ChimeraLlama-3-8B-v2
ChimeraLlama-3-8B-v2 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
- Locutusque/llama-3-neural-chat-v1-8b
- cloudyu/Meta-Llama-3-8B-Instruct-DPO
- vicgalle/Configurable-Llama-3-8B-v0.3
𧩠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: 0.55
- model: mlabonne/OrpoLlama-3-8B
parameters:
density: 0.55
weight: 0.05
- model: cognitivecomputations/dolphin-2.9-llama3-8b
parameters:
density: 0.55
weight: 0.1
- model: Locutusque/llama-3-neural-chat-v1-8b
parameters:
density: 0.55
weight: 0.05
- model: cloudyu/Meta-Llama-3-8B-Instruct-DPO
parameters:
density: 0.55
weight: 0.15
- model: vicgalle/Configurable-Llama-3-8B-v0.3
parameters:
density: 0.55
weight: 0.1
merge_method: dare_ties
base_model: NousResearch/Meta-Llama-3-8B
parameters:
int8_mask: true
dtype: float16
π» Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mlabonne/ChimeraLlama-3-8B-v2"
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. | 19.99 |
IFEval (0-Shot) | 44.69 |
BBH (3-Shot) | 28.48 |
MATH Lvl 5 (4-Shot) | 8.31 |
GPQA (0-shot) | 4.70 |
MuSR (0-shot) | 5.25 |
MMLU-PRO (5-shot) | 28.54 |
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Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard44.690
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard28.480
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard8.310
- acc_norm on GPQA (0-shot)Open LLM Leaderboard4.700
- acc_norm on MuSR (0-shot)Open LLM Leaderboard5.250
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard28.540