Instructions to use rbelanec/train_hellaswag_123_1760637740 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rbelanec/train_hellaswag_123_1760637740 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_1760637740")

Transformers

How to use rbelanec/train_hellaswag_123_1760637740 with Transformers:

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

pipe = pipeline("text-generation", model="rbelanec/train_hellaswag_123_1760637740")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rbelanec/train_hellaswag_123_1760637740", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rbelanec/train_hellaswag_123_1760637740 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_1760637740"
# 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_1760637740",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rbelanec/train_hellaswag_123_1760637740

SGLang

How to use rbelanec/train_hellaswag_123_1760637740 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_1760637740" \
    --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_1760637740",
		"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_1760637740" \
        --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_1760637740",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rbelanec/train_hellaswag_123_1760637740 with Docker Model Runner:
```
docker model run hf.co/rbelanec/train_hellaswag_123_1760637740
```

train_hellaswag_123_1760637740

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: 0.4621
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: 0.03
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
0.4688	1.0	8979	0.4636	10932896
0.4573	2.0	17958	0.4627	21856400
0.46	3.0	26937	0.4627	32797696
0.4644	4.0	35916	0.4622	43715520
0.4609	5.0	44895	0.4626	54639040
0.4619	6.0	53874	0.4626	65562352
0.4554	7.0	62853	0.4621	76495264
0.4586	8.0	71832	0.4625	87424000
0.4637	9.0	80811	0.4625	98355744
0.4647	10.0	89790	0.4625	109279616
0.4614	11.0	98769	0.4625	120190896
0.4631	12.0	107748	0.4623	131118336
0.4628	13.0	116727	0.4624	142033584
0.4598	14.0	125706	0.4621	152960704
0.4625	15.0	134685	0.4621	163884192
0.461	16.0	143664	0.4624	174816592
0.4611	17.0	152643	0.4621	185740864
0.4607	18.0	161622	0.4621	196657440
0.4621	19.0	170601	0.4621	207581424
0.4597	20.0	179580	0.4621	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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