metadata
license: cc-by-nc-4.0
tags:
- merge
- mergekit
base_model:
- seyf1elislam/WestKunai-Hermes-7b
model-index:
- name: WestKunai-Hermes-10.7b-test
results:
- task:
type: dataset_type
name: dataset_split
dataset:
name: metric_type
type: dataset_name
config: dataset_revision
split: dataset_args
revision: metric_name
args: task_type
metrics:
- type: metric_value
value: dataset_config
name: task_name
config: source_name
args: source_url
- type: metric_value
value: dataset_config
name: task_name
config: source_name
args: source_url
- type: metric_value
value: dataset_config
name: task_name
config: source_name
args: source_url
- type: metric_value
value: dataset_config
name: task_name
config: source_name
args: source_url
- task:
type: dataset_type
name: dataset_split
dataset:
name: metric_type
type: dataset_name
config: dataset_args
split: metric_name
revision: task_type
args: task_name
metrics:
- type: metric_value
value: dataset_config
name: source_name
config: source_url
- type: metric_value
value: dataset_config
name: source_name
config: source_url
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 :
Merge Details
Models Merged
The following models were included in the merge:
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 |