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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SteelStorage/VerB-Etheria-55b")
model = AutoModelForCausalLM.from_pretrained("SteelStorage/VerB-Etheria-55b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SteelStorage/VerB-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/VerB-Etheria-55b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

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

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

VerB-Etheria-55b

An attempt to make a functional goliath style merge to create a [Etheria] 55b-200k with two yi-34b-200k models, this is Version B or VerB, it is a Double Model Passthrough merge. with a 50/50 split between high performing models.

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 the 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

The following YAML configuration was used to produce this model:


dtype: bfloat16
slices:
- sources:
    - model: brucethemoose/Yi-34B-200K-DARE-megamerge-v8
      layer_range: [0, 14]
- sources:
    - model: one-man-army/UNA-34Beagles-32K-bf16-v1
      layer_range: [7, 21]
- sources:
    - model: brucethemoose/Yi-34B-200K-DARE-megamerge-v8
      layer_range: [15, 29]
- sources:
    - model: one-man-army/UNA-34Beagles-32K-bf16-v1
      layer_range: [22, 36]
- sources:
    - model: brucethemoose/Yi-34B-200K-DARE-megamerge-v8
      layer_range: [30, 44]
- sources:
    - model: one-man-army/UNA-34Beagles-32K-bf16-v1
      layer_range: [37, 51]
- sources:
    - model: brucethemoose/Yi-34B-200K-DARE-megamerge-v8
      layer_range: [45, 59]
merge_method: passthrough

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	63.83
AI2 Reasoning Challenge (25-Shot)	65.96
HellaSwag (10-Shot)	81.48
MMLU (5-Shot)	73.78
TruthfulQA (0-shot)	57.52
Winogrande (5-shot)	75.45
GSM8k (5-shot)	28.81

Downloads last month: 39

Safetensors

Model size

56B params

Tensor type

BF16

Model tree for SteelStorage/VerB-Etheria-55b

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

one-man-army/UNA-34Beagles-32K-bf16-v1

Merge model

this model

Quantizations

1 model

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

65.960
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

81.480
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

73.780
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

57.520
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

75.450
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

28.810