NeuralMonarchCoderPearlBeagle
NeuralMonarchCoderPearlBeagle is a merge of the following models using LazyMergekit:
Goals
This is a TIES merge, formed from MonarchCoder-7b (A merge of Alpha Monarch and TessCoder) and NeuralPearlBeagle(which is a merge of mlabonne's NeuralBeagle14-7b and Pearl-7B-Slerp). It is a somewhat haphazard experiment to see if we can merge more math and coding capabilities into the already outstanding NeuralBeagle14-7b and still maintain the same positive chat abilities.
If you find this or my other merges useful, please consider sending a bit of BTC so I don't have to use Google Colab :D
BTC: bc1q8lc4mzdtdyz7fx44vaw3jn8qg6w4c3ypfxpdrv
ETH/POLYGON: 0x102a6fd187db8441d2cbead33ac70e87f382f114
🧩 Configuration
models:
- model: abideen/MonarchCoder-7B
parameters:
density: 0.6
weight: 0.5
- model: eldogbbhed/NeuralPearlBeagle
parameters:
density: 0.8
weight: 0.8
merge_method: ties
base_model: eldogbbhed/NeuralPearlBeagle
parameters:
normalize: true
int8_mask: true
dtype: float16
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "eldogbbhed/NeuralMonarchCoderPearlBeagle"
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. | 71.50 |
| AI2 Reasoning Challenge (25-Shot) | 68.52 |
| HellaSwag (10-Shot) | 87.22 |
| MMLU (5-Shot) | 64.53 |
| TruthfulQA (0-shot) | 61.19 |
| Winogrande (5-shot) | 80.51 |
| GSM8k (5-shot) | 67.02 |