legacy-datasets/c4
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How to use crumb/core1-base-464m-c4 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="crumb/core1-base-464m-c4") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("crumb/core1-base-464m-c4")
model = AutoModelForCausalLM.from_pretrained("crumb/core1-base-464m-c4")How to use crumb/core1-base-464m-c4 with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "crumb/core1-base-464m-c4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "crumb/core1-base-464m-c4",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/crumb/core1-base-464m-c4
How to use crumb/core1-base-464m-c4 with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "crumb/core1-base-464m-c4" \
--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": "crumb/core1-base-464m-c4",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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 "crumb/core1-base-464m-c4" \
--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": "crumb/core1-base-464m-c4",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use crumb/core1-base-464m-c4 with Docker Model Runner:
docker model run hf.co/crumb/core1-base-464m-c4
CoreX models are Llama models in which the first X decoder layers are kept, and then the model is finetuned on 1 billion tokens from some dataset. Base model stems from Llama2-7b, medium from Llama2-13b, xl from Llama2-70b.