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

ce7bc8e

verified ·

1 Parent(s): 8a4989e

Training in progress, step 800

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:c2c957a1bcb3b38638fad769f2d37cb7cae7cfef3b412f967e910bac7e237fe6
 size 3554214752

 version https://git-lfs.github.com/spec/v1
+oid sha256:7373b3c7c257e39b812de422200af98e2e426c7da65d68f81f72c25cb79b992e
 size 3554214752

trainer_log.jsonl CHANGED Viewed

@@ -68,3 +68,13 @@
 {"current_steps": 680, "total_steps": 3886, "loss": 0.0113, "lr": 1.9662544397960182e-05, "epoch": 0.35001930253506625, "percentage": 17.5, "elapsed_time": "10:41:30", "remaining_time": "2 days, 2:24:31"}
 {"current_steps": 690, "total_steps": 3886, "loss": 0.0113, "lr": 1.9639013780972958e-05, "epoch": 0.3551666452194055, "percentage": 17.76, "elapsed_time": "10:50:44", "remaining_time": "2 days, 2:14:09"}
 {"current_steps": 700, "total_steps": 3886, "loss": 0.0115, "lr": 1.961470523821093e-05, "epoch": 0.3603139879037447, "percentage": 18.01, "elapsed_time": "11:00:00", "remaining_time": "2 days, 2:03:58"}

 {"current_steps": 680, "total_steps": 3886, "loss": 0.0113, "lr": 1.9662544397960182e-05, "epoch": 0.35001930253506625, "percentage": 17.5, "elapsed_time": "10:41:30", "remaining_time": "2 days, 2:24:31"}
 {"current_steps": 690, "total_steps": 3886, "loss": 0.0113, "lr": 1.9639013780972958e-05, "epoch": 0.3551666452194055, "percentage": 17.76, "elapsed_time": "10:50:44", "remaining_time": "2 days, 2:14:09"}
 {"current_steps": 700, "total_steps": 3886, "loss": 0.0115, "lr": 1.961470523821093e-05, "epoch": 0.3603139879037447, "percentage": 18.01, "elapsed_time": "11:00:00", "remaining_time": "2 days, 2:03:58"}
+{"current_steps": 710, "total_steps": 3886, "loss": 0.0111, "lr": 1.9589620731518166e-05, "epoch": 0.3654613305880839, "percentage": 18.27, "elapsed_time": "11:10:49", "remaining_time": "2 days, 2:00:47"}
+{"current_steps": 720, "total_steps": 3886, "loss": 0.0109, "lr": 1.956376228536363e-05, "epoch": 0.37060867327242314, "percentage": 18.53, "elapsed_time": "11:20:01", "remaining_time": "2 days, 1:50:13"}
+{"current_steps": 730, "total_steps": 3886, "loss": 0.0116, "lr": 1.953713198667782e-05, "epoch": 0.37575601595676233, "percentage": 18.79, "elapsed_time": "11:29:16", "remaining_time": "2 days, 1:39:55"}
+{"current_steps": 740, "total_steps": 3886, "loss": 0.0109, "lr": 1.950973198468431e-05, "epoch": 0.38090335864110153, "percentage": 19.04, "elapsed_time": "11:38:26", "remaining_time": "2 days, 1:29:20"}
+{"current_steps": 750, "total_steps": 3886, "loss": 0.0116, "lr": 1.9481564490726327e-05, "epoch": 0.3860507013254407, "percentage": 19.3, "elapsed_time": "11:47:40", "remaining_time": "2 days, 1:19:01"}
+{"current_steps": 760, "total_steps": 3886, "loss": 0.0109, "lr": 1.9452631778088262e-05, "epoch": 0.39119804400978, "percentage": 19.56, "elapsed_time": "11:56:48", "remaining_time": "2 days, 1:08:20"}
+{"current_steps": 770, "total_steps": 3886, "loss": 0.011, "lr": 1.94229361818122e-05, "epoch": 0.3963453866941192, "percentage": 19.81, "elapsed_time": "12:06:00", "remaining_time": "2 days, 0:57:59"}
+{"current_steps": 780, "total_steps": 3886, "loss": 0.0111, "lr": 1.9392480098509488e-05, "epoch": 0.40149272937845837, "percentage": 20.07, "elapsed_time": "12:15:18", "remaining_time": "2 days, 0:48:03"}
+{"current_steps": 790, "total_steps": 3886, "loss": 0.0112, "lr": 1.9361265986167292e-05, "epoch": 0.40664007206279756, "percentage": 20.33, "elapsed_time": "12:24:30", "remaining_time": "2 days, 0:37:42"}
+{"current_steps": 800, "total_steps": 3886, "loss": 0.0111, "lr": 1.9329296363950237e-05, "epoch": 0.4117874147471368, "percentage": 20.59, "elapsed_time": "12:33:46", "remaining_time": "2 days, 0:27:42"}