Instructions to use rbelanec/train_copa_789_1760637877 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rbelanec/train_copa_789_1760637877 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_1760637877")

Transformers

How to use rbelanec/train_copa_789_1760637877 with Transformers:

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

pipe = pipeline("text-generation", model="rbelanec/train_copa_789_1760637877")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rbelanec/train_copa_789_1760637877", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rbelanec/train_copa_789_1760637877 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_1760637877"
# 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_1760637877",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rbelanec/train_copa_789_1760637877

SGLang

How to use rbelanec/train_copa_789_1760637877 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_1760637877" \
    --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_1760637877",
		"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_1760637877" \
        --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_1760637877",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rbelanec/train_copa_789_1760637877 with Docker Model Runner:
```
docker model run hf.co/rbelanec/train_copa_789_1760637877
```

train_copa_789_1760637877

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: 0.0584
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: 5e-05
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
0.6696	1.0	90	0.5326	28192
0.1462	2.0	180	0.0699	56192
0.126	3.0	270	0.0657	84192
0.0396	4.0	360	0.0668	112544
0.1156	5.0	450	0.0635	140960
0.0155	6.0	540	0.0645	169216
0.0091	7.0	630	0.0612	197248
0.1638	8.0	720	0.0614	225440
0.0195	9.0	810	0.0608	253632
0.0328	10.0	900	0.0622	281984
0.0279	11.0	990	0.0616	310176
0.0731	12.0	1080	0.0589	338400
0.0441	13.0	1170	0.0590	366688
0.0081	14.0	1260	0.0601	394752
0.0287	15.0	1350	0.0598	422912
0.0147	16.0	1440	0.0593	451008
0.0935	17.0	1530	0.0584	479104
0.0122	18.0	1620	0.0601	507392
0.0685	19.0	1710	0.0614	535584
0.0292	20.0	1800	0.0607	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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