Instructions to use rbelanec/train_copa_789_1760637873 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rbelanec/train_copa_789_1760637873 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
model = PeftModel.from_pretrained(base_model, "rbelanec/train_copa_789_1760637873")

Transformers

How to use rbelanec/train_copa_789_1760637873 with Transformers:

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

pipe = pipeline("text-generation", model="rbelanec/train_copa_789_1760637873")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rbelanec/train_copa_789_1760637873", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rbelanec/train_copa_789_1760637873 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rbelanec/train_copa_789_1760637873"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rbelanec/train_copa_789_1760637873",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rbelanec/train_copa_789_1760637873

SGLang

How to use rbelanec/train_copa_789_1760637873 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 "rbelanec/train_copa_789_1760637873" \
    --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": "rbelanec/train_copa_789_1760637873",
		"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 "rbelanec/train_copa_789_1760637873" \
        --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": "rbelanec/train_copa_789_1760637873",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rbelanec/train_copa_789_1760637873 with Docker Model Runner:
```
docker model run hf.co/rbelanec/train_copa_789_1760637873
```

train_copa_789_1760637873

This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the copa dataset. It achieves the following results on the evaluation set:

Loss: 0.2308
Num Input Tokens Seen: 563744

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.03
train_batch_size: 4
eval_batch_size: 4
seed: 789
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Input Tokens Seen
0.2715	1.0	90	0.2760	28192
0.2409	2.0	180	0.2308	56192
0.2493	3.0	270	0.2417	84192
0.2453	4.0	360	0.2346	112544
0.2262	5.0	450	0.2422	140960
0.2318	6.0	540	0.2319	169216
0.2352	7.0	630	0.2362	197248
0.2421	8.0	720	0.2336	225440
0.2379	9.0	810	0.2355	253632
0.2244	10.0	900	0.2314	281984
0.2442	11.0	990	0.2330	310176
0.2306	12.0	1080	0.2360	338400
0.2275	13.0	1170	0.2338	366688
0.2299	14.0	1260	0.2351	394752
0.2361	15.0	1350	0.2367	422912
0.236	16.0	1440	0.2350	451008
0.2338	17.0	1530	0.2350	479104
0.2308	18.0	1620	0.2360	507392
0.2297	19.0	1710	0.2361	535584
0.2245	20.0	1800	0.2376	563744

Framework versions

PEFT 0.17.1
Transformers 4.51.3
Pytorch 2.9.0+cu128
Datasets 4.0.0
Tokenizers 0.21.4

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