Instructions to use rbelanec/train_sst2_123_1760637735 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rbelanec/train_sst2_123_1760637735 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_sst2_123_1760637735")

Transformers

How to use rbelanec/train_sst2_123_1760637735 with Transformers:

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

pipe = pipeline("text-generation", model="rbelanec/train_sst2_123_1760637735")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rbelanec/train_sst2_123_1760637735", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rbelanec/train_sst2_123_1760637735 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rbelanec/train_sst2_123_1760637735"
# 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_sst2_123_1760637735",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rbelanec/train_sst2_123_1760637735

SGLang

How to use rbelanec/train_sst2_123_1760637735 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_sst2_123_1760637735" \
    --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_sst2_123_1760637735",
		"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_sst2_123_1760637735" \
        --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_sst2_123_1760637735",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rbelanec/train_sst2_123_1760637735 with Docker Model Runner:
```
docker model run hf.co/rbelanec/train_sst2_123_1760637735
```

train_sst2_123_1760637735

This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the sst2 dataset. It achieves the following results on the evaluation set:

Loss: 0.2809
Num Input Tokens Seen: 67743008

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: 0.001
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
0.0427	1.0	15154	0.0658	3385616
0.102	2.0	30308	0.0607	6774096
0.0054	3.0	45462	0.0601	10161824
0.1286	4.0	60616	0.0586	13549104
0.0045	5.0	75770	0.0595	16935568
0.0179	6.0	90924	0.0594	20320896
0.09	7.0	106078	0.0578	23709008
0.004	8.0	121232	0.0583	27099520
0.0295	9.0	136386	0.0605	30484864
0.0162	10.0	151540	0.0662	33869824
0.0142	11.0	166694	0.0644	37256608
0.0082	12.0	181848	0.0678	40640592
0.004	13.0	197002	0.0755	44027424
0.0641	14.0	212156	0.0835	47415744
0.0047	15.0	227310	0.0855	50803600
0.0082	16.0	242464	0.0896	54189408
0.0231	17.0	257618	0.0919	57578368
0.002	18.0	272772	0.1025	60965904
0.1573	19.0	287926	0.1054	64353008
0.0006	20.0	303080	0.1050	67743008

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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