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---
base_model:
- defog/llama-3-sqlcoder-8b
- meta-llama/Meta-Llama-3-8B-Instruct
library_name: transformers
tags:
- mergekit
- merge
---
# QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF
This is quantized version of [arcee-ai/sepctrum-ties-sqlcoder-8b](https://huggingface.co/arcee-ai/sepctrum-ties-sqlcoder-8b) created using llama.cpp
# Original Model Card
# merge
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 [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) as a base.
### Models Merged
The following models were included in the merge:
* [defog/llama-3-sqlcoder-8b](https://huggingface.co/defog/llama-3-sqlcoder-8b)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
merge_method: ties
base_model: meta-llama/Meta-Llama-3-8B-Instruct
models:
- model: defog/llama-3-sqlcoder-8b
parameters:
weight:
- filter: mlp.down_proj
value: [0, 0, 0, 0, 0, 0, 0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0, 0.5, 0, 0, 0, 0, 0, 0.5, 0.5, 0.5, 0.5, 0.5, 0, 0, 0]
- filter: mlp.gate_proj
value: [0, 0, 0, 0, 0, 0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0.5]
- filter: mlp.up_proj
value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0.5, 0, 0, 0, 0, 0.5, 0, 0.5, 0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
- filter: self_attn.k_proj
value: [0.5, 0.5, 0.5, 0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0, 0.5, 0.5, 0.5, 0.5, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0]
- filter: self_attn.o_proj
value: [0.5, 0.5, 0.5, 0.5, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0.5, 0.5, 0.5, 0.5, 0, 0.5, 0, 0, 0, 0.5, 0.5, 0.5, 0.5, 0.5, 0]
- filter: self_attn.q_proj
value: [0, 0, 0.5, 0.5, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0, 0.5, 0.5, 0.5, 0.5, 0.5]
- filter: self_attn.v_proj
value: [0.5, 0, 0.5, 0, 0, 0.5, 0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0, 0, 0, 0, 0.5, 0.5, 0, 0, 0, 0, 0.5, 0, 0, 0.5, 0, 0, 0.5, 0.5]
- value: [0]
density: 0.75
- model: meta-llama/Meta-Llama-3-8B-Instruct
parameters:
weight:
- filter: mlp.down_proj
value: [1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 0.5, 1, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1]
- filter: mlp.gate_proj
value: [1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5]
- filter: mlp.up_proj
value: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 1, 1, 1, 1, 0.5, 1, 0.5, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
- filter: self_attn.k_proj
value: [0.5, 0.5, 0.5, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 1]
- filter: self_attn.o_proj
value: [0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 0.5, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 1]
- filter: self_attn.q_proj
value: [1, 1, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 0.5, 0.5, 0.5, 0.5, 0.5]
- filter: self_attn.v_proj
value: [0.5, 1, 0.5, 1, 1, 0.5, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 0.5, 0.5, 1, 1, 1, 1, 0.5, 1, 1, 0.5, 1, 1, 0.5, 0.5]
- value: [1]
density: 1.0
parameters: {normalize: true, int8_mask: true}
dtype: bfloat16
```
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