Instructions to use beberik/Nyxene-11B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use beberik/Nyxene-11B with Transformers:

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

pipe = pipeline("text-generation", model="beberik/Nyxene-11B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("beberik/Nyxene-11B")
model = AutoModelForMultimodalLM.from_pretrained("beberik/Nyxene-11B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use beberik/Nyxene-11B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "beberik/Nyxene-11B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "beberik/Nyxene-11B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/beberik/Nyxene-11B

SGLang

How to use beberik/Nyxene-11B 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 "beberik/Nyxene-11B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "beberik/Nyxene-11B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "beberik/Nyxene-11B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "beberik/Nyxene-11B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use beberik/Nyxene-11B with Docker Model Runner:
```
docker model run hf.co/beberik/Nyxene-11B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Description

This repo contains bf16 files of Nyxene-11B. Like OmniMix but with new models.

Model used

Prompt template

The best one after further testing is this one:

<|system|>
Below is an instruction that describes a task. Write a response that appropriately completes the request.
<|user|>
{prompt}
<|assistant|>

The secret sauce

dolphin-juanako-11B :

slices:
  - sources:
    - model: fblgit/juanako-7b-UNA
      layer_range: [0, 24]
  - sources:
    - model: ehartford/dolphin-2.1-mistral-7b
      layer_range: [8, 32]
merge_method: passthrough
dtype: bfloat16

Starling-NeuralHermes-11B :

slices:
  - sources:
    - model: berkeley-nest/Starling-LM-7B-alpha
      layer_range: [0, 24]
  - sources:
    - model: mlabonne/NeuralHermes-2.5-Mistral-7B
      layer_range: [8, 32]
merge_method: passthrough
dtype: bfloat16

Nyxene-11B :

slices:
  - sources:
      - model: dolphin-juanako-11B
        layer_range: [0, 48]
      - model: Starling-NeuralHermes-11B
        layer_range: [0, 48]
merge_method: slerp
base_model: dolphin-juanako-11B
parameters:
  t:
    - filter: lm_head 
      value: [0.75]
    - filter: embed_tokens
      value: [0.75]
    - filter: self_attn
      value: [0.75, 0.25]
    - filter: mlp
      value:  [0.25, 0.75]
    - filter: layernorm
      value: [0.5, 0.5]
    - filter: modelnorm
      value: [0.75]
    - value: 0.5 # fallback for rest of tensors
dtype: bfloat16

I use mergekit for all the manipulation told here.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	67.72
AI2 Reasoning Challenge (25-Shot)	68.34
HellaSwag (10-Shot)	84.54
MMLU (5-Shot)	65.09
TruthfulQA (0-shot)	57.50
Winogrande (5-shot)	79.08
GSM8k (5-shot)	51.78

Downloads last month: 113

Safetensors

Model size

11B params

Tensor type

BF16

Model tree for beberik/Nyxene-11B

Quantizations

3 models

Evaluation results

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

68.340
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

84.540
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

65.090
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

57.500
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

79.080
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

51.780