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 3, 2025

Commit

27d15f5

verified ·

1 Parent(s): d3328f3

Training in progress, step 200

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 sha256:89def664f28ba9a3d3ab00fc85dbb4ea22b9390651e1946a67208c1bfa4283c6
 size 3554214752

 version https://git-lfs.github.com/spec/v1
+oid sha256:2e335ee83c57219418c701bb130f28373615eeab1f2973d618da1a2125de02ac
 size 3554214752

trainer_log.jsonl CHANGED Viewed

@@ -8,3 +8,13 @@
 {"current_steps": 80, "total_steps": 3886, "loss": 0.0112, "lr": 4.0616966580976866e-06, "epoch": 0.041178741474713676, "percentage": 2.06, "elapsed_time": "1:15:02", "remaining_time": "2 days, 11:30:20"}
 {"current_steps": 90, "total_steps": 3886, "loss": 0.0113, "lr": 4.575835475578407e-06, "epoch": 0.04632608415905289, "percentage": 2.32, "elapsed_time": "1:24:31", "remaining_time": "2 days, 11:25:23"}
 {"current_steps": 100, "total_steps": 3886, "loss": 0.0115, "lr": 5.0899742930591265e-06, "epoch": 0.0514734268433921, "percentage": 2.57, "elapsed_time": "1:34:06", "remaining_time": "2 days, 11:22:43"}

 {"current_steps": 80, "total_steps": 3886, "loss": 0.0112, "lr": 4.0616966580976866e-06, "epoch": 0.041178741474713676, "percentage": 2.06, "elapsed_time": "1:15:02", "remaining_time": "2 days, 11:30:20"}
 {"current_steps": 90, "total_steps": 3886, "loss": 0.0113, "lr": 4.575835475578407e-06, "epoch": 0.04632608415905289, "percentage": 2.32, "elapsed_time": "1:24:31", "remaining_time": "2 days, 11:25:23"}
 {"current_steps": 100, "total_steps": 3886, "loss": 0.0115, "lr": 5.0899742930591265e-06, "epoch": 0.0514734268433921, "percentage": 2.57, "elapsed_time": "1:34:06", "remaining_time": "2 days, 11:22:43"}
+{"current_steps": 110, "total_steps": 3886, "loss": 0.011, "lr": 5.604113110539846e-06, "epoch": 0.05662076952773131, "percentage": 2.83, "elapsed_time": "1:44:54", "remaining_time": "2 days, 12:00:58"}
+{"current_steps": 120, "total_steps": 3886, "loss": 0.0112, "lr": 6.1182519280205664e-06, "epoch": 0.06176811221207052, "percentage": 3.09, "elapsed_time": "1:54:19", "remaining_time": "2 days, 11:48:04"}
+{"current_steps": 130, "total_steps": 3886, "loss": 0.0114, "lr": 6.632390745501286e-06, "epoch": 0.06691545489640972, "percentage": 3.35, "elapsed_time": "2:03:32", "remaining_time": "2 days, 11:29:11"}
+{"current_steps": 140, "total_steps": 3886, "loss": 0.0116, "lr": 7.1465295629820055e-06, "epoch": 0.07206279758074893, "percentage": 3.6, "elapsed_time": "2:12:54", "remaining_time": "2 days, 11:16:06"}
+{"current_steps": 150, "total_steps": 3886, "loss": 0.0112, "lr": 7.660668380462726e-06, "epoch": 0.07721014026508814, "percentage": 3.86, "elapsed_time": "2:21:52", "remaining_time": "2 days, 10:53:33"}
+{"current_steps": 160, "total_steps": 3886, "loss": 0.0115, "lr": 8.174807197943445e-06, "epoch": 0.08235748294942735, "percentage": 4.12, "elapsed_time": "2:30:56", "remaining_time": "2 days, 10:35:12"}
+{"current_steps": 170, "total_steps": 3886, "loss": 0.0126, "lr": 8.688946015424165e-06, "epoch": 0.08750482563376656, "percentage": 4.37, "elapsed_time": "2:40:14", "remaining_time": "2 days, 10:22:48"}
+{"current_steps": 180, "total_steps": 3886, "loss": 0.0116, "lr": 9.203084832904885e-06, "epoch": 0.09265216831810578, "percentage": 4.63, "elapsed_time": "2:49:30", "remaining_time": "2 days, 10:09:59"}
+{"current_steps": 190, "total_steps": 3886, "loss": 0.0115, "lr": 9.717223650385606e-06, "epoch": 0.097799511002445, "percentage": 4.89, "elapsed_time": "2:58:45", "remaining_time": "2 days, 9:57:21"}
+{"current_steps": 200, "total_steps": 3886, "loss": 0.0114, "lr": 1.0231362467866324e-05, "epoch": 0.1029468536867842, "percentage": 5.15, "elapsed_time": "3:08:01", "remaining_time": "2 days, 9:45:10"}