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 4, 2025

Commit

fda916c

verified ·

1 Parent(s): 47940fc

Training in progress, step 1500

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 sha256:0b39dc4809c97c89e506fc1294dbc13e2658d428430c349e6f70f3f2c67e2315
 size 3554214752

 version https://git-lfs.github.com/spec/v1
+oid sha256:7cf5adec509d12531b373a9ca895d9787d82cdec53226f793257208f61e245eb
 size 3554214752

trainer_log.jsonl CHANGED Viewed

@@ -138,3 +138,13 @@
 {"current_steps": 1380, "total_steps": 3886, "loss": 0.0102, "lr": 1.6298903880848836e-05, "epoch": 0.710333290438811, "percentage": 35.51, "elapsed_time": "21:40:11", "remaining_time": "1 day, 15:21:05"}
 {"current_steps": 1390, "total_steps": 3886, "loss": 0.0099, "lr": 1.6228875836438386e-05, "epoch": 0.7154806331231501, "percentage": 35.77, "elapsed_time": "21:49:33", "remaining_time": "1 day, 15:11:32"}
 {"current_steps": 1400, "total_steps": 3886, "loss": 0.0095, "lr": 1.615834508467931e-05, "epoch": 0.7206279758074894, "percentage": 36.03, "elapsed_time": "21:58:40", "remaining_time": "1 day, 15:01:34"}

 {"current_steps": 1380, "total_steps": 3886, "loss": 0.0102, "lr": 1.6298903880848836e-05, "epoch": 0.710333290438811, "percentage": 35.51, "elapsed_time": "21:40:11", "remaining_time": "1 day, 15:21:05"}
 {"current_steps": 1390, "total_steps": 3886, "loss": 0.0099, "lr": 1.6228875836438386e-05, "epoch": 0.7154806331231501, "percentage": 35.77, "elapsed_time": "21:49:33", "remaining_time": "1 day, 15:11:32"}
 {"current_steps": 1400, "total_steps": 3886, "loss": 0.0095, "lr": 1.615834508467931e-05, "epoch": 0.7206279758074894, "percentage": 36.03, "elapsed_time": "21:58:40", "remaining_time": "1 day, 15:01:34"}
+{"current_steps": 1410, "total_steps": 3886, "loss": 0.0101, "lr": 1.608731731782301e-05, "epoch": 0.7257753184918286, "percentage": 36.28, "elapsed_time": "22:09:41", "remaining_time": "1 day, 14:54:59"}
+{"current_steps": 1420, "total_steps": 3886, "loss": 0.0102, "lr": 1.6015798268232964e-05, "epoch": 0.7309226611761678, "percentage": 36.54, "elapsed_time": "22:18:56", "remaining_time": "1 day, 14:45:13"}
+{"current_steps": 1430, "total_steps": 3886, "loss": 0.0093, "lr": 1.5943793707922086e-05, "epoch": 0.736070003860507, "percentage": 36.8, "elapsed_time": "22:28:07", "remaining_time": "1 day, 14:35:22"}
+{"current_steps": 1440, "total_steps": 3886, "loss": 0.0104, "lr": 1.5871309448086903e-05, "epoch": 0.7412173465448463, "percentage": 37.06, "elapsed_time": "22:37:18", "remaining_time": "1 day, 14:25:31"}
+{"current_steps": 1450, "total_steps": 3886, "loss": 0.0097, "lr": 1.5798351338638548e-05, "epoch": 0.7463646892291854, "percentage": 37.31, "elapsed_time": "22:46:37", "remaining_time": "1 day, 14:15:56"}
+{"current_steps": 1460, "total_steps": 3886, "loss": 0.0103, "lr": 1.5724925267730625e-05, "epoch": 0.7515120319135247, "percentage": 37.57, "elapsed_time": "22:56:03", "remaining_time": "1 day, 14:06:31"}
+{"current_steps": 1470, "total_steps": 3886, "loss": 0.0102, "lr": 1.5651037161284032e-05, "epoch": 0.7566593745978638, "percentage": 37.83, "elapsed_time": "23:05:21", "remaining_time": "1 day, 13:56:52"}
+{"current_steps": 1480, "total_steps": 3886, "loss": 0.0098, "lr": 1.5576692982508665e-05, "epoch": 0.7618067172822031, "percentage": 38.09, "elapsed_time": "23:14:33", "remaining_time": "1 day, 13:47:06"}
+{"current_steps": 1490, "total_steps": 3886, "loss": 0.0099, "lr": 1.550189873142219e-05, "epoch": 0.7669540599665423, "percentage": 38.34, "elapsed_time": "23:23:50", "remaining_time": "1 day, 13:37:26"}
+{"current_steps": 1500, "total_steps": 3886, "loss": 0.0097, "lr": 1.542666044436577e-05, "epoch": 0.7721014026508815, "percentage": 38.6, "elapsed_time": "23:33:04", "remaining_time": "1 day, 13:27:43"}