Instructions to use rbelanec/train_rte_101112_1760638014 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rbelanec/train_rte_101112_1760638014 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_rte_101112_1760638014")

Transformers

How to use rbelanec/train_rte_101112_1760638014 with Transformers:

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

pipe = pipeline("text-generation", model="rbelanec/train_rte_101112_1760638014")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rbelanec/train_rte_101112_1760638014", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rbelanec/train_rte_101112_1760638014 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rbelanec/train_rte_101112_1760638014"
# 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_rte_101112_1760638014",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rbelanec/train_rte_101112_1760638014

SGLang

How to use rbelanec/train_rte_101112_1760638014 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_rte_101112_1760638014" \
    --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_rte_101112_1760638014",
		"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_rte_101112_1760638014" \
        --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_rte_101112_1760638014",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rbelanec/train_rte_101112_1760638014 with Docker Model Runner:
```
docker model run hf.co/rbelanec/train_rte_101112_1760638014
```

train_rte_101112_1760638014

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

Loss: 0.0625
Num Input Tokens Seen: 6980984

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: 101112
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
0.0084	1.0	561	0.0786	350480
0.0693	2.0	1122	0.0625	700992
0.0546	3.0	1683	0.0631	1050848
0.0619	4.0	2244	0.1006	1400856
0.0	5.0	2805	0.1246	1749544
0.0	6.0	3366	0.1842	2099368
0.0	7.0	3927	0.1637	2447504
0.0	8.0	4488	0.1264	2794592
0.0	9.0	5049	0.2143	3145760
0.0	10.0	5610	0.1665	3495600
0.0001	11.0	6171	0.1197	3844488
0.0	12.0	6732	0.1446	4191800
0.0	13.0	7293	0.1505	4538416
0.0	14.0	7854	0.1551	4888904
0.0	15.0	8415	0.1557	5236560
0.0	16.0	8976	0.1649	5587768
0.0	17.0	9537	0.1629	5935088
0.0	18.0	10098	0.1677	6283144
0.0	19.0	10659	0.1635	6632504
0.0	20.0	11220	0.1673	6980984

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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