Instructions to use rbelanec/train_hellaswag_456_1760637857 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rbelanec/train_hellaswag_456_1760637857 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_hellaswag_456_1760637857")

Transformers

How to use rbelanec/train_hellaswag_456_1760637857 with Transformers:

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

pipe = pipeline("text-generation", model="rbelanec/train_hellaswag_456_1760637857")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rbelanec/train_hellaswag_456_1760637857", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rbelanec/train_hellaswag_456_1760637857 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rbelanec/train_hellaswag_456_1760637857"
# 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_hellaswag_456_1760637857",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rbelanec/train_hellaswag_456_1760637857

SGLang

How to use rbelanec/train_hellaswag_456_1760637857 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_hellaswag_456_1760637857" \
    --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_hellaswag_456_1760637857",
		"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_hellaswag_456_1760637857" \
        --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_hellaswag_456_1760637857",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rbelanec/train_hellaswag_456_1760637857 with Docker Model Runner:
```
docker model run hf.co/rbelanec/train_hellaswag_456_1760637857
```

train_hellaswag_456_1760637857

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

Loss: 0.7093
Num Input Tokens Seen: 218351424

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: 456
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
1.0742	1.0	8979	1.0175	10917968
0.8217	2.0	17958	0.7741	21834304
0.4915	3.0	26937	0.7171	32747296
0.5894	4.0	35916	0.7116	43666592
0.5193	5.0	44895	0.7111	54575648
0.7684	6.0	53874	0.7108	65491248
0.8571	7.0	62853	0.7188	76405264
0.9533	8.0	71832	0.7093	87319216
0.6646	9.0	80811	0.7220	98235568
0.7685	10.0	89790	0.7222	109159872
0.6413	11.0	98769	0.7191	120071152
0.8741	12.0	107748	0.7190	130995232
0.6947	13.0	116727	0.7254	141910672
1.1252	14.0	125706	0.7254	152831088
0.8754	15.0	134685	0.7254	163756480
0.7325	16.0	143664	0.7254	174682064
0.7487	17.0	152643	0.7254	185591248
0.7894	18.0	161622	0.7254	196510528
0.6409	19.0	170601	0.7254	207424736
0.6316	20.0	179580	0.7254	218351424

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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