Instructions to use Sefika/CRE_llama_fewrel_1_memory_10_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sefika/CRE_llama_fewrel_1_memory_10_4 with Transformers:

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

pipe = pipeline("text-generation", model="Sefika/CRE_llama_fewrel_1_memory_10_4")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Sefika/CRE_llama_fewrel_1_memory_10_4")
model = AutoModelForMultimodalLM.from_pretrained("Sefika/CRE_llama_fewrel_1_memory_10_4")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Sefika/CRE_llama_fewrel_1_memory_10_4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sefika/CRE_llama_fewrel_1_memory_10_4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sefika/CRE_llama_fewrel_1_memory_10_4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Sefika/CRE_llama_fewrel_1_memory_10_4

SGLang

How to use Sefika/CRE_llama_fewrel_1_memory_10_4 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 "Sefika/CRE_llama_fewrel_1_memory_10_4" \
    --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": "Sefika/CRE_llama_fewrel_1_memory_10_4",
		"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 "Sefika/CRE_llama_fewrel_1_memory_10_4" \
        --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": "Sefika/CRE_llama_fewrel_1_memory_10_4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Sefika/CRE_llama_fewrel_1_memory_10_4 with Docker Model Runner:
```
docker model run hf.co/Sefika/CRE_llama_fewrel_1_memory_10_4
```

Sefika commited on Aug 5, 2025

Commit

2a65864

verified ·

1 Parent(s): 693bed6

Update README.md

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@@ -3,6 +3,7 @@ library_name: transformers
 tags:
 - trl
 - sft
 ---
 # Model Card for Model ID
@@ -64,4 +65,4 @@ The paper "Large Language Models for Continual Relation Extraction" is submitted
 ## Model Card Contact
-sefika efeoglu

 tags:
 - trl
 - sft
+license: mit
 ---
 # Model Card for Model ID
 ## Model Card Contact
+sefika efeoglu