Instructions to use ChaoticNeutrals/Eris_7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ChaoticNeutrals/Eris_7B with Transformers:

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

pipe = pipeline("text-generation", model="ChaoticNeutrals/Eris_7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ChaoticNeutrals/Eris_7B")
model = AutoModelForCausalLM.from_pretrained("ChaoticNeutrals/Eris_7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ChaoticNeutrals/Eris_7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ChaoticNeutrals/Eris_7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ChaoticNeutrals/Eris_7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ChaoticNeutrals/Eris_7B

SGLang

How to use ChaoticNeutrals/Eris_7B 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 "ChaoticNeutrals/Eris_7B" \
    --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": "ChaoticNeutrals/Eris_7B",
		"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 "ChaoticNeutrals/Eris_7B" \
        --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": "ChaoticNeutrals/Eris_7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ChaoticNeutrals/Eris_7B with Docker Model Runner:
```
docker model run hf.co/ChaoticNeutrals/Eris_7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Jeitral: "Eris, the Greek goddess of chaos and discord."

Notes: Model should be excellent for both RP/Chat related tasks. Seems to be working in both Alpaca/Chatml.

Collaborative effort from both @Jeiku and @Nitral involving what we currently felt were our best individual projects.

We hope you enjoy! - The Chaotic Neutrals.

Imatrix GGUF Quants Thanks to @Lewdiculus: https://huggingface.co/Lewdiculous/Eris_7B-GGUF-IQ-Imatrix

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
      - model: Test157t/Prima-LelantaclesV6-7b
        layer_range: [0, 32]
      - model: ChaoticNeutrals/Prodigy_7B
        layer_range: [0, 32]
merge_method: slerp
base_model: Test157t/Prima-LelantaclesV6-7b
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	73.68
AI2 Reasoning Challenge (25-Shot)	71.42
HellaSwag (10-Shot)	87.99
MMLU (5-Shot)	65.24
TruthfulQA (0-shot)	66.95
Winogrande (5-shot)	84.21
GSM8k (5-shot)	66.26

Downloads last month: 79

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for ChaoticNeutrals/Eris_7B

ChaoticNeutrals/Prodigy_7B

Nitral-Archive/Prima-LelantaclesV6-7b

Merge model

this model

Finetunes

1 model

Merges

2 models

Quantizations

2 models

Evaluation results

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

71.420
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

87.990
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

65.240
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

66.950
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

84.210
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

66.260