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

0adb156

verified ·

1 Parent(s): c533052

Training in progress, step 1700

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 sha256:334e69974a98bade9fd0ca1430525bcf46817c708ce62823a2737582e19df558
 size 3554214752

 version https://git-lfs.github.com/spec/v1
+oid sha256:bc02b689f1f1c9bcf53c5a65b1d74f9883b5b1000f0a7a9cda9e5a07a2afbc8d
 size 3554214752

trainer_log.jsonl CHANGED Viewed

@@ -158,3 +158,13 @@
 {"current_steps": 1580, "total_steps": 3886, "loss": 0.0096, "lr": 1.480950481221795e-05, "epoch": 0.8132801441255951, "percentage": 40.66, "elapsed_time": "1 day, 0:49:01", "remaining_time": "1 day, 12:13:13"}
 {"current_steps": 1590, "total_steps": 3886, "loss": 0.0093, "lr": 1.4730547586762114e-05, "epoch": 0.8184274868099344, "percentage": 40.92, "elapsed_time": "1 day, 0:58:13", "remaining_time": "1 day, 12:03:27"}
 {"current_steps": 1600, "total_steps": 3886, "loss": 0.0101, "lr": 1.4651208577964099e-05, "epoch": 0.8235748294942736, "percentage": 41.17, "elapsed_time": "1 day, 1:07:28", "remaining_time": "1 day, 11:53:48"}

 {"current_steps": 1580, "total_steps": 3886, "loss": 0.0096, "lr": 1.480950481221795e-05, "epoch": 0.8132801441255951, "percentage": 40.66, "elapsed_time": "1 day, 0:49:01", "remaining_time": "1 day, 12:13:13"}
 {"current_steps": 1590, "total_steps": 3886, "loss": 0.0093, "lr": 1.4730547586762114e-05, "epoch": 0.8184274868099344, "percentage": 40.92, "elapsed_time": "1 day, 0:58:13", "remaining_time": "1 day, 12:03:27"}
 {"current_steps": 1600, "total_steps": 3886, "loss": 0.0101, "lr": 1.4651208577964099e-05, "epoch": 0.8235748294942736, "percentage": 41.17, "elapsed_time": "1 day, 1:07:28", "remaining_time": "1 day, 11:53:48"}
+{"current_steps": 1610, "total_steps": 3886, "loss": 0.01, "lr": 1.4571494188954058e-05, "epoch": 0.8287221721786128, "percentage": 41.43, "elapsed_time": "1 day, 1:18:32", "remaining_time": "1 day, 11:46:42"}
+{"current_steps": 1620, "total_steps": 3886, "loss": 0.0095, "lr": 1.4491410853157577e-05, "epoch": 0.833869514862952, "percentage": 41.69, "elapsed_time": "1 day, 1:27:35", "remaining_time": "1 day, 11:36:43"}
+{"current_steps": 1630, "total_steps": 3886, "loss": 0.0099, "lr": 1.4410965033776439e-05, "epoch": 0.8390168575472912, "percentage": 41.95, "elapsed_time": "1 day, 1:36:48", "remaining_time": "1 day, 11:27:01"}
+{"current_steps": 1640, "total_steps": 3886, "loss": 0.01, "lr": 1.4330163223267005e-05, "epoch": 0.8441642002316304, "percentage": 42.2, "elapsed_time": "1 day, 1:46:08", "remaining_time": "1 day, 11:17:27"}
+{"current_steps": 1650, "total_steps": 3886, "loss": 0.0099, "lr": 1.4249011942816245e-05, "epoch": 0.8493115429159697, "percentage": 42.46, "elapsed_time": "1 day, 1:55:18", "remaining_time": "1 day, 11:07:40"}
+{"current_steps": 1660, "total_steps": 3886, "loss": 0.0097, "lr": 1.416751774181543e-05, "epoch": 0.8544588856003088, "percentage": 42.72, "elapsed_time": "1 day, 2:04:32", "remaining_time": "1 day, 10:58:00"}
+{"current_steps": 1670, "total_steps": 3886, "loss": 0.0097, "lr": 1.4085687197331559e-05, "epoch": 0.859606228284648, "percentage": 42.97, "elapsed_time": "1 day, 2:13:55", "remaining_time": "1 day, 10:48:31"}
+{"current_steps": 1680, "total_steps": 3886, "loss": 0.0103, "lr": 1.4003526913576555e-05, "epoch": 0.8647535709689873, "percentage": 43.23, "elapsed_time": "1 day, 2:23:11", "remaining_time": "1 day, 10:38:53"}
+{"current_steps": 1690, "total_steps": 3886, "loss": 0.0091, "lr": 1.392104352137426e-05, "epoch": 0.8699009136533264, "percentage": 43.49, "elapsed_time": "1 day, 2:32:28", "remaining_time": "1 day, 10:29:16"}
+{"current_steps": 1700, "total_steps": 3886, "loss": 0.0099, "lr": 1.3838243677625292e-05, "epoch": 0.8750482563376657, "percentage": 43.75, "elapsed_time": "1 day, 2:41:49", "remaining_time": "1 day, 10:19:45"}