MonarchCoder-7B
MonarchCoder-7B is a slerp merge of the following models using LazyMergekit:
The main aim behind creating this model is to create a model that performs well in reasoning, conversation, and coding. AlphaMonarch pperforms amazing on reasoning and conversation tasks. Merging AlphaMonarch with a coding model yielded MonarchCoder-7B which performs better on OpenLLM, Nous, and HumanEval benchmark. Although MonarchCoder-2x7B performs better than MonarchCoder-7B.
🏆 Evaluation results
| Metric |MonarchCoder-Moe-2x7B||MonarchCoder-7B||AlphaMonarch|
|---------------------------------|---------------------|-----------------|------------|
|Avg. | 74.23 | 71.17 | 75.99 |
|HumanEval | 41.15 | 39.02 | 34.14 |
|HumanEval+ | 29.87 | 31.70 | 29.26 |
|MBPP | 40.60 | * | * |
|AI2 Reasoning Challenge (25-Shot)| 70.99 | 68.52 | 73.04 |
|HellaSwag (10-Shot) | 87.99 | 87.30 | 89.18 |
|MMLU (5-Shot) | 65.11 | 64.65 | 64.40 |
|TruthfulQA (0-shot) | 71.25 | 61.21 | 77.91 |
|Winogrande (5-shot) | 80.66 | 80.19 .| 84.69 |
|GSM8k (5-shot) . | 69.37 | 65.13 | 66.72 |
🧩 Configuration
slices:
- sources:
- model: Syed-Hasan-8503/Tess-Coder-7B-Mistral-v1.0
layer_range: [0, 32]
- model: mlabonne/AlphaMonarch-7B
layer_range: [0, 32]
merge_method: slerp
base_model: mlabonne/AlphaMonarch-7B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "abideen/MonarchCoder-7B"
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"])