Instructions to use rbelanec/train_cb_789_1760637868 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rbelanec/train_cb_789_1760637868 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_cb_789_1760637868")

Transformers

How to use rbelanec/train_cb_789_1760637868 with Transformers:

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

pipe = pipeline("text-generation", model="rbelanec/train_cb_789_1760637868")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rbelanec/train_cb_789_1760637868", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rbelanec/train_cb_789_1760637868 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rbelanec/train_cb_789_1760637868"
# 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_cb_789_1760637868",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rbelanec/train_cb_789_1760637868

SGLang

How to use rbelanec/train_cb_789_1760637868 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_cb_789_1760637868" \
    --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_cb_789_1760637868",
		"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_cb_789_1760637868" \
        --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_cb_789_1760637868",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rbelanec/train_cb_789_1760637868 with Docker Model Runner:
```
docker model run hf.co/rbelanec/train_cb_789_1760637868
```

train_cb_789_1760637868

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

Loss: 0.0390
Num Input Tokens Seen: 711112

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: 0.001
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
0.4751	1.0	57	1.2149	35448
0.3845	2.0	114	1.1475	70496
0.2278	3.0	171	0.1985	106416
0.2558	4.0	228	0.0931	142480
0.1601	5.0	285	0.1481	177224
0.0544	6.0	342	0.0972	212000
0.0212	7.0	399	0.0559	248272
0.0503	8.0	456	0.0554	284248
0.0561	9.0	513	0.0994	319488
0.0031	10.0	570	0.0718	354472
0.0013	11.0	627	0.1540	389408
0.0013	12.0	684	0.1397	425328
0.0005	13.0	741	0.1118	461216
0.0003	14.0	798	0.1103	496704
0.0002	15.0	855	0.1187	532504
0.0003	16.0	912	0.1145	567952
0.0002	17.0	969	0.1075	603760
0.0004	18.0	1026	0.1133	639784
0.0002	19.0	1083	0.1141	675800
0.0002	20.0	1140	0.1113	711112

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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