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

Libraries

How to use logicBombExe/gemma_identity_fine_tune_for_umaysamli with Transformers:

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

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

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("logicBombExe/gemma_identity_fine_tune_for_umaysamli", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use logicBombExe/gemma_identity_fine_tune_for_umaysamli with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/logicBombExe/gemma_identity_fine_tune_for_umaysamli

SGLang

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

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

gemma_identity_fine_tune_for_umaysamli

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Commit History

Update README.md e074f65 verified

Update README.md 8a185bb verified

Upload model trained with Unsloth 297a043 verified

Upload model trained with Unsloth 8e7f461 verified

Upload model trained with Unsloth ea13b24 verified

initial commit 9af13cf verified

Update README.md

e074f65
verified

Update README.md

8a185bb
verified

Upload model trained with Unsloth

297a043
verified

Upload model trained with Unsloth

8e7f461
verified

Upload model trained with Unsloth

ea13b24
verified

initial commit

9af13cf
verified