Instructions to use Kronu/gemma-2-2b-lean-expert-optimized-cache-enabled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kronu/gemma-2-2b-lean-expert-optimized-cache-enabled with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("google/gemma-2-2b")
model = PeftModel.from_pretrained(base_model, "Kronu/gemma-2-2b-lean-expert-optimized-cache-enabled")

Transformers

How to use Kronu/gemma-2-2b-lean-expert-optimized-cache-enabled with Transformers:

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

pipe = pipeline("text-generation", model="Kronu/gemma-2-2b-lean-expert-optimized-cache-enabled")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Kronu/gemma-2-2b-lean-expert-optimized-cache-enabled", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Kronu/gemma-2-2b-lean-expert-optimized-cache-enabled with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kronu/gemma-2-2b-lean-expert-optimized-cache-enabled"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kronu/gemma-2-2b-lean-expert-optimized-cache-enabled",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Kronu/gemma-2-2b-lean-expert-optimized-cache-enabled

SGLang

How to use Kronu/gemma-2-2b-lean-expert-optimized-cache-enabled 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 "Kronu/gemma-2-2b-lean-expert-optimized-cache-enabled" \
    --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": "Kronu/gemma-2-2b-lean-expert-optimized-cache-enabled",
		"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 "Kronu/gemma-2-2b-lean-expert-optimized-cache-enabled" \
        --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": "Kronu/gemma-2-2b-lean-expert-optimized-cache-enabled",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Kronu/gemma-2-2b-lean-expert-optimized-cache-enabled with Docker Model Runner:
```
docker model run hf.co/Kronu/gemma-2-2b-lean-expert-optimized-cache-enabled
```

gemma-2-2b-lean-expert-optimized-cache-enabled / tokenizer.json

Kronu

Training in progress, step 150

2efe37b verified 9 months ago

download

history blame contribute delete

34.4 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 5adb7b58b5d1fee98a5b293d33133a5e7932b34f4d1ddb5a636792d04495847e
Size of remote file:: 34.4 MB
SHA256:: abd905f70a0604065e2115e2e99a985ae7f6c42a528cb9e0ac42c1178c6fc151

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.