Instructions to use rbelanec/train_copa_789_1768397603 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rbelanec/train_copa_789_1768397603 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_copa_789_1768397603")

Transformers

How to use rbelanec/train_copa_789_1768397603 with Transformers:

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

pipe = pipeline("text-generation", model="rbelanec/train_copa_789_1768397603")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rbelanec/train_copa_789_1768397603", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rbelanec/train_copa_789_1768397603 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rbelanec/train_copa_789_1768397603"
# 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_copa_789_1768397603",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rbelanec/train_copa_789_1768397603

SGLang

How to use rbelanec/train_copa_789_1768397603 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_copa_789_1768397603" \
    --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_copa_789_1768397603",
		"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_copa_789_1768397603" \
        --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_copa_789_1768397603",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rbelanec/train_copa_789_1768397603 with Docker Model Runner:
```
docker model run hf.co/rbelanec/train_copa_789_1768397603
```

train_copa_789_1768397603

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

Loss: 0.0839
Num Input Tokens Seen: 274208

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: 2
eval_batch_size: 2
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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Input Tokens Seen
0.0039	0.5	90	0.1701	13792
0.5837	1.0	180	0.1115	27424
0.0054	1.5	270	0.0873	41056
0.0005	2.0	360	0.0839	54832
0.0027	2.5	450	0.0871	68416
0.3341	3.0	540	0.0933	82160
0.0109	3.5	630	0.1026	95856
0.0009	4.0	720	0.0998	109632
0.0012	4.5	810	0.1032	123360
0.03	5.0	900	0.1080	137120
0.0015	5.5	990	0.1099	150880
0.0001	6.0	1080	0.1141	164592
0.0004	6.5	1170	0.1137	178240
0.0002	7.0	1260	0.1140	191920
0.0002	7.5	1350	0.1174	205632
0.0002	8.0	1440	0.1175	219344
0.0001	8.5	1530	0.1246	232976
0.0002	9.0	1620	0.1214	246736
0.0001	9.5	1710	0.1240	260384
0.0002	10.0	1800	0.1224	274208

Framework versions

PEFT 0.17.1
Transformers 4.51.3
Pytorch 2.9.1+cu128
Datasets 4.0.0
Tokenizers 0.21.4

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