README.md

---
base_model:
- grimjim/Mistral-Starling-merge-trial1-7B
- grimjim/kukulemon-7B
library_name: transformers
tags:
- mergekit
- merge
license: cc-by-nc-4.0
pipeline_tag: text-generation
---
# cuckoo-starling-32k-7B

For this merged model, rope theta was in config.json was manually adjusted down to 100K, a value less than 1M as initially released by Mistral for v0.2, but higher than the 10K that accompanied practical 8K context for v0.1. We idly conjecture that 1M rope theta might improve performance for needle-in-a-haystack queries; however, during informal testing, narrative coherence seemed to occasionally suffer under 1M rope theta. Furthermore, the results reported in the arXiv paper [Scaling Laws of RoPE-based Extrapolation](https://arxiv.org/abs/2310.05209) suggest that 1M rope theta may be overkill for a 32K token context window.

Lightly tested with temperature 0.9-1.0 and minP 0.02, using ChatML prompts. The model natively supports Alpaca prompts.

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

Full weights: [grimjim/cuckoo-starling-32k-7B](https://huggingface.co/grimjim/cuckoo-starling-32k-7B/)
GGUFs: [grimjim/cuckoo-starling-32k-7B-GGUF](https://huggingface.co/grimjim/cuckoo-starling-32k-7B-GGUF/)

## Merge Details
### Merge Method

This model was merged using the SLERP merge method.

### Models Merged

The following models were included in the merge:
* [grimjim/Mistral-Starling-merge-trial1-7B](https://huggingface.co/grimjim/Mistral-Starling-merge-trial1-7B)
* [grimjim/kukulemon-7B](https://huggingface.co/grimjim/kukulemon-7B)

### Configuration

The following YAML configuration was used to produce this model:

```yaml
slices:
  - sources:
      - model: grimjim/Mistral-Starling-merge-trial1-7B
        layer_range: [0, 32]
      - model: grimjim/kukulemon-7B
        layer_range: [0, 32]
# or, the equivalent models: syntax:
# models:
merge_method: slerp
base_model: grimjim/Mistral-Starling-merge-trial1-7B
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5 # fallback for rest of tensors
dtype: bfloat16

```