Instructions to use rbelanec/train_cola_789_1760637930 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rbelanec/train_cola_789_1760637930 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_1760637930")

Transformers

How to use rbelanec/train_cola_789_1760637930 with Transformers:

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

pipe = pipeline("text-generation", model="rbelanec/train_cola_789_1760637930")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rbelanec/train_cola_789_1760637930", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rbelanec/train_cola_789_1760637930 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_1760637930"
# 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_1760637930",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rbelanec/train_cola_789_1760637930

SGLang

How to use rbelanec/train_cola_789_1760637930 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_1760637930" \
    --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_1760637930",
		"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_1760637930" \
        --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_1760637930",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rbelanec/train_cola_789_1760637930 with Docker Model Runner:
```
docker model run hf.co/rbelanec/train_cola_789_1760637930
```

train_cola_789_1760637930

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: 0.2552
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: 0.03
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
0.2775	1.0	1924	0.2689	365728
0.3636	2.0	3848	0.2573	731984
0.3798	3.0	5772	0.2567	1098920
0.1577	4.0	7696	0.2727	1465464
0.2357	5.0	9620	0.2564	1831920
0.2165	6.0	11544	0.2563	2198176
0.2541	7.0	13468	0.2560	2564952
0.3109	8.0	15392	0.2568	2931096
0.2238	9.0	17316	0.2581	3296808
0.2143	10.0	19240	0.2566	3663512
0.2707	11.0	21164	0.2574	4029608
0.2783	12.0	23088	0.2564	4395616
0.2444	13.0	25012	0.2564	4762456
0.222	14.0	26936	0.2553	5128712
0.258	15.0	28860	0.2558	5495008
0.2634	16.0	30784	0.2564	5861104
0.2836	17.0	32708	0.2553	6228320
0.2879	18.0	34632	0.2557	6595032
0.2208	19.0	36556	0.2555	6961416
0.2898	20.0	38480	0.2552	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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