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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("adpretko/train-armv8-O2_epoch3_AMD")
model = AutoModelForCausalLM.from_pretrained("adpretko/train-armv8-O2_epoch3_AMD", 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 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 29, 2025

Commit

5aa584f

verified ·

1 Parent(s): be8b7b3

Training in progress, step 1400

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:e2dac18fa83ac80d46dd312f810c1eec2209fc4705df81fb90288539759a1d35
 size 3554214752

 version https://git-lfs.github.com/spec/v1
+oid sha256:329e5d317fc30bae5dd2b303f57e53801a395864ab5ea21612ab6ad2e2dda765
 size 3554214752

trainer_log.jsonl CHANGED Viewed

@@ -128,3 +128,13 @@
 {"current_steps": 1280, "total_steps": 3236, "loss": 0.0071, "lr": 1.5145653468990626e-05, "epoch": 0.7914669964445818, "percentage": 39.56, "elapsed_time": "23:16:07", "remaining_time": "1 day, 11:33:26"}
 {"current_steps": 1290, "total_steps": 3236, "loss": 0.0069, "lr": 1.50528502410759e-05, "epoch": 0.7976503323543052, "percentage": 39.86, "elapsed_time": "23:26:58", "remaining_time": "1 day, 11:22:27"}
 {"current_steps": 1300, "total_steps": 3236, "loss": 0.0072, "lr": 1.4959458915816681e-05, "epoch": 0.8038336682640285, "percentage": 40.17, "elapsed_time": "23:37:37", "remaining_time": "1 day, 11:11:09"}

 {"current_steps": 1280, "total_steps": 3236, "loss": 0.0071, "lr": 1.5145653468990626e-05, "epoch": 0.7914669964445818, "percentage": 39.56, "elapsed_time": "23:16:07", "remaining_time": "1 day, 11:33:26"}
 {"current_steps": 1290, "total_steps": 3236, "loss": 0.0069, "lr": 1.50528502410759e-05, "epoch": 0.7976503323543052, "percentage": 39.86, "elapsed_time": "23:26:58", "remaining_time": "1 day, 11:22:27"}
 {"current_steps": 1300, "total_steps": 3236, "loss": 0.0072, "lr": 1.4959458915816681e-05, "epoch": 0.8038336682640285, "percentage": 40.17, "elapsed_time": "23:37:37", "remaining_time": "1 day, 11:11:09"}
+{"current_steps": 1310, "total_steps": 3236, "loss": 0.0073, "lr": 1.4865490362957317e-05, "epoch": 0.8100170041737518, "percentage": 40.48, "elapsed_time": "23:50:04", "remaining_time": "1 day, 11:02:32"}
+{"current_steps": 1320, "total_steps": 3236, "loss": 0.0074, "lr": 1.477095551942525e-05, "epoch": 0.8162003400834751, "percentage": 40.79, "elapsed_time": "1 day, 0:00:50", "remaining_time": "1 day, 10:51:23"}
+{"current_steps": 1330, "total_steps": 3236, "loss": 0.007, "lr": 1.4675865388058037e-05, "epoch": 0.8223836759931983, "percentage": 41.1, "elapsed_time": "1 day, 0:11:37", "remaining_time": "1 day, 10:40:18"}
+{"current_steps": 1340, "total_steps": 3236, "loss": 0.0071, "lr": 1.458023103632277e-05, "epoch": 0.8285670119029216, "percentage": 41.41, "elapsed_time": "1 day, 0:22:25", "remaining_time": "1 day, 10:29:13"}
+{"current_steps": 1350, "total_steps": 3236, "loss": 0.0071, "lr": 1.4484063595027924e-05, "epoch": 0.8347503478126449, "percentage": 41.72, "elapsed_time": "1 day, 0:33:14", "remaining_time": "1 day, 10:18:10"}
+{"current_steps": 1360, "total_steps": 3236, "loss": 0.0071, "lr": 1.438737425702786e-05, "epoch": 0.8409336837223682, "percentage": 42.03, "elapsed_time": "1 day, 0:43:55", "remaining_time": "1 day, 10:06:56"}
+{"current_steps": 1370, "total_steps": 3236, "loss": 0.0072, "lr": 1.4290174275920095e-05, "epoch": 0.8471170196320915, "percentage": 42.34, "elapsed_time": "1 day, 0:54:32", "remaining_time": "1 day, 9:55:37"}
+{"current_steps": 1380, "total_steps": 3236, "loss": 0.0071, "lr": 1.41924749647355e-05, "epoch": 0.8533003555418148, "percentage": 42.65, "elapsed_time": "1 day, 1:05:17", "remaining_time": "1 day, 9:44:29"}
+{"current_steps": 1390, "total_steps": 3236, "loss": 0.0069, "lr": 1.4094287694621589e-05, "epoch": 0.8594836914515381, "percentage": 42.95, "elapsed_time": "1 day, 1:16:04", "remaining_time": "1 day, 9:33:26"}
+{"current_steps": 1400, "total_steps": 3236, "loss": 0.007, "lr": 1.3995623893519044e-05, "epoch": 0.8656670273612614, "percentage": 43.26, "elapsed_time": "1 day, 1:26:53", "remaining_time": "1 day, 9:22:24"}