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---
license: apache-2.0
language:
- en
- zh
base_model:
- Qwen/Qwen2.5-14B
- Qwen/Qwen2.5-14B-Instruct
- Qwen/Qwen2.5-14B-Instruct-1M
- EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2
- Azure99/Blossom-V6-14B
- arcee-ai/Virtuoso-Small-v2
pipeline_tag: text-generation
tags:
- merge
model-index:
- name: Qwen2.5-14B-1M-YOYO-V3
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 83.98
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=YOYO-AI/Qwen2.5-14B-1M-YOYO-V3
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 49.47
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=YOYO-AI/Qwen2.5-14B-1M-YOYO-V3
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 53.55
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=YOYO-AI/Qwen2.5-14B-1M-YOYO-V3
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 10.51
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=YOYO-AI/Qwen2.5-14B-1M-YOYO-V3
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 11.10
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=YOYO-AI/Qwen2.5-14B-1M-YOYO-V3
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 46.74
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=YOYO-AI/Qwen2.5-14B-1M-YOYO-V3
name: Open LLM Leaderboard
---
# Qwen2.5-14B-1M-YOYO-V3
*[Qwen2.5-YOYO Fourth-Gen Model Officially Released!](https://huggingface.co/YOYO-AI/Qwen2.5-14B-YOYO-V4)*
This time, I not only released the model but also shared some model merging insights that might be even more valuable than the model itself.
Let’s start by looking at the initial merge configuration (YAML):
```yaml
merge_method: model_stock
base_model: Qwen/Qwen2.5-14B
models:
- model: Qwen/Qwen2.5-14B-instruct
- model: Qwen/Qwen2.5-14B-instruct-1M
dtype: bfloat16
```
Does it seem like there are no issues at all? However, merged models occasionally exhibit **uncontrollable outputs**, likely due to significant discrepancies between instruction-tuned models and base models.
To address this, I first attempted to directly integrate a fine-tuned model with smaller divergence from the base model, such as **Virtuoso-Small-v2**.
This gave rise to [Qwen2.5-14B-YOYO-latest-V2](https://huggingface.co/YOYO-AI/Qwen2.5-14B-YOYO-latest-V2).
```yaml
merge_method: model_stock
base_model: Qwen/Qwen2.5-14B
models:
- model: Qwen/Qwen2.5-14B-instruct
- model: Qwen/Qwen2.5-14B-instruct-1M
- model: arcee-ai/Virtuoso-Small-v2
dtype: bfloat16
name: Qwen2.5-14B-YOYO-latest-V2
```
Although the uncontrollable output issue has been addressed, the model still lacks stability.
Through practical experimentation, I found that first merging **"high-divergence"** models (significantly different from the base) into **"low-divergence"** models (closer to the base) using the [DELLA](https://arxiv.org/abs/2406.11617) method, then applying the [Model Stock](https://arxiv.org/abs/2403.19522) method, ultimately produces a model that is not only more stable but also achieves better performance.
## Key models used:
*1. Low-divergence, high-performance models:*
- Virtuoso-Small-v2
- Blossom-V6-14B
*2. High-divergence, instruction-focused models:*
- Qwen2.5-14B-instruct
- Qwen2.5-14B-instruct-1M
## DELLA Merge Configuration:
```yaml
models:
- model: Qwen/Qwen2.5-14B-Instruct
parameters:
density: 1
weight: 1
lambda: 0.9
merge_method: della
base_model: arcee-ai/Virtuoso-Small-v2
parameters:
density: 1
weight: 1
lambda: 0.9
normalize: true
int8_mask: true
dtype: bfloat16
tokenizer_source: base
name: Qwen2.5-14B-YOYO-della1
```
```yaml
models:
- model: Qwen/Qwen2.5-14B-Instruct-1M
parameters:
density: 1
weight: 1
lambda: 0.9
merge_method: della
base_model: arcee-ai/Virtuoso-Small-v2
parameters:
density: 1
weight: 1
lambda: 0.9
normalize: true
int8_mask: true
dtype: bfloat16
tokenizer_source: base
name: Qwen2.5-14B-YOYO-della2
```
```yaml
models:
- model: Qwen/Qwen2.5-14B-Instruct
parameters:
density: 1
weight: 1
lambda: 0.9
merge_method: della
base_model: Azure99/Blossom-V6-14B
parameters:
density: 1
weight: 1
lambda: 0.9
normalize: true
int8_mask: true
dtype: bfloat16
tokenizer_source: base
name: Qwen2.5-14B-YOYO-della3
```
```yaml
models:
- model: Qwen/Qwen2.5-14B-Instruct-1M
parameters:
density: 1
weight: 1
lambda: 0.9
merge_method: della
base_model: Azure99/Blossom-V6-14B
parameters:
density: 1
weight: 1
lambda: 0.9
normalize: true
int8_mask: true
dtype: bfloat16
tokenizer_source: base
name: Qwen2.5-14B-YOYO-della4
```
This approach yielded four variants:
- `Qwen2.5-14B-YOYO-della1`
- `Qwen2.5-14B-YOYO-della2`
- `Qwen2.5-14B-YOYO-della3`
- `Qwen2.5-14B-YOYO-della4`
## Base Model:
To enhance base model roleplay and creative writing capabilities, I applied the same strategy:
```yaml
models:
- model: EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2
parameters:
density: 1
weight: 1
lambda: 0.9
merge_method: della
base_model: Qwen/Qwen2.5-14B
parameters:
density: 1
weight: 1
lambda: 0.9
normalize: true
int8_mask: true
dtype: bfloat16
tokenizer_source: base
name: EVA-Qwen2.5-14B-base
```
Next, I extended the context length using the SCE method:
```yaml
merge_method: sce
models:
- model: EVA-Qwen2.5-14B-base
base_model: Qwen/Qwen2.5-14B-Instruct-1M
parameters:
select_topk: 1
dtype: bfloat16
tokenizer_source: base
normalize: true
int8_mask: true
name: Qwen2.5-14B-pro
```
## Final Merge Step:
```yaml
merge_method: model_stock
base_model: Qwen2.5-14B-pro
models:
- model: Qwen2.5-14B-YOYO-della1
- model: Qwen2.5-14B-YOYO-della2
- model: Qwen2.5-14B-YOYO-della3
- model: Qwen2.5-14B-YOYO-della4
dtype: bfloat16
tokenizer_source: base
int8_mask: true
normalize: true
name: Qwen2.5-14B-1M-YOYO-V3
```
I hope this helps!
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/YOYO-AI__Qwen2.5-14B-1M-YOYO-V3-details)
| Metric |Value|
|-------------------|----:|
|Avg. |42.56|
|IFEval (0-Shot) |83.98|
|BBH (3-Shot) |49.47|
|MATH Lvl 5 (4-Shot)|53.55|
|GPQA (0-shot) |10.51|
|MuSR (0-shot) |11.10|
|MMLU-PRO (5-shot) |46.74|
|