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

Libraries

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

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

pipe = pipeline("text-generation", model="beberik/Lonepino-11B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("beberik/Lonepino-11B")
model = AutoModelForCausalLM.from_pretrained("beberik/Lonepino-11B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "beberik/Lonepino-11B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "beberik/Lonepino-11B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

How to use beberik/Lonepino-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/Lonepino-11B" \
    --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": "beberik/Lonepino-11B",
		"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 "beberik/Lonepino-11B" \
        --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": "beberik/Lonepino-11B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

Description

This repo contains bf16 files of Lonepino-11B. Just a normal model.

Model used

The secret sauce

neural-maid-11B:

slices:
  - sources:
    - model: Intel/neural-chat-7b-v3-3-Slerp
      layer_range: [0, 24]
  - sources:
    - model: NeverSleep/Noromaid-7b-v0.2
      layer_range: [8, 32]

merge_method: passthrough
dtype: bfloat16

loyal-PiVoT-11B:

slices:
  - sources:
    - model: chargoddard/loyal-piano-m7-cdpo
      layer_range: [0, 24]
  - sources:
    - model: maywell/PiVoT-0.1-Starling-LM-RP
      layer_range: [8, 32]

merge_method: passthrough
dtype: bfloat16

Lonepino-11B:

slices:
  - sources:
      - model: "./neural-maid-11B"
        layer_range: [0, 48]
      - model: "./loyal-PiVoT-11B"
        layer_range: [0, 48]
merge_method: slerp
base_model: "./neural-maid-11B"
parameters:
  t:
    - value: 0.4
dtype: bfloat16

Prompt template

Alpaca. Or chatml. Or any you like.

=w=

I use mergekit for all the manipulation told here.

Thanks to the Undi95 for the original 11B mistral merge recipe.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	70.10
AI2 Reasoning Challenge (25-Shot)	68.26
HellaSwag (10-Shot)	84.57
MMLU (5-Shot)	63.76
TruthfulQA (0-shot)	63.45
Winogrande (5-shot)	78.93
GSM8k (5-shot)	61.64

Downloads last month: 205

Safetensors

Model size

11B params

Tensor type

BF16

Model tree for beberik/Lonepino-11B

Quantizations

3 models

Evaluation results

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

68.260
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

84.570
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

63.760
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

63.450
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

78.930
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

61.640