Instructions to use rbelanec/train_sst2_42_1760637621 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rbelanec/train_sst2_42_1760637621 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_42_1760637621")

Transformers

How to use rbelanec/train_sst2_42_1760637621 with Transformers:

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

pipe = pipeline("text-generation", model="rbelanec/train_sst2_42_1760637621")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rbelanec/train_sst2_42_1760637621", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rbelanec/train_sst2_42_1760637621 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rbelanec/train_sst2_42_1760637621"
# 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_42_1760637621",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rbelanec/train_sst2_42_1760637621

SGLang

How to use rbelanec/train_sst2_42_1760637621 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_42_1760637621" \
    --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_42_1760637621",
		"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_42_1760637621" \
        --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_42_1760637621",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rbelanec/train_sst2_42_1760637621 with Docker Model Runner:
```
docker model run hf.co/rbelanec/train_sst2_42_1760637621
```

train_sst2_42_1760637621

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: 0.0640
Num Input Tokens Seen: 67768656

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
0.0247	1.0	15154	0.0640	3383904
0.1484	2.0	30308	0.0716	6774480
0.0016	3.0	45462	0.0735	10163152
0.0502	4.0	60616	0.0910	13552544
0.3178	5.0	75770	0.1166	16941120
0.1327	6.0	90924	0.1176	20331520
0.0001	7.0	106078	0.1640	23721840
0.0001	8.0	121232	0.1532	27111168
0.0379	9.0	136386	0.1800	30498912
0.04	10.0	151540	0.1885	33886240
0.0001	11.0	166694	0.2168	37277536
0.0	12.0	181848	0.2099	40664576
0.0	13.0	197002	0.2239	44053792
0.0	14.0	212156	0.2550	47441984
0.0852	15.0	227310	0.2460	50830480
0.0	16.0	242464	0.2393	54215696
0.0	17.0	257618	0.2930	57603296
0.0	18.0	272772	0.3301	60987568
0.0	19.0	287926	0.3340	64378192
0.0	20.0	303080	0.3361	67768656

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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