Instructions to use rbelanec/train_stsb_123_1760637700 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rbelanec/train_stsb_123_1760637700 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_stsb_123_1760637700")

Transformers

How to use rbelanec/train_stsb_123_1760637700 with Transformers:

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

pipe = pipeline("text-generation", model="rbelanec/train_stsb_123_1760637700")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rbelanec/train_stsb_123_1760637700", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rbelanec/train_stsb_123_1760637700 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rbelanec/train_stsb_123_1760637700"
# 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_stsb_123_1760637700",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rbelanec/train_stsb_123_1760637700

SGLang

How to use rbelanec/train_stsb_123_1760637700 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_stsb_123_1760637700" \
    --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_stsb_123_1760637700",
		"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_stsb_123_1760637700" \
        --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_stsb_123_1760637700",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rbelanec/train_stsb_123_1760637700 with Docker Model Runner:
```
docker model run hf.co/rbelanec/train_stsb_123_1760637700
```

train_stsb_123_1760637700

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

Loss: 0.5127
Num Input Tokens Seen: 8725024

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: 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
1.1276	1.0	1294	1.1261	435488
0.5037	2.0	2588	0.6961	871200
0.6182	3.0	3882	0.6256	1307968
0.3773	4.0	5176	0.5923	1745568
0.6961	5.0	6470	0.5698	2182352
0.5883	6.0	7764	0.5536	2619888
0.4578	7.0	9058	0.5439	3057216
0.4402	8.0	10352	0.5379	3493600
0.4747	9.0	11646	0.5294	3928704
0.4438	10.0	12940	0.5248	4364240
0.4731	11.0	14234	0.5212	4800144
0.4504	12.0	15528	0.5178	5234320
0.5421	13.0	16822	0.5169	5670720
0.4152	14.0	18116	0.5145	6108240
0.3473	15.0	19410	0.5141	6543632
0.4726	16.0	20704	0.5141	6978816
0.4865	17.0	21998	0.5137	7415824
0.4733	18.0	23292	0.5127	7851088
0.3958	19.0	24586	0.5140	8287536
0.4228	20.0	25880	0.5127	8725024

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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