ChimeraLlama-3-8B / README.md
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metadata
license: other
tags:
  - merge
  - mergekit
  - lazymergekit
  - llama
base_model:
  - NousResearch/Meta-Llama-3-8B-Instruct
  - mlabonne/OrpoLlama-3-8B
  - Locutusque/Llama-3-Orca-1.0-8B
  - abacusai/Llama-3-Smaug-8B

ChimeraLlama-3-8B

ChimeraLlama-3-8B outperforms Llama 3 8B Instruct on Nous' benchmark suite.

ChimeraLlama-3-8B is a merge of the following models using LazyMergekit:

πŸ† Evaluation

Nous

Evaluation performed using LLM AutoEval, see the entire leaderboard here.

Model Average AGIEval GPT4All TruthfulQA Bigbench
mlabonne/ChimeraLlama-3-8B πŸ“„ 51.58 39.12 71.81 52.4 42.98
meta-llama/Meta-Llama-3-8B-Instruct πŸ“„ 51.34 41.22 69.86 51.65 42.64
mlabonne/OrpoLlama-3-8B πŸ“„ 48.63 34.17 70.59 52.39 37.36
meta-llama/Meta-Llama-3-8B πŸ“„ 45.42 31.1 69.95 43.91 36.7

🧩 Configuration

models:
  - model: NousResearch/Meta-Llama-3-8B
    # No parameters necessary for base model
  - model: NousResearch/Meta-Llama-3-8B-Instruct
    parameters:
      density: 0.58
      weight: 0.4
  - model: mlabonne/OrpoLlama-3-8B
    parameters:
      density: 0.52
      weight: 0.2
  - model: Locutusque/Llama-3-Orca-1.0-8B
    parameters:
      density: 0.52
      weight: 0.2
  - model: abacusai/Llama-3-Smaug-8B
    parameters:
      density: 0.52
      weight: 0.2
merge_method: dare_ties
base_model: NousResearch/Meta-Llama-3-8B
parameters:
  int8_mask: true
dtype: float16

πŸ’» Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mlabonne/ChimeraLlama-3-8B"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])