Instructions to use smcleish/clrs_gemma_2b_100k_finetune_with_traces with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use smcleish/clrs_gemma_2b_100k_finetune_with_traces with Transformers:

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

pipe = pipeline("text-generation", model="smcleish/clrs_gemma_2b_100k_finetune_with_traces")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("smcleish/clrs_gemma_2b_100k_finetune_with_traces")
model = AutoModelForMultimodalLM.from_pretrained("smcleish/clrs_gemma_2b_100k_finetune_with_traces")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use smcleish/clrs_gemma_2b_100k_finetune_with_traces with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "smcleish/clrs_gemma_2b_100k_finetune_with_traces"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "smcleish/clrs_gemma_2b_100k_finetune_with_traces",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/smcleish/clrs_gemma_2b_100k_finetune_with_traces

SGLang

How to use smcleish/clrs_gemma_2b_100k_finetune_with_traces 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 "smcleish/clrs_gemma_2b_100k_finetune_with_traces" \
    --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": "smcleish/clrs_gemma_2b_100k_finetune_with_traces",
		"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 "smcleish/clrs_gemma_2b_100k_finetune_with_traces" \
        --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": "smcleish/clrs_gemma_2b_100k_finetune_with_traces",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use smcleish/clrs_gemma_2b_100k_finetune_with_traces with Docker Model Runner:
```
docker model run hf.co/smcleish/clrs_gemma_2b_100k_finetune_with_traces
```

clrs_gemma_2b_100k_finetune_with_traces / model-00002-of-00002.safetensors

smcleish

Upload GemmaForCausalLM

8f8b271 verified over 1 year ago

Download

History Blame

67.1 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:: 649f0949784f8e4de1c48d612b3a763fd54205a344a982a21e86380ca89e18fa
Size of remote file:: 67.1 MB
SHA256:: 38ca11479f56635f572ffbc711ec4bd53cb239b739451b096c580f8cb41b28aa

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