Text Generation
Transformers
Safetensors
mistral
Merge
Eval Results (legacy)
text-generation-inference
Instructions to use beberik/Lonepino-11B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("beberik/Lonepino-11B") model = AutoModelForMultimodalLM.from_pretrained("beberik/Lonepino-11B") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- 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
metadata
license: cc-by-nc-4.0
tags:
- merge
model-index:
- name: Lonepino-11B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 68.26
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=beberik/Lonepino-11B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 84.57
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=beberik/Lonepino-11B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 63.76
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=beberik/Lonepino-11B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 63.45
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=beberik/Lonepino-11B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 78.93
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=beberik/Lonepino-11B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 61.64
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=beberik/Lonepino-11B
name: Open LLM Leaderboard
Description
This repo contains bf16 files of Lonepino-11B. Just a normal model.
Model used
- Intel/neural-chat-7b-v3-3-Slerp
- NeverSleep/Noromaid-7b-v0.2
- chargoddard/loyal-piano-m7-cdpo
- maywell/PiVoT-0.1-Starling-LM-RP
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 |