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

Libraries

How to use iot/Gemma_model_fine_tune_custom_Data with Transformers:

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

pipe = pipeline("text-generation", model="iot/Gemma_model_fine_tune_custom_Data")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("iot/Gemma_model_fine_tune_custom_Data")
model = AutoModelForCausalLM.from_pretrained("iot/Gemma_model_fine_tune_custom_Data", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use iot/Gemma_model_fine_tune_custom_Data with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "iot/Gemma_model_fine_tune_custom_Data"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "iot/Gemma_model_fine_tune_custom_Data",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/iot/Gemma_model_fine_tune_custom_Data

SGLang

How to use iot/Gemma_model_fine_tune_custom_Data 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 "iot/Gemma_model_fine_tune_custom_Data" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "iot/Gemma_model_fine_tune_custom_Data",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "iot/Gemma_model_fine_tune_custom_Data" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "iot/Gemma_model_fine_tune_custom_Data",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

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Check out the documentation for more information.

Gemma Model Fine-Tuned on Custom Data

Model Description

This model is a fine-tuned version of Gemma Model on custom data. It was trained using the SFTTrainer and incorporates LoRA configurations to enhance performance.

Training Procedure

Batch size: 1
Gradient accumulation steps: 4
Learning rate: 2e-4
Warmup steps: 2
Max steps: 100
Optimizer: Paged AdamW 8-bit
FP16: Enabled

Usage

You can use this model, Below is an example of how to load and use the model:

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("iot/Gemma_model_fine_tune_custom_Data")
model = AutoModelForCausalLM.from_pretrained("iot/Gemma_model_fine_tune_custom_Data")

input_text = "Your input text here"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloads last month: 5

Safetensors

Model size

3B params

Tensor type

F32