Instructions to use rbelanec/train_conala_456_1760637782 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rbelanec/train_conala_456_1760637782 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_conala_456_1760637782")

Transformers

How to use rbelanec/train_conala_456_1760637782 with Transformers:

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

pipe = pipeline("text-generation", model="rbelanec/train_conala_456_1760637782")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rbelanec/train_conala_456_1760637782", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rbelanec/train_conala_456_1760637782 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rbelanec/train_conala_456_1760637782"
# 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_conala_456_1760637782",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rbelanec/train_conala_456_1760637782

SGLang

How to use rbelanec/train_conala_456_1760637782 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_conala_456_1760637782" \
    --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_conala_456_1760637782",
		"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_conala_456_1760637782" \
        --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_conala_456_1760637782",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rbelanec/train_conala_456_1760637782 with Docker Model Runner:
```
docker model run hf.co/rbelanec/train_conala_456_1760637782
```

train_conala_456_1760637782

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

Loss: 2.8011
Num Input Tokens Seen: 3043720

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: 456
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
3.254	1.0	536	2.8281	152088
2.3055	2.0	1072	2.8202	303784
2.9109	3.0	1608	2.8084	456552
2.5029	4.0	2144	2.8037	608704
2.9398	5.0	2680	2.8035	761184
3.0148	6.0	3216	2.8027	912912
2.4542	7.0	3752	2.8017	1065128
2.7746	8.0	4288	2.8030	1216496
3.486	9.0	4824	2.8020	1368880
3.0883	10.0	5360	2.8014	1522016
3.094	11.0	5896	2.8037	1674136
3.2793	12.0	6432	2.8016	1826160
2.5697	13.0	6968	2.8040	1978984
2.3809	14.0	7504	2.8015	2130656
2.8492	15.0	8040	2.8026	2282720
2.8076	16.0	8576	2.8011	2434896
2.8856	17.0	9112	2.8029	2586968
3.1466	18.0	9648	2.8037	2738448
2.5162	19.0	10184	2.8032	2891056
2.3038	20.0	10720	2.8032	3043720

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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