-armv8-O0

SGLang

How to use ahmedheakl/gg-armv8-O0 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 "ahmedheakl/gg-armv8-O0" \
    --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": "ahmedheakl/gg-armv8-O0",
		"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 "ahmedheakl/gg-armv8-O0" \
        --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": "ahmedheakl/gg-armv8-O0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ahmedheakl/gg-armv8-O0 with Docker Model Runner:
```
docker model run hf.co/ahmedheakl/gg-armv8-O0
```

Check out more datails here:

Paper: https://arxiv.org/abs/2506.14606
Code: https://github.com/ahmedheakl/Guaranteed-Guess

ex19_qwen2.5-1.5b-1M-stack-16kcw

This model is a fine-tuned version of Qwen/Qwen2.5-Coder-1.5B-Instruct on the stack_16k, the anghabench_16k_1 and the anghabench_16k_2 datasets. It achieves the following results on the evaluation set:

Loss: 0.0003

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 4
total_train_batch_size: 8
total_eval_batch_size: 2
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 2.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.0037	0.1645	25000	0.0028
0.003	0.3289	50000	0.0017
0.002	0.4934	75000	0.0012
0.0002	0.6579	100000	0.0011
0.0011	0.8224	125000	0.0009
0.001	0.9868	150000	0.0007
0.0013	1.1513	175000	0.0005
0.0004	1.3158	200000	0.0005
0.0007	1.4802	225000	0.0004
0.0007	1.6447	250000	0.0004
0.0003	1.8092	275000	0.0003
0.0002	1.9736	300000	0.0003