Instructions to use chaithanya277/gemma-roadmap-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chaithanya277/gemma-roadmap-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("google/gemma-3-270m")
model = PeftModel.from_pretrained(base_model, "chaithanya277/gemma-roadmap-lora")

Transformers

How to use chaithanya277/gemma-roadmap-lora with Transformers:

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

pipe = pipeline("text-generation", model="chaithanya277/gemma-roadmap-lora")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("chaithanya277/gemma-roadmap-lora", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use chaithanya277/gemma-roadmap-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chaithanya277/gemma-roadmap-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chaithanya277/gemma-roadmap-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/chaithanya277/gemma-roadmap-lora

SGLang

How to use chaithanya277/gemma-roadmap-lora 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 "chaithanya277/gemma-roadmap-lora" \
    --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": "chaithanya277/gemma-roadmap-lora",
		"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 "chaithanya277/gemma-roadmap-lora" \
        --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": "chaithanya277/gemma-roadmap-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use chaithanya277/gemma-roadmap-lora with Docker Model Runner:
```
docker model run hf.co/chaithanya277/gemma-roadmap-lora
```

chaithanya277 commited on Sep 21, 2025

Commit

359239d

verified ·

1 Parent(s): 61a1858

End of training

Browse files

Files changed (1) hide show

README.md +0 -1

README.md CHANGED Viewed

@@ -45,7 +45,6 @@ The following hyperparameters were used during training:
 - optimizer: Use OptimizerNames.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

 - optimizer: Use OptimizerNames.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
 ### Training results