Instructions to use adpretko/train-riscv-O2_epoch1and2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use adpretko/train-riscv-O2_epoch1and2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="adpretko/train-riscv-O2_epoch1and2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("adpretko/train-riscv-O2_epoch1and2")
model = AutoModelForCausalLM.from_pretrained("adpretko/train-riscv-O2_epoch1and2", device_map="auto")
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=256)
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_epoch1and2 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_epoch1and2"
# 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_epoch1and2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/adpretko/train-riscv-O2_epoch1and2

SGLang

How to use adpretko/train-riscv-O2_epoch1and2 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_epoch1and2" \
    --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_epoch1and2",
		"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_epoch1and2" \
        --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_epoch1and2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use adpretko/train-riscv-O2_epoch1and2 with Docker Model Runner:
```
docker model run hf.co/adpretko/train-riscv-O2_epoch1and2
```

adpretko commited on Nov 1, 2025

Commit

974556c

verified ·

1 Parent(s): c1ca1e6

Training in progress, step 1900

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:df7d9ae75340cc3fbf56f47c5c5087f5b5a9cba3faac02db6004ef42f3ee1ce8
 size 3554214752

 version https://git-lfs.github.com/spec/v1
+oid sha256:8e84caaf68f3293b54f7522db1d243ec3db460b3430738ec1a79f6669ec28f66
 size 3554214752

trainer_log.jsonl CHANGED Viewed

@@ -178,3 +178,13 @@
 {"current_steps": 1780, "total_steps": 3886, "loss": 0.0141, "lr": 1.316525967798976e-05, "epoch": 0.9162269978123794, "percentage": 45.81, "elapsed_time": "1 day, 3:43:41", "remaining_time": "1 day, 8:48:23"}
 {"current_steps": 1790, "total_steps": 3886, "loss": 0.0146, "lr": 1.307991536598469e-05, "epoch": 0.9213743404967185, "percentage": 46.06, "elapsed_time": "1 day, 3:52:56", "remaining_time": "1 day, 8:38:56"}
 {"current_steps": 1800, "total_steps": 3886, "loss": 0.0137, "lr": 1.2994322486480716e-05, "epoch": 0.9265216831810578, "percentage": 46.32, "elapsed_time": "1 day, 4:02:15", "remaining_time": "1 day, 8:29:32"}

 {"current_steps": 1780, "total_steps": 3886, "loss": 0.0141, "lr": 1.316525967798976e-05, "epoch": 0.9162269978123794, "percentage": 45.81, "elapsed_time": "1 day, 3:43:41", "remaining_time": "1 day, 8:48:23"}
 {"current_steps": 1790, "total_steps": 3886, "loss": 0.0146, "lr": 1.307991536598469e-05, "epoch": 0.9213743404967185, "percentage": 46.06, "elapsed_time": "1 day, 3:52:56", "remaining_time": "1 day, 8:38:56"}
 {"current_steps": 1800, "total_steps": 3886, "loss": 0.0137, "lr": 1.2994322486480716e-05, "epoch": 0.9265216831810578, "percentage": 46.32, "elapsed_time": "1 day, 4:02:15", "remaining_time": "1 day, 8:29:32"}
+{"current_steps": 1810, "total_steps": 3886, "loss": 0.0135, "lr": 1.2908487947332588e-05, "epoch": 0.931669025865397, "percentage": 46.58, "elapsed_time": "1 day, 4:13:04", "remaining_time": "1 day, 8:21:53"}
+{"current_steps": 1820, "total_steps": 3886, "loss": 0.0146, "lr": 1.2822418675898428e-05, "epoch": 0.9368163685497362, "percentage": 46.83, "elapsed_time": "1 day, 4:22:13", "remaining_time": "1 day, 8:12:18"}
+{"current_steps": 1830, "total_steps": 3886, "loss": 0.0137, "lr": 1.273612161848064e-05, "epoch": 0.9419637112340754, "percentage": 47.09, "elapsed_time": "1 day, 4:31:34", "remaining_time": "1 day, 8:02:57"}
+{"current_steps": 1840, "total_steps": 3886, "loss": 0.0134, "lr": 1.2649603739765323e-05, "epoch": 0.9471110539184147, "percentage": 47.35, "elapsed_time": "1 day, 4:40:42", "remaining_time": "1 day, 7:53:21"}
+{"current_steps": 1850, "total_steps": 3886, "loss": 0.0142, "lr": 1.2562872022260168e-05, "epoch": 0.9522583966027538, "percentage": 47.61, "elapsed_time": "1 day, 4:50:01", "remaining_time": "1 day, 7:43:57"}
+{"current_steps": 1860, "total_steps": 3886, "loss": 0.0134, "lr": 1.2475933465730923e-05, "epoch": 0.957405739287093, "percentage": 47.86, "elapsed_time": "1 day, 4:59:07", "remaining_time": "1 day, 7:34:19"}
+{"current_steps": 1870, "total_steps": 3886, "loss": 0.0136, "lr": 1.238879508663648e-05, "epoch": 0.9625530819714323, "percentage": 48.12, "elapsed_time": "1 day, 5:08:19", "remaining_time": "1 day, 7:24:49"}
+{"current_steps": 1880, "total_steps": 3886, "loss": 0.0138, "lr": 1.2301463917562602e-05, "epoch": 0.9677004246557714, "percentage": 48.38, "elapsed_time": "1 day, 5:17:33", "remaining_time": "1 day, 7:15:20"}
+{"current_steps": 1890, "total_steps": 3886, "loss": 0.0136, "lr": 1.2213947006654347e-05, "epoch": 0.9728477673401107, "percentage": 48.64, "elapsed_time": "1 day, 5:26:37", "remaining_time": "1 day, 7:05:42"}
+{"current_steps": 1900, "total_steps": 3886, "loss": 0.0144, "lr": 1.212625141704725e-05, "epoch": 0.9779951100244498, "percentage": 48.89, "elapsed_time": "1 day, 5:35:44", "remaining_time": "1 day, 6:56:07"}