---
base_model:
- cognitivecomputations/dolphin-2.9-llama3-8b
- meta-llama/Meta-Llama-3-8B-Instruct
library_name: transformers
tags:
- mergekit
- merge
- llama
- llama3
license: other
license_name: llama3
license_link: LICENSE
---

# Model Details

Uses ChatML but Alpaca probably works as well.

[Roleplaying presets for SillyTavern](https://huggingface.co/Virt-io/SillyTavern-Presets)

Configs copied from:
- [chargoddard/mistral-11b-slimorca](https://huggingface.co/chargoddard/mistral-11b-slimorca)
- [Replete-AI/Llama-3-11.5B-V2](https://huggingface.co/Replete-AI/Llama-3-11.5B-V2)
- [abacusai/TheProfessor-155b](https://huggingface.co/abacusai/TheProfessor-155b)


A try at a larger llama3 model.

Using [cognitivecomputations/dolphin-2.9-llama3-8b](cognitivecomputations/dolphin-2.9-llama3-8b) for an uncensored base and [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) as the duplicated layers as I really like its instructions following abilities. Hoping that it will be smarter and less censored.

---
# llama3-11.5B

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 [linear](https://arxiv.org/abs/2203.05482) merge method.

### Models Merged

The following models were included in the merge:
* [cognitivecomputations/dolphin-2.9-llama3-8b](https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b)
* [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct)

### Configuration

The following YAML configuration was used to produce this model:

```yaml
merge_method: linear # use linear so we can include multiple models, albeit at a zero weight
parameters:
  weight: 1.0 # weight everything as 1 unless specified otherwise - linear with one model weighted at 1 is a no-op like passthrough
slices:
  - sources:
      - model: cognitivecomputations/dolphin-2.9-llama3-8b # embed_tokens comes along with the ride with whatever is the first layer
        layer_range: [0, 1]
      - model:  NousResearch/Meta-Llama-3-8B-Instruct # add dummy second model with 0 weight so tokenizer-based merge routine is invoked for embed_tokens
        layer_range: [0, 1]
        parameters:
          weight: 0
  - sources:
      - model: cognitivecomputations/dolphin-2.9-llama3-8b
        layer_range: [1, 24]
  - sources:
      - model: NousResearch/Meta-Llama-3-8B-Instruct
        layer_range: [8, 24]
        parameters:
          scale:
            - filter: o_proj
              value: 0.0
            - filter: down_proj
              value: 0.0
            - value: 1.0
  - sources:
      - model: cognitivecomputations/dolphin-2.9-llama3-8b
        layer_range: [24, 31]
  - sources: # same as above, but for lm_head with the last layer
      - model: cognitivecomputations/dolphin-2.9-llama3-8b
        layer_range: [31, 32]
      - model: NousResearch/Meta-Llama-3-8B-Instruct
        layer_range: [31, 32]
        parameters:
          weight: 0
dtype: bfloat16
tokenizer_source: model:cognitivecomputations/dolphin-2.9-llama3-8b # keep exact tokenizer used by dolphin - or you could use `union` if you add all of the input models to the first/last slice

```