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

c90de70

verified ·

1 Parent(s): ce7bc8e

Training in progress, step 900

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 sha256:7373b3c7c257e39b812de422200af98e2e426c7da65d68f81f72c25cb79b992e
 size 3554214752

 version https://git-lfs.github.com/spec/v1
+oid sha256:8e913b228896edb0c67a85e5836317a84ded5ea5091ee17cce68a732e89a0dbd
 size 3554214752

trainer_log.jsonl CHANGED Viewed

@@ -78,3 +78,13 @@
 {"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"}

 {"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"}
+{"current_steps": 810, "total_steps": 3886, "loss": 0.0113, "lr": 1.929657381199709e-05, "epoch": 0.416934757431476, "percentage": 20.84, "elapsed_time": "12:44:52", "remaining_time": "2 days, 0:24:38"}
+{"current_steps": 820, "total_steps": 3886, "loss": 0.0115, "lr": 1.9263100971212533e-05, "epoch": 0.4220821001158152, "percentage": 21.1, "elapsed_time": "12:54:03", "remaining_time": "2 days, 0:14:12"}
+{"current_steps": 830, "total_steps": 3886, "loss": 0.0114, "lr": 1.922888054305401e-05, "epoch": 0.4272294428001544, "percentage": 21.36, "elapsed_time": "13:03:10", "remaining_time": "2 days, 0:03:35"}
+{"current_steps": 840, "total_steps": 3886, "loss": 0.011, "lr": 1.9193915289313726e-05, "epoch": 0.43237678548449365, "percentage": 21.62, "elapsed_time": "13:12:29", "remaining_time": "1 day, 23:53:42"}
+{"current_steps": 850, "total_steps": 3886, "loss": 0.0108, "lr": 1.9158208031895738e-05, "epoch": 0.43752412816883285, "percentage": 21.87, "elapsed_time": "13:21:42", "remaining_time": "1 day, 23:43:31"}
+{"current_steps": 860, "total_steps": 3886, "loss": 0.011, "lr": 1.9121761652588217e-05, "epoch": 0.44267147085317204, "percentage": 22.13, "elapsed_time": "13:31:03", "remaining_time": "1 day, 23:33:49"}
+{"current_steps": 870, "total_steps": 3886, "loss": 0.0105, "lr": 1.9084579092830873e-05, "epoch": 0.44781881353751124, "percentage": 22.39, "elapsed_time": "13:40:24", "remaining_time": "1 day, 23:24:04"}
+{"current_steps": 880, "total_steps": 3886, "loss": 0.0097, "lr": 1.904666335347755e-05, "epoch": 0.4529661562218505, "percentage": 22.65, "elapsed_time": "13:49:35", "remaining_time": "1 day, 23:13:48"}
+{"current_steps": 890, "total_steps": 3886, "loss": 0.0104, "lr": 1.900801749455406e-05, "epoch": 0.4581134989061897, "percentage": 22.9, "elapsed_time": "13:58:50", "remaining_time": "1 day, 23:03:45"}
+{"current_steps": 900, "total_steps": 3886, "loss": 0.0108, "lr": 1.8968644635011192e-05, "epoch": 0.4632608415905289, "percentage": 23.16, "elapsed_time": "14:08:07", "remaining_time": "1 day, 22:53:53"}