Instructions to use adpretko/train-armv8-O2_epoch3_AMD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use adpretko/train-armv8-O2_epoch3_AMD with Transformers:

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

pipe = pipeline("text-generation", model="adpretko/train-armv8-O2_epoch3_AMD")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("adpretko/train-armv8-O2_epoch3_AMD")
model = AutoModelForMultimodalLM.from_pretrained("adpretko/train-armv8-O2_epoch3_AMD")
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 adpretko/train-armv8-O2_epoch3_AMD with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "adpretko/train-armv8-O2_epoch3_AMD"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "adpretko/train-armv8-O2_epoch3_AMD",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/adpretko/train-armv8-O2_epoch3_AMD

SGLang

How to use adpretko/train-armv8-O2_epoch3_AMD 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 "adpretko/train-armv8-O2_epoch3_AMD" \
    --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": "adpretko/train-armv8-O2_epoch3_AMD",
		"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 "adpretko/train-armv8-O2_epoch3_AMD" \
        --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": "adpretko/train-armv8-O2_epoch3_AMD",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use adpretko/train-armv8-O2_epoch3_AMD with Docker Model Runner:
```
docker model run hf.co/adpretko/train-armv8-O2_epoch3_AMD
```

adpretko commited on Oct 30, 2025

Commit

9e1aa2e

verified ·

1 Parent(s): f2209a6

Training in progress, step 1900

Browse files

Files changed (2) hide show

model.safetensors +1 -1
trainer_log.jsonl +10 -0

model.safetensors CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:89c88f6158822aa4f562bd38349dd7e993b688bf0d47122c9daaaec6b5035452
 size 3554214752

 version https://git-lfs.github.com/spec/v1
+oid sha256:d1594550fed2d7691b07a977eb2900a6211ab81c00be284e7123cbc0981939b9
 size 3554214752

trainer_log.jsonl CHANGED Viewed

@@ -178,3 +178,13 @@
 {"current_steps": 1780, "total_steps": 3236, "loss": 0.0059, "lr": 1.0010788434217625e-05, "epoch": 1.1001700417375173, "percentage": 55.01, "elapsed_time": "1 day, 8:21:14", "remaining_time": "1 day, 2:27:53"}
 {"current_steps": 1790, "total_steps": 3236, "loss": 0.0059, "lr": 9.902905598838177e-06, "epoch": 1.1063533776472407, "percentage": 55.32, "elapsed_time": "1 day, 8:32:00", "remaining_time": "1 day, 2:16:52"}
 {"current_steps": 1800, "total_steps": 3236, "loss": 0.0058, "lr": 9.795034064201235e-06, "epoch": 1.112536713556964, "percentage": 55.62, "elapsed_time": "1 day, 8:42:44", "remaining_time": "1 day, 2:05:49"}

 {"current_steps": 1780, "total_steps": 3236, "loss": 0.0059, "lr": 1.0010788434217625e-05, "epoch": 1.1001700417375173, "percentage": 55.01, "elapsed_time": "1 day, 8:21:14", "remaining_time": "1 day, 2:27:53"}
 {"current_steps": 1790, "total_steps": 3236, "loss": 0.0059, "lr": 9.902905598838177e-06, "epoch": 1.1063533776472407, "percentage": 55.32, "elapsed_time": "1 day, 8:32:00", "remaining_time": "1 day, 2:16:52"}
 {"current_steps": 1800, "total_steps": 3236, "loss": 0.0058, "lr": 9.795034064201235e-06, "epoch": 1.112536713556964, "percentage": 55.62, "elapsed_time": "1 day, 8:42:44", "remaining_time": "1 day, 2:05:49"}
+{"current_steps": 1810, "total_steps": 3236, "loss": 0.0059, "lr": 9.68718638539156e-06, "epoch": 1.1187200494666873, "percentage": 55.93, "elapsed_time": "1 day, 8:55:07", "remaining_time": "1 day, 1:56:05"}
+{"current_steps": 1820, "total_steps": 3236, "loss": 0.0058, "lr": 9.579375114717352e-06, "epoch": 1.1249033853764105, "percentage": 56.24, "elapsed_time": "1 day, 9:05:56", "remaining_time": "1 day, 1:45:06"}
+{"current_steps": 1830, "total_steps": 3236, "loss": 0.0059, "lr": 9.471612800249295e-06, "epoch": 1.1310867212861337, "percentage": 56.55, "elapsed_time": "1 day, 9:16:45", "remaining_time": "1 day, 1:34:07"}
+{"current_steps": 1840, "total_steps": 3236, "loss": 0.0059, "lr": 9.363911984360104e-06, "epoch": 1.1372700571958572, "percentage": 56.86, "elapsed_time": "1 day, 9:27:29", "remaining_time": "1 day, 1:23:04"}
+{"current_steps": 1850, "total_steps": 3236, "loss": 0.0061, "lr": 9.256285202264716e-06, "epoch": 1.1434533931055806, "percentage": 57.17, "elapsed_time": "1 day, 9:38:18", "remaining_time": "1 day, 1:12:05"}
+{"current_steps": 1860, "total_steps": 3236, "loss": 0.0056, "lr": 9.148744980561332e-06, "epoch": 1.1496367290153038, "percentage": 57.48, "elapsed_time": "1 day, 9:49:02", "remaining_time": "1 day, 1:01:03"}
+{"current_steps": 1870, "total_steps": 3236, "loss": 0.006, "lr": 9.041303835773458e-06, "epoch": 1.155820064925027, "percentage": 57.79, "elapsed_time": "1 day, 9:59:47", "remaining_time": "1 day, 0:50:02"}
+{"current_steps": 1880, "total_steps": 3236, "loss": 0.0058, "lr": 8.933974272893113e-06, "epoch": 1.1620034008347504, "percentage": 58.1, "elapsed_time": "1 day, 10:10:28", "remaining_time": "1 day, 0:38:57"}
+{"current_steps": 1890, "total_steps": 3236, "loss": 0.0058, "lr": 8.826768783925385e-06, "epoch": 1.1681867367444736, "percentage": 58.41, "elapsed_time": "1 day, 10:21:15", "remaining_time": "1 day, 0:27:57"}
+{"current_steps": 1900, "total_steps": 3236, "loss": 0.0057, "lr": 8.719699846434493e-06, "epoch": 1.174370072654197, "percentage": 58.71, "elapsed_time": "1 day, 10:31:56", "remaining_time": "1 day, 0:16:54"}