Instructions to use rombodawg/Everyone-Coder-4x7b-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rombodawg/Everyone-Coder-4x7b-Base with Transformers:

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

pipe = pipeline("text-generation", model="rombodawg/Everyone-Coder-4x7b-Base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rombodawg/Everyone-Coder-4x7b-Base")
model = AutoModelForCausalLM.from_pretrained("rombodawg/Everyone-Coder-4x7b-Base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rombodawg/Everyone-Coder-4x7b-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rombodawg/Everyone-Coder-4x7b-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rombodawg/Everyone-Coder-4x7b-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/rombodawg/Everyone-Coder-4x7b-Base

SGLang

How to use rombodawg/Everyone-Coder-4x7b-Base 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 "rombodawg/Everyone-Coder-4x7b-Base" \
    --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": "rombodawg/Everyone-Coder-4x7b-Base",
		"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 "rombodawg/Everyone-Coder-4x7b-Base" \
        --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": "rombodawg/Everyone-Coder-4x7b-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use rombodawg/Everyone-Coder-4x7b-Base with Docker Model Runner:
```
docker model run hf.co/rombodawg/Everyone-Coder-4x7b-Base
```

rombodawg commited on Jan 14, 2024

Commit

df11f29

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@@ -26,6 +26,10 @@ The models that were used in this merger were as follow:
 Thank you to the creators of the above ai models, they have full credit for the EveryoneLLM series of models. Without their hard work we wouldnt be able to achieve the great success we have in the open source community. 💗
 Config for the merger can be found bellow:
 ```

 Thank you to the creators of the above ai models, they have full credit for the EveryoneLLM series of models. Without their hard work we wouldnt be able to achieve the great success we have in the open source community. 💗
+You can find the write up for this model here:
+https://docs.google.com/document/d/1_vOftBnrk9NRk5h10UqrfJ5CDih9KBKL61yvrZtVWPE/edit?usp=sharing
 Config for the merger can be found bellow:
 ```