Instructions to use rbelanec/train_mrpc_789_1760637910 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rbelanec/train_mrpc_789_1760637910 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_mrpc_789_1760637910")

Transformers

How to use rbelanec/train_mrpc_789_1760637910 with Transformers:

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

pipe = pipeline("text-generation", model="rbelanec/train_mrpc_789_1760637910")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rbelanec/train_mrpc_789_1760637910", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rbelanec/train_mrpc_789_1760637910 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rbelanec/train_mrpc_789_1760637910"
# 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_mrpc_789_1760637910",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rbelanec/train_mrpc_789_1760637910

SGLang

How to use rbelanec/train_mrpc_789_1760637910 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_mrpc_789_1760637910" \
    --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_mrpc_789_1760637910",
		"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_mrpc_789_1760637910" \
        --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_mrpc_789_1760637910",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rbelanec/train_mrpc_789_1760637910 with Docker Model Runner:
```
docker model run hf.co/rbelanec/train_mrpc_789_1760637910
```

train_mrpc_789_1760637910

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

Loss: 0.1258
Num Input Tokens Seen: 6772448

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
0.2112	1.0	826	0.1850	338792
0.1743	2.0	1652	0.1608	677792
0.1157	3.0	2478	0.1466	1016968
0.2048	4.0	3304	0.1407	1355576
0.1566	5.0	4130	0.1440	1694240
0.2413	6.0	4956	0.1397	2032216
0.0604	7.0	5782	0.1317	2370752
0.1455	8.0	6608	0.1314	2708952
0.0816	9.0	7434	0.1272	3047656
0.0841	10.0	8260	0.1275	3386008
0.048	11.0	9086	0.1327	3724536
0.0668	12.0	9912	0.1269	4063536
0.0262	13.0	10738	0.1280	4402704
0.1085	14.0	11564	0.1281	4740960
0.0647	15.0	12390	0.1269	5078920
0.1221	16.0	13216	0.1280	5417832
0.0964	17.0	14042	0.1266	5755672
0.0603	18.0	14868	0.1284	6094000
0.0567	19.0	15694	0.1268	6433272
0.0751	20.0	16520	0.1258	6772448

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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