Instructions to use rombodawg/EveryoneLLM-7b-Gemma-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rombodawg/EveryoneLLM-7b-Gemma-Base with Transformers:

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

pipe = pipeline("text-generation", model="rombodawg/EveryoneLLM-7b-Gemma-Base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rombodawg/EveryoneLLM-7b-Gemma-Base")
model = AutoModelForCausalLM.from_pretrained("rombodawg/EveryoneLLM-7b-Gemma-Base")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rombodawg/EveryoneLLM-7b-Gemma-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rombodawg/EveryoneLLM-7b-Gemma-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/EveryoneLLM-7b-Gemma-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/rombodawg/EveryoneLLM-7b-Gemma-Base

SGLang

How to use rombodawg/EveryoneLLM-7b-Gemma-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/EveryoneLLM-7b-Gemma-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/EveryoneLLM-7b-Gemma-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/EveryoneLLM-7b-Gemma-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/EveryoneLLM-7b-Gemma-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use rombodawg/EveryoneLLM-7b-Gemma-Base with Docker Model Runner:
```
docker model run hf.co/rombodawg/EveryoneLLM-7b-Gemma-Base
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

EveryoneLLM-7b-Gemma-Base

Quantizations:

GGUF

https://huggingface.co/rombodawg/EveryoneLLM-7b-Gemma-Base-GGUF

EveryoneLLM series of models made by the community, for the community.

This is the second version of Everyone-LLM using Gemma-7b, a model that combines the power of the large majority of powerfull fine-tuned LLM's made by the community, to create a vast and knowledgable LLM with various abilities with an extra emphasis on coding capabilities.

Prompt template: Alpaca

Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:

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. 💗

This model was merges in 2 parts. The order of parts is listed bellow, then a copy and pastable version is bellow that.

models:
  - model: VAGOsolutions_SauerkrautLM-Gemma-7b
    parameters:
      weight: 1
  - model: macadeliccc_gemma-orchid-7b-dpo
    parameters:
      weight: 1
  - model: HuggingFaceH4_zephyr-7b-gemma-v0.1
    parameters:
      weight: 1
  - model: CorticalStack_gemma-7b-ultrachat-sft
    parameters:
      weight: 1
merge_method: task_arithmetic
base_model: gemma-7b-base
parameters:
  normalize: true
  int8_mask: true
dtype: float16

models:
  - model: Gemma-Merge-1-7b
    parameters:
      weight: 1
  - model: openchat_openchat-3.5-0106-gemma
    parameters:
      weight: 1
  - model: TechxGenus_CodeGemma-7b
    parameters:
      weight: 1
merge_method: task_arithmetic
base_model: gemma-7b-base
parameters:
  normalize: true
  int8_mask: true
dtype: float16

Downloads last month: 64

Safetensors

Model size

9B params

Tensor type

F16

Model tree for rombodawg/EveryoneLLM-7b-Gemma-Base

Quantizations

2 models