Instructions to use LoneStriker/Etheria-55b-v0.1-4.65bpw-h6-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LoneStriker/Etheria-55b-v0.1-4.65bpw-h6-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="LoneStriker/Etheria-55b-v0.1-4.65bpw-h6-exl2")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LoneStriker/Etheria-55b-v0.1-4.65bpw-h6-exl2")
model = AutoModelForCausalLM.from_pretrained("LoneStriker/Etheria-55b-v0.1-4.65bpw-h6-exl2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LoneStriker/Etheria-55b-v0.1-4.65bpw-h6-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LoneStriker/Etheria-55b-v0.1-4.65bpw-h6-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LoneStriker/Etheria-55b-v0.1-4.65bpw-h6-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LoneStriker/Etheria-55b-v0.1-4.65bpw-h6-exl2

SGLang

How to use LoneStriker/Etheria-55b-v0.1-4.65bpw-h6-exl2 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 "LoneStriker/Etheria-55b-v0.1-4.65bpw-h6-exl2" \
    --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": "LoneStriker/Etheria-55b-v0.1-4.65bpw-h6-exl2",
		"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 "LoneStriker/Etheria-55b-v0.1-4.65bpw-h6-exl2" \
        --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": "LoneStriker/Etheria-55b-v0.1-4.65bpw-h6-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LoneStriker/Etheria-55b-v0.1-4.65bpw-h6-exl2 with Docker Model Runner:
```
docker model run hf.co/LoneStriker/Etheria-55b-v0.1-4.65bpw-h6-exl2
```

Steelskull/Etheria-55b-v0.1

Merge Details

An attempt to make a functional goliath style merge to create a [Etheria] 55b-200k with two yi-34b-200k models.

due to the merge it 'theoretically' should have a context of 200k but I recommend starting at 32k and moveing up, as it is unknown (at this time) what the merge has done to the context length.

This is a merge of both VerA and VerB of Etheria-55b (There numbers were surprisingly good), I then created a sacrificial 55B out of the most performant yi-34b-200k Model and performed a Dare_ties merge and equalize the model into its current state.

Merge Method

This model was merged using the DARE TIES merge method using Merged-Etheria-55b as a base.

Configuration

The following YAML configuration was used to produce this model:


base_model: Merged-Etheria-55b
models:
  - model: Sacr-Etheria-55b
    parameters:
      weight: [0.22, 0.113, 0.113, 0.113, 0.113, 0.113]
      density: 0.61
  - model: Merged-Etheria-55b
    parameters:
      weight: [0.22, 0.113, 0.113, 0.113, 0.113, 0.113]
      density: 0.61
merge_method: dare_ties
tokenizer_source: union
parameters:
  int8_mask: true
dtype: bfloat16

Downloads last month: 10

Papers for LoneStriker/Etheria-55b-v0.1-4.65bpw-h6-exl2

Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch

Paper • 2311.03099 • Published Nov 6, 2023 • 38

Resolving Interference When Merging Models

Paper • 2306.01708 • Published Jun 2, 2023 • 19