Instructions to use rbelanec/train_hellaswag_42_1760637628 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rbelanec/train_hellaswag_42_1760637628 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_42_1760637628")

Transformers

How to use rbelanec/train_hellaswag_42_1760637628 with Transformers:

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

pipe = pipeline("text-generation", model="rbelanec/train_hellaswag_42_1760637628")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rbelanec/train_hellaswag_42_1760637628", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rbelanec/train_hellaswag_42_1760637628 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rbelanec/train_hellaswag_42_1760637628"
# 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_42_1760637628",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rbelanec/train_hellaswag_42_1760637628

SGLang

How to use rbelanec/train_hellaswag_42_1760637628 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_42_1760637628" \
    --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_42_1760637628",
		"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_42_1760637628" \
        --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_42_1760637628",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rbelanec/train_hellaswag_42_1760637628 with Docker Model Runner:
```
docker model run hf.co/rbelanec/train_hellaswag_42_1760637628
```

train_hellaswag_42_1760637628

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: 0.7116
Num Input Tokens Seen: 218263888

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: 42
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
0.8732	1.0	8979	1.0233	10917120
0.7955	2.0	17958	0.7851	21836032
0.7456	3.0	26937	0.7186	32746560
0.766	4.0	35916	0.7116	43661424
0.6636	5.0	44895	0.7137	54578912
0.6306	6.0	53874	0.7167	65488016
0.8581	7.0	62853	0.7175	76410304
0.7264	8.0	71832	0.7148	87327296
0.7082	9.0	80811	0.7139	98229232
0.5871	10.0	89790	0.7167	109127968
0.6326	11.0	98769	0.7145	120042688
0.8029	12.0	107748	0.7155	130954720
0.7298	13.0	116727	0.7155	141874656
0.6079	14.0	125706	0.7155	152783392
0.5391	15.0	134685	0.7155	163694096
0.7002	16.0	143664	0.7155	174604544
0.5754	17.0	152643	0.7155	185523328
0.7789	18.0	161622	0.7155	196433472
0.5864	19.0	170601	0.7155	207345200
0.7214	20.0	179580	0.7155	218263888

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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