L3-12B-Lunaris-v1
L3-12B-Lunaris-v1 is a self merge of the following model using LazyMergekit with --clone-tensors argument added:
Works best with lower temperature, between 0.8-0.9.
🧩 Configuration
dtype: bfloat16
merge_method: passthrough
slices:
- sources:
- layer_range: [0, 8]
model: Sao10K/L3-8B-Lunaris-v1
parameters:
scale_rules:
- filter: value
value: 0.8
- sources:
- layer_range: [8, 16]
model: Sao10K/L3-8B-Lunaris-v1
parameters:
scale_rules:
- filter: value
value: 0.8
- sources:
- layer_range: [16, 24]
model: Sao10K/L3-8B-Lunaris-v1
parameters:
scale_rules:
- filter: value
value: 1.0
- sources:
- layer_range: [24, 32]
model: Sao10K/L3-8B-Lunaris-v1
parameters:
scale_rules:
- filter: value
value: 1.0
- sources:
- layer_range: [0, 8]
model: Sao10K/L3-8B-Lunaris-v1
parameters:
scale_rules:
- filter: value
value: 0.7
- sources:
- layer_range: [8, 16]
model: Sao10K/L3-8B-Lunaris-v1
parameters:
scale_rules:
- filter: value
value: 0.7
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Tremontaine/L3-12B-Lunaris-v1"
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. | 25.38 |
| IFEval (0-Shot) | 69.09 |
| BBH (3-Shot) | 32.18 |
| MATH Lvl 5 (4-Shot) | 8.16 |
| GPQA (0-shot) | 7.94 |
| MuSR (0-shot) | 4.05 |
| MMLU-PRO (5-shot) | 30.83 |