ChimeraLlama-3-8B-v3

ChimeraLlama-3-8B-v3 is a merge of the following models using LazyMergekit:

• NousResearch/Meta-Llama-3-8B-Instruct
• mlabonne/OrpoLlama-3-8B
• cognitivecomputations/dolphin-2.9-llama3-8b
• Danielbrdz/Barcenas-Llama3-8b-ORPO
• VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct
• vicgalle/Configurable-Llama-3-8B-v0.3
• MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3

🧩 Configuration

models:
 - model: NousResearch/Meta-Llama-3-8B
 # No parameters necessary for base model
 - model: NousResearch/Meta-Llama-3-8B-Instruct
 parameters:
 density: 0.6
 weight: 0.5
 - model: mlabonne/OrpoLlama-3-8B
 parameters:
 density: 0.55
 weight: 0.05
 - model: cognitivecomputations/dolphin-2.9-llama3-8b
 parameters:
 density: 0.55
 weight: 0.05
 - model: Danielbrdz/Barcenas-Llama3-8b-ORPO
 parameters:
 density: 0.55
 weight: 0.2
 - model: VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct
 parameters:
 density: 0.55
 weight: 0.1
 - model: vicgalle/Configurable-Llama-3-8B-v0.3
 parameters:
 density: 0.55
 weight: 0.05
 - model: MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3
 parameters:
 density: 0.55
 weight: 0.05
merge_method: dare_ties
base_model: NousResearch/Meta-Llama-3-8B
parameters:
 int8_mask: true
dtype: float16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mlabonne/ChimeraLlama-3-8B-v3"
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"])

Open LLM Leaderboard Evaluation Results

Detailed results can be found here