WestKunai-Hermes-10.7b-test

Replicate the configuration utilized in the froggeric/WestLake-10.7B-v2 model to extend the WestKunai-Hermes-7b model to 10.7b.

Quantized versions :

• GGUF

comparison

Metric	diff	Current(10.7b)	Origin(7b)
Avg.	-3.76	69.75	73.51
AI2 Reasoning Challenge (25-Shot)	-3.07	68.09	71.16
HellaSwag (10-Shot)	-0.66	87.10	87.76
MMLU (5-Shot)	-0.34	64.43	64.77
TruthfulQA (0-shot)	-0.97	64.28	65.25
Winogrande (5-shot)	-0.31	82.72	83.03
GSM8k (5-shot)	-17.21	51.86	69.07

Merge Details

Models Merged

The following models were included in the merge:

• seyf1elislam/WestKunai-Hermes-7b

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
    - model: seyf1elislam/WestKunai-Hermes-7b
      layer_range: [0,9]
  - sources:
    - model: seyf1elislam/WestKunai-Hermes-7b
      layer_range: [5,14]
  - sources:
    - model: seyf1elislam/WestKunai-Hermes-7b
      layer_range: [10,19]
  - sources:
    - model: seyf1elislam/WestKunai-Hermes-7b
      layer_range: [15,24]
  - sources:
    - model: seyf1elislam/WestKunai-Hermes-7b
      layer_range: [20,32]
merge_method: passthrough
dtype: bfloat16

Usage Example

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "seyf1elislam/WestKunai-Hermes-10.7b-test"
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.	69.75
AI2 Reasoning Challenge (25-Shot)	68.09
HellaSwag (10-Shot)	87.10
MMLU (5-Shot)	64.43
TruthfulQA (0-shot)	64.28
Winogrande (5-shot)	82.72
GSM8k (5-shot)	51.86