Instructions to use rbelanec/train_stsb_42_1760637583 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rbelanec/train_stsb_42_1760637583 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_42_1760637583")

Transformers

How to use rbelanec/train_stsb_42_1760637583 with Transformers:

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

pipe = pipeline("text-generation", model="rbelanec/train_stsb_42_1760637583")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rbelanec/train_stsb_42_1760637583", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rbelanec/train_stsb_42_1760637583 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rbelanec/train_stsb_42_1760637583"
# 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_42_1760637583",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rbelanec/train_stsb_42_1760637583

SGLang

How to use rbelanec/train_stsb_42_1760637583 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_42_1760637583" \
    --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_42_1760637583",
		"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_42_1760637583" \
        --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_42_1760637583",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rbelanec/train_stsb_42_1760637583 with Docker Model Runner:
```
docker model run hf.co/rbelanec/train_stsb_42_1760637583
```

train_stsb_42_1760637583

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: 4.6624
Num Input Tokens Seen: 8733312

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
5.1311	1.0	1294	4.8549	435696
4.533	2.0	2588	4.7069	872288
5.05	3.0	3882	4.6819	1309872
4.33	4.0	5176	4.6632	1747344
4.3298	5.0	6470	4.6738	2184032
4.4377	6.0	7764	4.6624	2622912
4.7251	7.0	9058	4.6698	3059648
4.8367	8.0	10352	4.6727	3496896
4.8132	9.0	11646	4.6701	3934000
4.6233	10.0	12940	4.6722	4369680
4.7196	11.0	14234	4.6824	4807440
4.7721	12.0	15528	4.6716	5243776
4.719	13.0	16822	4.6645	5681232
4.6732	14.0	18116	4.6693	6118000
4.5954	15.0	19410	4.6714	6554032
4.6003	16.0	20704	4.6758	6989408
4.4188	17.0	21998	4.6758	7425264
4.6091	18.0	23292	4.6639	7861712
4.4443	19.0	24586	4.6639	8297664
4.7556	20.0	25880	4.6639	8733312

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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