Instructions to use rbelanec/train_conala_123_1760637665 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rbelanec/train_conala_123_1760637665 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_123_1760637665")

Transformers

How to use rbelanec/train_conala_123_1760637665 with Transformers:

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

pipe = pipeline("text-generation", model="rbelanec/train_conala_123_1760637665")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rbelanec/train_conala_123_1760637665", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rbelanec/train_conala_123_1760637665 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rbelanec/train_conala_123_1760637665"
# 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_123_1760637665",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rbelanec/train_conala_123_1760637665

SGLang

How to use rbelanec/train_conala_123_1760637665 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_123_1760637665" \
    --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_123_1760637665",
		"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_123_1760637665" \
        --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_123_1760637665",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rbelanec/train_conala_123_1760637665 with Docker Model Runner:
```
docker model run hf.co/rbelanec/train_conala_123_1760637665
```

train_conala_123_1760637665

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: 1.0658
Num Input Tokens Seen: 3047552

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: 123
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
0.8894	1.0	536	0.6439	152672
0.6937	2.0	1072	0.6180	305288
0.4875	3.0	1608	0.5986	457952
0.4151	4.0	2144	0.6029	610944
0.8716	5.0	2680	0.6069	762440
0.5753	6.0	3216	0.6013	914920
0.4714	7.0	3752	0.6204	1067520
0.549	8.0	4288	0.6045	1220200
0.6915	9.0	4824	0.6048	1372560
0.3567	10.0	5360	0.6144	1524216
0.5933	11.0	5896	0.6268	1675880
0.5259	12.0	6432	0.6402	1828344
0.489	13.0	6968	0.6632	1980376
0.2976	14.0	7504	0.6913	2132544
0.3224	15.0	8040	0.7080	2284440
0.2692	16.0	8576	0.7352	2436520
0.2695	17.0	9112	0.7561	2589096
0.3296	18.0	9648	0.7822	2741936
0.2217	19.0	10184	0.7954	2894976
0.2721	20.0	10720	0.8006	3047552

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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