Instructions to use rbelanec/train_sst2_456_1760637851 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rbelanec/train_sst2_456_1760637851 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_456_1760637851")

Transformers

How to use rbelanec/train_sst2_456_1760637851 with Transformers:

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

pipe = pipeline("text-generation", model="rbelanec/train_sst2_456_1760637851")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rbelanec/train_sst2_456_1760637851", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rbelanec/train_sst2_456_1760637851 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rbelanec/train_sst2_456_1760637851"
# 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_456_1760637851",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rbelanec/train_sst2_456_1760637851

SGLang

How to use rbelanec/train_sst2_456_1760637851 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_456_1760637851" \
    --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_456_1760637851",
		"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_456_1760637851" \
        --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_456_1760637851",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rbelanec/train_sst2_456_1760637851 with Docker Model Runner:
```
docker model run hf.co/rbelanec/train_sst2_456_1760637851
```

train_sst2_456_1760637851

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: 0.8334
Num Input Tokens Seen: 67744848

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
0.9669	1.0	15154	0.9100	3391536
0.9341	2.0	30308	0.8540	6778448
0.8366	3.0	45462	0.8535	10165856
0.7148	4.0	60616	0.8443	13553232
0.792	5.0	75770	0.8505	16942544
0.7039	6.0	90924	0.8441	20329264
0.774	7.0	106078	0.8334	23711968
0.7926	8.0	121232	0.8464	27102656
1.0991	9.0	136386	0.8358	30492336
1.0172	10.0	151540	0.8516	33879088
0.9327	11.0	166694	0.8446	37261968
0.746	12.0	181848	0.8395	40650384
0.8472	13.0	197002	0.8403	44037328
0.6921	14.0	212156	0.8517	47424688
0.8701	15.0	227310	0.8517	50810944
0.729	16.0	242464	0.8517	54197216
0.9737	17.0	257618	0.8517	57585488
0.9374	18.0	272772	0.8517	60973008
0.712	19.0	287926	0.8517	64361136
0.7503	20.0	303080	0.8517	67744848

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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