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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("adpretko/train-riscv-O2_epoch3_AMD")
model = AutoModelForCausalLM.from_pretrained("adpretko/train-riscv-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-riscv-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-riscv-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-riscv-O2_epoch3_AMD",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

adpretko commited on Nov 5, 2025

Commit

4475f54

verified ·

1 Parent(s): 03806ea

Training in progress, step 2100

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:b01d79be947d9a880c92cee42fec8d0ad85828b8d0e064f0946229c6739e29a5
 size 3554214752

 version https://git-lfs.github.com/spec/v1
+oid sha256:592fb0a2a57c325ae82aaf5df4760ba5dac4872968a5538649560606b000a1c9
 size 3554214752

trainer_log.jsonl CHANGED Viewed

@@ -198,3 +198,13 @@
 {"current_steps": 1980, "total_steps": 3886, "loss": 0.0077, "lr": 1.141910633764327e-05, "epoch": 1.0190451679320551, "percentage": 50.95, "elapsed_time": "1 day, 7:06:11", "remaining_time": "1 day, 5:56:26"}
 {"current_steps": 1990, "total_steps": 3886, "loss": 0.008, "lr": 1.1330122671225855e-05, "epoch": 1.0241925106163943, "percentage": 51.21, "elapsed_time": "1 day, 7:15:23", "remaining_time": "1 day, 5:46:48"}
 {"current_steps": 2000, "total_steps": 3886, "loss": 0.0081, "lr": 1.1241031655993188e-05, "epoch": 1.0293398533007334, "percentage": 51.47, "elapsed_time": "1 day, 7:24:45", "remaining_time": "1 day, 5:37:20"}

 {"current_steps": 1980, "total_steps": 3886, "loss": 0.0077, "lr": 1.141910633764327e-05, "epoch": 1.0190451679320551, "percentage": 50.95, "elapsed_time": "1 day, 7:06:11", "remaining_time": "1 day, 5:56:26"}
 {"current_steps": 1990, "total_steps": 3886, "loss": 0.008, "lr": 1.1330122671225855e-05, "epoch": 1.0241925106163943, "percentage": 51.21, "elapsed_time": "1 day, 7:15:23", "remaining_time": "1 day, 5:46:48"}
 {"current_steps": 2000, "total_steps": 3886, "loss": 0.0081, "lr": 1.1241031655993188e-05, "epoch": 1.0293398533007334, "percentage": 51.47, "elapsed_time": "1 day, 7:24:45", "remaining_time": "1 day, 5:37:20"}
+{"current_steps": 2010, "total_steps": 3886, "loss": 0.008, "lr": 1.1151840482120386e-05, "epoch": 1.0344871959850728, "percentage": 51.72, "elapsed_time": "1 day, 7:35:48", "remaining_time": "1 day, 5:29:24"}
+{"current_steps": 2020, "total_steps": 3886, "loss": 0.0081, "lr": 1.1062556347865967e-05, "epoch": 1.039634538669412, "percentage": 51.98, "elapsed_time": "1 day, 7:45:08", "remaining_time": "1 day, 5:19:53"}
+{"current_steps": 2030, "total_steps": 3886, "loss": 0.0076, "lr": 1.0973186458990902e-05, "epoch": 1.044781881353751, "percentage": 52.24, "elapsed_time": "1 day, 7:54:21", "remaining_time": "1 day, 5:10:16"}
+{"current_steps": 2040, "total_steps": 3886, "loss": 0.0081, "lr": 1.0883738028177069e-05, "epoch": 1.0499292240380904, "percentage": 52.5, "elapsed_time": "1 day, 8:03:43", "remaining_time": "1 day, 5:00:46"}
+{"current_steps": 2050, "total_steps": 3886, "loss": 0.0082, "lr": 1.0794218274445155e-05, "epoch": 1.0550765667224296, "percentage": 52.75, "elapsed_time": "1 day, 8:12:58", "remaining_time": "1 day, 4:51:11"}
+{"current_steps": 2060, "total_steps": 3886, "loss": 0.0082, "lr": 1.0704634422572029e-05, "epoch": 1.0602239094067687, "percentage": 53.01, "elapsed_time": "1 day, 8:22:12", "remaining_time": "1 day, 4:41:35"}
+{"current_steps": 2070, "total_steps": 3886, "loss": 0.0083, "lr": 1.0614993702507649e-05, "epoch": 1.065371252091108, "percentage": 53.27, "elapsed_time": "1 day, 8:31:32", "remaining_time": "1 day, 4:32:04"}
+{"current_steps": 2080, "total_steps": 3886, "loss": 0.0077, "lr": 1.0525303348791599e-05, "epoch": 1.0705185947754472, "percentage": 53.53, "elapsed_time": "1 day, 8:40:47", "remaining_time": "1 day, 4:22:29"}
+{"current_steps": 2090, "total_steps": 3886, "loss": 0.0081, "lr": 1.0435570599969168e-05, "epoch": 1.0756659374597863, "percentage": 53.78, "elapsed_time": "1 day, 8:50:04", "remaining_time": "1 day, 4:12:57"}
+{"current_steps": 2100, "total_steps": 3886, "loss": 0.0083, "lr": 1.0345802698007198e-05, "epoch": 1.0808132801441257, "percentage": 54.04, "elapsed_time": "1 day, 8:59:21", "remaining_time": "1 day, 4:03:23"}