mlabonne/ChimeraLlama-3-8B-v3
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README.md

license, tags, base_model, model-index
license tags base_model model-index
other
merge
mergekit
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
name results
ChimeraLlama-3-8B-v3
task dataset metrics source
type name
text-generation Text Generation
name type args
IFEval (0-Shot) HuggingFaceH4/ifeval
num_few_shot
0
type value name
inst_level_strict_acc and prompt_level_strict_acc 44.08 strict accuracy
url name
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/ChimeraLlama-3-8B-v3 Open LLM Leaderboard
task dataset metrics source
type name
text-generation Text Generation
name type args
BBH (3-Shot) BBH
num_few_shot
3
type value name
acc_norm 27.65 normalized accuracy
url name
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/ChimeraLlama-3-8B-v3 Open LLM Leaderboard
task dataset metrics source
type name
text-generation Text Generation
name type args
MATH Lvl 5 (4-Shot) hendrycks/competition_math
num_few_shot
4
type value name
exact_match 7.85 exact match
url name
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/ChimeraLlama-3-8B-v3 Open LLM Leaderboard
task dataset metrics source
type name
text-generation Text Generation
name type args
GPQA (0-shot) Idavidrein/gpqa
num_few_shot
0
type value name
acc_norm 5.59 acc_norm
url name
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/ChimeraLlama-3-8B-v3 Open LLM Leaderboard
task dataset metrics source
type name
text-generation Text Generation
name type args
MuSR (0-shot) TAUR-Lab/MuSR
num_few_shot
0
type value name
acc_norm 8.38 acc_norm
url name
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/ChimeraLlama-3-8B-v3 Open LLM Leaderboard
task dataset metrics source
type name
text-generation Text Generation
name type config split args
MMLU-PRO (5-shot) TIGER-Lab/MMLU-Pro main test
num_few_shot
5
type value name
acc 29.65 accuracy
url name
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/ChimeraLlama-3-8B-v3 Open LLM Leaderboard

ChimeraLlama-3-8B-v3

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

🧩 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

Metric Value
Avg. 20.53
IFEval (0-Shot) 44.08
BBH (3-Shot) 27.65
MATH Lvl 5 (4-Shot) 7.85
GPQA (0-shot) 5.59
MuSR (0-shot) 8.38
MMLU-PRO (5-shot) 29.65
Description
Model synced from source: mlabonne/ChimeraLlama-3-8B-v3
Readme 30 KiB