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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
- Qwen/Qwen2.5-Coder-14B
- Qwen/Qwen2.5-Coder-14B-Instruct
- Azure99/Blossom-V6-14B
- arcee-ai/SuperNova-Medius
- arcee-ai/Virtuoso-Small-v2
- deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
- huihui-ai/DeepSeek-R1-Distill-Qwen-14B-abliterated-v2
pipeline_tag: text-generation
tags:
- merge
model-index:
- name: ZYH-LLM-Qwen2.5-14B-V4
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: 83.65
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=YOYO-AI/ZYH-LLM-Qwen2.5-14B-V4
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: 50.27
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=YOYO-AI/ZYH-LLM-Qwen2.5-14B-V4
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: 53.93
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=YOYO-AI/ZYH-LLM-Qwen2.5-14B-V4
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: 8.61
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=YOYO-AI/ZYH-LLM-Qwen2.5-14B-V4
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: 15.66
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=YOYO-AI/ZYH-LLM-Qwen2.5-14B-V4
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: 46.71
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=YOYO-AI/ZYH-LLM-Qwen2.5-14B-V4
name: Open LLM Leaderboard
---
# ZYH-LLM-Qwen2.5-14B-V4
*The fourth-generation model of ZYH-LLM-Qwen2.5 has been released!*
*Increase the proportion of the **R1 distillation model** in the model merging recipe while maintaining the model's **instruction-following ability** and **general capabilities.***
## Merge Template
```yaml
merge_method: model_stock
base_model: Instruction Model
models:
- model: Instruction Fine-tuning Model 1
- model: Instruction Fine-tuning Model 2
- model: Inference Fine-tuning Model 1
- model: Inference Fine-tuning Model 2
dtype: bfloat16
tokenizer_source: base
int8_mask: true
normalize: true
```
Using the above template for merging can improve the **calculation accuracy** and **inference ability** of the model without reducing the **general capabilities** of the instruction model.
**ZYH-LLM-Qwen2.5-V4** used this template during the model merging process.
## [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__ZYH-LLM-Qwen2.5-14B-V4-details)
| Metric |Value|
|-------------------|----:|
|Avg. |43.14|
|IFEval (0-Shot) |83.65|
|BBH (3-Shot) |50.27|
|MATH Lvl 5 (4-Shot)|53.93|
|GPQA (0-shot) |8.61|
|MuSR (0-shot) |15.66|
|MMLU-PRO (5-shot) |46.71|
## First stage:
*Create four different instruction models and code model*
```yaml
models:
- model: Qwen/Qwen2.5-14B-Instruct
parameters:
density: 1
weight: 1
lambda: 0.9
- model: Qwen/Qwen2.5-14B-Instruct-1M
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: Qwen2.5-14B-della-base
```
```yaml
models:
- model: Qwen/Qwen2.5-14B-Instruct
parameters:
density: 1
weight: 1
lambda: 0.9
- 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-della-v2
```
```yaml
models:
- model: Qwen/Qwen2.5-14B-Instruct
parameters:
density: 1
weight: 1
lambda: 0.9
- model: Qwen/Qwen2.5-14B-Instruct-1M
parameters:
density: 1
weight: 1
lambda: 0.9
merge_method: della
base_model: arcee-ai/SuperNova-Medius
parameters:
density: 1
weight: 1
lambda: 0.9
normalize: true
int8_mask: true
dtype: bfloat16
tokenizer_source: base
name: Qwen2.5-14B-della-Nova
```
```yaml
models:
- model: Qwen/Qwen2.5-14B-Instruct
parameters:
density: 1
weight: 1
lambda: 0.9
- 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-della-V6
```
```yaml
models:
- model: Qwen/Qwen2.5-Coder-14B-Instruct
parameters:
density: 1
weight: 1
lambda: 0.9
merge_method: della
base_model: Qwen/Qwen2.5-Coder-14B
parameters:
density: 1
weight: 1
lambda: 0.9
normalize: true
int8_mask: true
dtype: bfloat16
tokenizer_source: base
name: Qwen2.5-Coder-14B-della
```
## Second stage:
### Step 1:
*Create three instruction models with a bias towards reasoning by using templates.*
```yaml
merge_method: model_stock
base_model: Qwen2.5-14B-della-base
models:
- model: Qwen2.5-Coder-14B-della
- model: Qwen2.5-14B-della-v2
- model: deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
- model: huihui-ai/DeepSeek-R1-Distill-Qwen-14B-abliterated-v2
dtype: bfloat16
tokenizer_source: base
int8_mask: true
normalize: true
name: Qwen2.5-14B-mst-Coder
```
```yaml
merge_method: model_stock
base_model: Qwen2.5-14B-della-base
models:
- model: Qwen2.5-14B-della-V6
- model: Qwen2.5-14B-della-v2
- model: deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
- model: huihui-ai/DeepSeek-R1-Distill-Qwen-14B-abliterated-v2
dtype: bfloat16
tokenizer_source: base
int8_mask: true
normalize: true
name: Qwen2.5-14B-mst-V6
```
```yaml
merge_method: model_stock
base_model: Qwen2.5-14B-della-base
models:
- model: Qwen2.5-14B-della-Nova
- model: Qwen2.5-14B-della-v2
- model: deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
- model: huihui-ai/DeepSeek-R1-Distill-Qwen-14B-abliterated-v2
dtype: bfloat16
tokenizer_source: base
int8_mask: true
normalize: true
name: Qwen2.5-14B-mst-Nova
```
### Step 2:
*Create a pure instruction model to restore the generality of the final model.*
```yaml
merge_method: model_stock
base_model: Qwen2.5-14B-della-base
models:
- model: Qwen2.5-14B-della-Nova
- model: Qwen2.5-14B-della-v2
- model: Qwen2.5-14B-della-V6
dtype: bfloat16
tokenizer_source: base
int8_mask: true
normalize: true
name: Qwen2.5-14B-mst-it
```
## Third stage:
*Create a base model with a context of 1 million tokens.*
```yaml
merge_method: sce
models:
# Pivot model
- model: Qwen/Qwen2.5-14B-Instruct-1M
# Target models
- model: Qwen/Qwen2.5-14B
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-1M
```
```yaml
models:
- model: Qwen/Qwen2.5-14B-Instruct
parameters:
density: 1
weight: 1
lambda: 0.9
- model: Qwen/Qwen2.5-14B-Instruct-1M
parameters:
density: 1
weight: 1
lambda: 0.9
merge_method: della
base_model: Qwen2.5-14B-1M
parameters:
density: 1
weight: 1
lambda: 0.9
normalize: true
int8_mask: true
dtype: bfloat16
tokenizer_source: base
name: Qwen2.5-14B-della-1M
```
## Final stage:
```yaml
merge_method: model_stock
base_model: Qwen2.5-14B-della-1M
models:
- model: Qwen2.5-14B-mst-Coder
- model: Qwen2.5-14B-mst-V6
- model: Qwen2.5-14B-mst-Nova
- model: Qwen2.5-14B-mst-it
dtype: bfloat16
tokenizer_source: base
int8_mask: true
normalize: true
name: ZYH-LLM-Qwen2.5-14B-V4
``` |