---
base_model:
- unsloth/Qwen2.5-3B-Instruct
- unsloth/Qwen2.5-3B
library_name: transformers
tags:
- mergekit
- merge

---
# merged_output_ties_1_4

This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

## Merge Details
### Merge Method

This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [unsloth/Qwen2.5-3B](https://huggingface.co/unsloth/Qwen2.5-3B) as a base.

### Models Merged

The following models were included in the merge:
* [unsloth/Qwen2.5-3B-Instruct](https://huggingface.co/unsloth/Qwen2.5-3B-Instruct)
* triples/merged_model
* genstruct/merged_model
* kg/merged_model

### Configuration

The following YAML configuration was used to produce this model:

```yaml
models:
  # Base instructed model
  - model: unsloth/Qwen2.5-3B-Instruct
    parameters:
      weight: 1
      density: 1

  # Merged LoRA models
  - model: genstruct/merged_model
    parameters:
      weight: 1.0
      density: 1.0

  # - model: summary/merged_model
  #   parameters:
  #     weight: 1.0
  #     density: 1.0

  - model: kg/merged_model
    parameters:
      weight: 1.0
      density: 1.0

  #### THIS BREAKS KG!!!
  # - model: pII/merged_model
  #   parameters:
  #     weight: 1.0
  #     density: 1.0

  # #### Breaks KG!
  # - model: preference/merged_model
  #   parameters:
  #     weight: 1.0
  #     density: 1.0

  - model: triples/merged_model
    parameters:
      weight: 1.0
      density: 1.0

  # - model: suitable/merged_model
  #   parameters:
  #     weight: 1.0
  #     density: 1.0

  # - model: feedback/merged_model
  #   parameters:
  #     weight: 1.0
  #     density: 1.0

# Merge configuration
merge_method: ties
base_model: unsloth/Qwen2.5-3B
parameters:
  normalize: true
  int8_mask: true
dtype: bfloat16

# # Tokenizer configuration
# tokenizer_source: Qwen/Qwen1.5-14B-Chat
# tokenizer_parameters:
#   trust_remote_code: true
  
# # Output configuration
# output:
#   precision: bfloat16
#   model_format: safetensors
#   max_shard_size: "4GB"

# # Training configuration (for potential fine-tuning)
# training:
#   learning_rate: 2e-5
#   warmup_steps: 100
#   gradient_checkpointing: true
#   gradient_accumulation_steps: 4
  
# # Hardware optimization
# hardware:
#   mixed_precision: true
#   cuda_memory_fraction: 0.95
#   optimize_model_memory: true
```