
Full weight fine tuned on two epochs of [SlimOrca](https://huggingface.co/datasets/Open-Orca/SlimOrca). Uses Mistral Instruct's prompt format.

The base model for this came from a variation on Undi's [Mistral 11B recipe](https://huggingface.co/Undi95/Mistral-11B-v0.1). The `o_proj` and `down_proj` tensors were set to zero in the added layers, making the output exactly identical to Mistral 7B before training.

Benchmarks look good locally but still evaluating actual usefulness.


### Reproducing

This [mergekit](https://github.com/cg123/mergekit) config was used to produce the base model:
```yml
slices:
  - sources:
      - model: mistralai/Mistral-7B-v0.1
        layer_range: [0, 24]
  - sources: # add middle layers with residuals scaled to zero
      - model: mistralai/Mistral-7B-v0.1
        layer_range: [8, 24]
        parameters:
          scale:
            - filter: o_proj
              value: 0.0
            - filter: down_proj
              value: 0.0
            - value: 1.0
  - sources:
      - model: mistralai/Mistral-7B-v0.1
        layer_range: [24, 32]
merge_method: passthrough
dtype: bfloat16
```

The axolotl config for fine tuning is available [here](https://huggingface.co/chargoddard/mistral-11b-slimorca/blob/main/axolotl_config.yaml).

{"language": ["en"], "license": "apache-2.0", "datasets": ["Open-Orca/SlimOrca"], "base_model": "mistralai/Mistral-7B-v0.1"}
transformers pytorch safetensors mistral text-generation en dataset:Open-Orca/SlimOrca base_model:mistralai/Mistral-7B-v0.1 license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us