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

Libraries

How to use thedeba/gemma_bn_instruct with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("INSAIT-Institute/BgGPT-Gemma-2-2.6B-IT-v1.0")
model = PeftModel.from_pretrained(base_model, "thedeba/gemma_bn_instruct")

Transformers

How to use thedeba/gemma_bn_instruct with Transformers:

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

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

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use thedeba/gemma_bn_instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thedeba/gemma_bn_instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thedeba/gemma_bn_instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/thedeba/gemma_bn_instruct

SGLang

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

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

gemma_bn_instruct

File size: 2,117 Bytes

b3ab80c

---
library_name: peft
license: gemma
base_model: INSAIT-Institute/BgGPT-Gemma-2-2.6B-IT-v1.0
tags:
- base_model:adapter:INSAIT-Institute/BgGPT-Gemma-2-2.6B-IT-v1.0
- lora
- transformers
pipeline_tag: text-generation
model-index:
- name: gemma_bn_instruct
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# gemma_bn_instruct

This model is a fine-tuned version of [INSAIT-Institute/BgGPT-Gemma-2-2.6B-IT-v1.0](https://huggingface.co/INSAIT-Institute/BgGPT-Gemma-2-2.6B-IT-v1.0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7552

## 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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.0629        | 0.2844 | 200  | 1.9151          |
| 1.8976        | 0.5689 | 400  | 1.8619          |
| 1.8624        | 0.8533 | 600  | 1.8252          |
| 1.787         | 1.1365 | 800  | 1.8043          |
| 1.7492        | 1.4210 | 1000 | 1.7880          |
| 1.7227        | 1.7054 | 1200 | 1.7763          |
| 1.7327        | 1.9899 | 1400 | 1.7640          |
| 1.6352        | 2.2731 | 1600 | 1.7663          |
| 1.6366        | 2.5575 | 1800 | 1.7605          |
| 1.6381        | 2.8420 | 2000 | 1.7552          |


### Framework versions

- PEFT 0.17.0
- Transformers 4.51.3
- Pytorch 2.7.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.1