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

Libraries

How to use kmeanskaran/IPC-gemma with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kmeanskaran/IPC-gemma")
model = AutoModelForCausalLM.from_pretrained("kmeanskaran/IPC-gemma", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kmeanskaran/IPC-gemma with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/kmeanskaran/IPC-gemma

SGLang

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

Docker Model Runner
How to use kmeanskaran/IPC-gemma with Docker Model Runner:
```
docker model run hf.co/kmeanskaran/IPC-gemma
```

IPC-gemma

Commit History

Update README.md

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Karan Shingde commited on Jun 26, 2024

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Upload GemmaForCausalLM

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Karan Shingde commited on Jun 26, 2024

initial commit

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Karan Shingde commited on Jun 26, 2024

Commit History

Update README.md baa881d verified

Upload tokenizer 6c7f0fe verified

Upload GemmaForCausalLM 98fde14 verified

initial commit 883fc9a verified

Update README.md

baa881d
verified

Upload tokenizer

6c7f0fe
verified

Upload GemmaForCausalLM

98fde14
verified

initial commit

883fc9a
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