Instructions to use ahmedheakl/gg-armv8-O2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ahmedheakl/gg-armv8-O2 with Transformers:

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

pipe = pipeline("text-generation", model="ahmedheakl/gg-armv8-O2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ahmedheakl/gg-armv8-O2")
model = AutoModelForCausalLM.from_pretrained("ahmedheakl/gg-armv8-O2")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ahmedheakl/gg-armv8-O2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ahmedheakl/gg-armv8-O2"
# 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-O2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ahmedheakl/gg-armv8-O2

SGLang

How to use ahmedheakl/gg-armv8-O2 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-O2" \
    --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-O2",
		"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-O2" \
        --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-O2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ahmedheakl/gg-armv8-O2 with Docker Model Runner:
```
docker model run hf.co/ahmedheakl/gg-armv8-O2
```

Add pipeline tag, code-translation tag, project page link and improve description

by nielsr HF Staff - opened Jun 19, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-5

Files changed (1) hide show

README.md +8 -5

README.md CHANGED Viewed

@@ -1,19 +1,22 @@
 ---
 library_name: transformers
 license: apache-2.0
-base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
 tags:
 - llama-factory
 - full
 - generated_from_trainer
 model-index:
 - name: ex33_armv8
   results: []
 ---
 Check out more datails here:
 - Paper: https://arxiv.org/abs/2506.14606
 - Code: https://github.com/ahmedheakl/Guaranteed-Guess
 # ex33_armv8
@@ -21,15 +24,15 @@ This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-1.5B-Instruct](https:/
 ## Model description
-More information needed
 ## Intended uses & limitations
-More information needed
 ## Training and evaluation data
-More information needed
 ## Training procedure
@@ -59,4 +62,4 @@ The following hyperparameters were used during training:
 - Transformers 4.50.0
 - Pytorch 2.6.0+cu124
 - Datasets 3.4.1
-- Tokenizers 0.21.0

 ---
+base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
 library_name: transformers
 license: apache-2.0
 tags:
 - llama-factory
 - full
 - generated_from_trainer
+- code-translation
 model-index:
 - name: ex33_armv8
   results: []
+pipeline_tag: translation
 ---
 Check out more datails here:
 - Paper: https://arxiv.org/abs/2506.14606
 - Code: https://github.com/ahmedheakl/Guaranteed-Guess
+- Project page: https://ahmedheakl.github.io/Guaranteed-Guess/
 # ex33_armv8
 ## Model description
+This model is part of the Guaranteed Guess (GG) pipeline, which tackles the challenging problem of CISC-to-RISC transpilation. GG combines the power of pre-trained large language models (LLMs) with software testing to generate and validate code translations between instruction set architectures (ISAs). This model is fine-tuned to translate from x86 (CISC) to ARMv8 (RISC).
 ## Intended uses & limitations
+This model is intended for researchers and developers interested in ISA transpilation, particularly CISC-to-RISC translation. It can be used to translate x86 assembly code to ARMv8 assembly code. However, the model's performance may vary depending on the complexity and optimization level of the input code.
 ## Training and evaluation data
+The model was trained and evaluated on the anghabench_armv8_O2_p1, the anghabench_armv8_O2_p2 and the stack_armv8_O2 datasets. These datasets include code snippets and programs designed to test the model's ability to translate between x86 and ARMv8 architectures.
 ## Training procedure
 - Transformers 4.50.0
 - Pytorch 2.6.0+cu124
 - Datasets 3.4.1
+- Tokenizers 0.21.0