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

aacd58b

verified ·

1 Parent(s): 870a264

Training in progress, step 3200

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 sha256:79de5abf3cd29aa8e26f878987b5f14516b41cc7fa3a83845861ab37d0441161
 size 3554214752

 version https://git-lfs.github.com/spec/v1
+oid sha256:0cf70aa7c330e0ac25fb75ad31852e6409635f93f291680cad5649720c592511
 size 3554214752

trainer_log.jsonl CHANGED Viewed

@@ -308,3 +308,13 @@
 {"current_steps": 3080, "total_steps": 3886, "loss": 0.0075, "lr": 2.514892518288988e-06, "epoch": 1.585252863209368, "percentage": 79.26, "elapsed_time": "2 days, 0:23:20", "remaining_time": "12:39:46"}
 {"current_steps": 3090, "total_steps": 3886, "loss": 0.0073, "lr": 2.4556225529366316e-06, "epoch": 1.5904002058937072, "percentage": 79.52, "elapsed_time": "2 days, 0:32:40", "remaining_time": "12:30:19"}
 {"current_steps": 3100, "total_steps": 3886, "loss": 0.0078, "lr": 2.3969614637415474e-06, "epoch": 1.5955475485780466, "percentage": 79.77, "elapsed_time": "2 days, 0:41:53", "remaining_time": "12:20:50"}

 {"current_steps": 3080, "total_steps": 3886, "loss": 0.0075, "lr": 2.514892518288988e-06, "epoch": 1.585252863209368, "percentage": 79.26, "elapsed_time": "2 days, 0:23:20", "remaining_time": "12:39:46"}
 {"current_steps": 3090, "total_steps": 3886, "loss": 0.0073, "lr": 2.4556225529366316e-06, "epoch": 1.5904002058937072, "percentage": 79.52, "elapsed_time": "2 days, 0:32:40", "remaining_time": "12:30:19"}
 {"current_steps": 3100, "total_steps": 3886, "loss": 0.0078, "lr": 2.3969614637415474e-06, "epoch": 1.5955475485780466, "percentage": 79.77, "elapsed_time": "2 days, 0:41:53", "remaining_time": "12:20:50"}
+{"current_steps": 3110, "total_steps": 3886, "loss": 0.0071, "lr": 2.338913985002739e-06, "epoch": 1.600694891262386, "percentage": 80.03, "elapsed_time": "2 days, 0:52:56", "remaining_time": "12:11:49"}
+{"current_steps": 3120, "total_steps": 3886, "loss": 0.0072, "lr": 2.281484801497186e-06, "epoch": 1.605842233946725, "percentage": 80.29, "elapsed_time": "2 days, 1:02:24", "remaining_time": "12:02:23"}
+{"current_steps": 3130, "total_steps": 3886, "loss": 0.0068, "lr": 2.224678548101762e-06, "epoch": 1.6109895766310642, "percentage": 80.55, "elapsed_time": "2 days, 1:11:49", "remaining_time": "11:52:57"}
+{"current_steps": 3140, "total_steps": 3886, "loss": 0.0071, "lr": 2.168499809419177e-06, "epoch": 1.6161369193154034, "percentage": 80.8, "elapsed_time": "2 days, 1:21:07", "remaining_time": "11:43:30"}
+{"current_steps": 3150, "total_steps": 3886, "loss": 0.0072, "lr": 2.1129531194079656e-06, "epoch": 1.6212842619997425, "percentage": 81.06, "elapsed_time": "2 days, 1:30:17", "remaining_time": "11:34:00"}
+{"current_steps": 3160, "total_steps": 3886, "loss": 0.0073, "lr": 2.0580429610165763e-06, "epoch": 1.6264316046840819, "percentage": 81.32, "elapsed_time": "2 days, 1:39:33", "remaining_time": "11:24:32"}
+{"current_steps": 3170, "total_steps": 3886, "loss": 0.0074, "lr": 2.0037737658215684e-06, "epoch": 1.631578947368421, "percentage": 81.57, "elapsed_time": "2 days, 1:48:50", "remaining_time": "11:15:04"}
+{"current_steps": 3180, "total_steps": 3886, "loss": 0.0074, "lr": 1.950149913669953e-06, "epoch": 1.6367262900527604, "percentage": 81.83, "elapsed_time": "2 days, 1:58:01", "remaining_time": "11:05:35"}
+{"current_steps": 3190, "total_steps": 3886, "loss": 0.0072, "lr": 1.89717573232572e-06, "epoch": 1.6418736327370995, "percentage": 82.09, "elapsed_time": "2 days, 2:07:09", "remaining_time": "10:56:06"}
+{"current_steps": 3200, "total_steps": 3886, "loss": 0.0074, "lr": 1.844855497120558e-06, "epoch": 1.6470209754214387, "percentage": 82.35, "elapsed_time": "2 days, 2:16:32", "remaining_time": "10:46:40"}