Instructions to use SteelStorage/VerA-Etheria-55b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SteelStorage/VerA-Etheria-55b with Transformers:

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

pipe = pipeline("text-generation", model="SteelStorage/VerA-Etheria-55b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SteelStorage/VerA-Etheria-55b")
model = AutoModelForCausalLM.from_pretrained("SteelStorage/VerA-Etheria-55b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SteelStorage/VerA-Etheria-55b with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/SteelStorage/VerA-Etheria-55b

SGLang

How to use SteelStorage/VerA-Etheria-55b 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 "SteelStorage/VerA-Etheria-55b" \
    --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": "SteelStorage/VerA-Etheria-55b",
		"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 "SteelStorage/VerA-Etheria-55b" \
        --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": "SteelStorage/VerA-Etheria-55b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SteelStorage/VerA-Etheria-55b with Docker Model Runner:
```
docker model run hf.co/SteelStorage/VerA-Etheria-55b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

VerA-Etheria-55b

An attempt to make a functional goliath style merge with One yi-34b-200k model Merged to make a [Etheria] 55b-200k Model, this is Version A or VerA, it is a single Model Passthrough merge.

Roadmap:

Depending on quality, I Might private the other Version. Then generate a sacrificial 55b and perform a 55b Dare ties merge or Slerp merge.

1: If the Dual Model Merge performs well I will make a direct inverse of the config then merge.

2: If the single model performs well I will generate a 55b of the most performant model then either Slerp or Dare ties merge.

3: If both models perform well, then I will complete both 1 & 2 then change the naming scheme to match each of the new models.

🧩 Configuration

dtype: bfloat16
slices:
- sources:
    - model: brucethemoose/Yi-34B-200K-DARE-megamerge-v8
      layer_range: [0, 14]
- sources:
    - model: brucethemoose/Yi-34B-200K-DARE-megamerge-v8
      layer_range: [7, 21]
- sources:
    - model: brucethemoose/Yi-34B-200K-DARE-megamerge-v8
      layer_range: [15, 29]
- sources:
    - model: brucethemoose/Yi-34B-200K-DARE-megamerge-v8
      layer_range: [22, 36]
- sources:
    - model: brucethemoose/Yi-34B-200K-DARE-megamerge-v8
      layer_range: [30, 44]
- sources:
    - model: brucethemoose/Yi-34B-200K-DARE-megamerge-v8
      layer_range: [37, 51]
- sources:
    - model: brucethemoose/Yi-34B-200K-DARE-megamerge-v8
      layer_range: [45, 59]
merge_method: passthrough

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "steelskull/VA-Etheria-55b"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

Downloads last month: 8

Safetensors

Model size

56B params

Tensor type

BF16

Model tree for SteelStorage/VerA-Etheria-55b

Base model

brucethemoose/Yi-34B-200K-DARE-megamerge-v8

Finetuned

(1)

this model

Quantizations

1 model