Instructions to use rbelanec/train_hellaswag_123_1760637745 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rbelanec/train_hellaswag_123_1760637745 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_123_1760637745")

Transformers

How to use rbelanec/train_hellaswag_123_1760637745 with Transformers:

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

pipe = pipeline("text-generation", model="rbelanec/train_hellaswag_123_1760637745")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rbelanec/train_hellaswag_123_1760637745", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rbelanec/train_hellaswag_123_1760637745 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rbelanec/train_hellaswag_123_1760637745"
# 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_123_1760637745",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rbelanec/train_hellaswag_123_1760637745

SGLang

How to use rbelanec/train_hellaswag_123_1760637745 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_123_1760637745" \
    --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_123_1760637745",
		"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_123_1760637745" \
        --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_123_1760637745",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rbelanec/train_hellaswag_123_1760637745 with Docker Model Runner:
```
docker model run hf.co/rbelanec/train_hellaswag_123_1760637745
```

train_hellaswag_123_1760637745

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: 0.0771
Num Input Tokens Seen: 218506144

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: 123
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
0.1942	1.0	8979	0.1737	10932896
0.026	2.0	17958	0.1204	21856400
0.1132	3.0	26937	0.0994	32797696
0.0637	4.0	35916	0.0902	43715520
0.0597	5.0	44895	0.0837	54639040
0.2529	6.0	53874	0.0812	65562352
0.0659	7.0	62853	0.0793	76495264
0.085	8.0	71832	0.0779	87424000
0.0729	9.0	80811	0.0771	98355744
0.0876	10.0	89790	0.0781	109279616
0.0054	11.0	98769	0.0772	120190896
0.1594	12.0	107748	0.0799	131118336
0.008	13.0	116727	0.0797	142033584
0.0087	14.0	125706	0.0799	152960704
0.0414	15.0	134685	0.0806	163884192
0.0072	16.0	143664	0.0813	174816592
0.0756	17.0	152643	0.0817	185740864
0.0044	18.0	161622	0.0822	196657440
0.0566	19.0	170601	0.0824	207581424
0.0165	20.0	179580	0.0824	218506144

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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