Instructions to use DeepAuto-AI/ldm_soup_Llama-3.1-8B-Inst with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeepAuto-AI/ldm_soup_Llama-3.1-8B-Inst with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="DeepAuto-AI/ldm_soup_Llama-3.1-8B-Inst") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("DeepAuto-AI/ldm_soup_Llama-3.1-8B-Inst") model = AutoModelForCausalLM.from_pretrained("DeepAuto-AI/ldm_soup_Llama-3.1-8B-Inst", device_map="auto") 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]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use DeepAuto-AI/ldm_soup_Llama-3.1-8B-Inst with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DeepAuto-AI/ldm_soup_Llama-3.1-8B-Inst" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DeepAuto-AI/ldm_soup_Llama-3.1-8B-Inst", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/DeepAuto-AI/ldm_soup_Llama-3.1-8B-Inst
- SGLang
How to use DeepAuto-AI/ldm_soup_Llama-3.1-8B-Inst 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 "DeepAuto-AI/ldm_soup_Llama-3.1-8B-Inst" \ --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": "DeepAuto-AI/ldm_soup_Llama-3.1-8B-Inst", "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 "DeepAuto-AI/ldm_soup_Llama-3.1-8B-Inst" \ --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": "DeepAuto-AI/ldm_soup_Llama-3.1-8B-Inst", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use DeepAuto-AI/ldm_soup_Llama-3.1-8B-Inst with Docker Model Runner:
docker model run hf.co/DeepAuto-AI/ldm_soup_Llama-3.1-8B-Inst
library_name: transformers
model-index:
- name: ldm_soup_Llama-3.1-8B-Inst
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 80.33
name: strict accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepAutoAI/ldm_soup_Llama-3.1-8B-Inst
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 31.1
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepAutoAI/ldm_soup_Llama-3.1-8B-Inst
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 11.56
name: exact match
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepAutoAI/ldm_soup_Llama-3.1-8B-Inst
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 5.26
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepAutoAI/ldm_soup_Llama-3.1-8B-Inst
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 11.52
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepAutoAI/ldm_soup_Llama-3.1-8B-Inst
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 32.07
name: accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepAutoAI/ldm_soup_Llama-3.1-8B-Inst
name: Open LLM Leaderboard
license: apache-2.0
language:
- en
base_model:
- meta-llama/Llama-3.1-8B-Instruct
Model Card for DeepAutoAI/ldm_soup_Llama-3.1-8B-Inst
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in
- compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
Overview
DeepAutoAI/ldm_soup_Llama-3.1-8B-Inst is developed by deepAuto.ai and builds upon the VAGOsolutions/Llama-3.1-SauerkrautLM-8B-Instruct model. Our approach leverages the base model’s pretrained weights and optimizes them for the Winogrande and ARC-Challenge datasets by training a latent diffusion model on the pretrained weights.
Through this process, we learn the distribution of the base model's weight space, enabling us to explore optimal configurations. We then sample multiple sets of weights, using the model-soup averaging technique to identify the best-performing weights for both datasets. These weights are merged using linear interpolation to create the final model weights for DeepAutoAI/ldm_soup_Llama-3.1-8B-Inst.
This approach has led to improved performance on previously unseen leaderboard tasks, all without any additional task-specific training.
The work is currently in progress
References
Diffusion-Based Neural Network Weights Generation
Evaluation
Results
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 28.64 |
| IFEval (0-Shot) | 80.33 |
| BBH (3-Shot) | 31.10 |
| MATH Lvl 5 (4-Shot) | 11.56 |
| GPQA (0-shot) | 5.26 |
| MuSR (0-shot) | 11.52 |
| MMLU-PRO (5-shot) | 32.07 |