Instructions to use rbelanec/train_cola_789_1760637933 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rbelanec/train_cola_789_1760637933 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_cola_789_1760637933")

Transformers

How to use rbelanec/train_cola_789_1760637933 with Transformers:

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

pipe = pipeline("text-generation", model="rbelanec/train_cola_789_1760637933")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rbelanec/train_cola_789_1760637933", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rbelanec/train_cola_789_1760637933 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rbelanec/train_cola_789_1760637933"
# 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_cola_789_1760637933",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rbelanec/train_cola_789_1760637933

SGLang

How to use rbelanec/train_cola_789_1760637933 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_cola_789_1760637933" \
    --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_cola_789_1760637933",
		"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_cola_789_1760637933" \
        --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_cola_789_1760637933",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rbelanec/train_cola_789_1760637933 with Docker Model Runner:
```
docker model run hf.co/rbelanec/train_cola_789_1760637933
```

train_cola_789_1760637933

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

Loss: 0.1701
Num Input Tokens Seen: 7327648

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: 789
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
0.2623	1.0	1924	0.1701	365728
0.2018	2.0	3848	0.1763	731984
0.3552	3.0	5772	0.1723	1098920
0.0101	4.0	7696	0.2101	1465464
0.0605	5.0	9620	0.2285	1831920
0.0009	6.0	11544	0.3140	2198176
0.0003	7.0	13468	0.3418	2564952
0.0053	8.0	15392	0.3419	2931096
0.0002	9.0	17316	0.3458	3296808
0.0012	10.0	19240	0.3297	3663512
0.0	11.0	21164	0.4454	4029608
0.0	12.0	23088	0.6117	4395616
0.0	13.0	25012	0.5573	4762456
0.0	14.0	26936	0.4571	5128712
0.0	15.0	28860	0.5523	5495008
0.0139	16.0	30784	0.5230	5861104
0.0	17.0	32708	0.5678	6228320
0.0153	18.0	34632	0.5942	6595032
0.0	19.0	36556	0.5975	6961416
0.0127	20.0	38480	0.6042	7327648

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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