Instructions to use rbelanec/train_stsb_123_1760637696 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rbelanec/train_stsb_123_1760637696 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_1760637696")

Transformers

How to use rbelanec/train_stsb_123_1760637696 with Transformers:

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

pipe = pipeline("text-generation", model="rbelanec/train_stsb_123_1760637696")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rbelanec/train_stsb_123_1760637696", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rbelanec/train_stsb_123_1760637696 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_1760637696"
# 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_1760637696",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rbelanec/train_stsb_123_1760637696

SGLang

How to use rbelanec/train_stsb_123_1760637696 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_1760637696" \
    --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_1760637696",
		"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_1760637696" \
        --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_1760637696",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rbelanec/train_stsb_123_1760637696 with Docker Model Runner:
```
docker model run hf.co/rbelanec/train_stsb_123_1760637696
```

train_stsb_123_1760637696

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: 2.3309
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: 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
0.5796	1.0	1294	0.7036	435488
0.405	2.0	2588	0.6050	871200
0.4826	3.0	3882	0.5194	1307968
0.355	4.0	5176	0.5213	1745568
0.6005	5.0	6470	0.4893	2182352
0.5369	6.0	7764	0.4895	2619888
0.3825	7.0	9058	0.4864	3057216
0.3519	8.0	10352	0.4998	3493600
0.3994	9.0	11646	0.4883	3928704
0.4038	10.0	12940	0.4858	4364240
0.4779	11.0	14234	0.4757	4800144
0.3692	12.0	15528	0.4697	5234320
0.3688	13.0	16822	0.4744	5670720
0.404	14.0	18116	0.4865	6108240
0.2171	15.0	19410	0.4986	6543632
0.3107	16.0	20704	0.5108	6978816
0.2954	17.0	21998	0.5228	7415824
0.3044	18.0	23292	0.5332	7851088
0.255	19.0	24586	0.5385	8287536
0.2648	20.0	25880	0.5399	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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