miquliz-120b

⚠️ This older model that has been replaced by its improved successor: miquliz-120b-v2.0 ⚠️

EXL2: 2.4bpw | 2.65bpw | 2.9bpw | 4.0bpw
GGUF: IQ3_XXS | Q4_K_S+Q4_K_M

This is a 120b frankenmerge created by interleaving layers of miqu-1-70b-sf with lzlv_70b_fp16_hf using mergekit.

Inspired by goliath-120b.

Thanks for the support, CopilotKit - the open-source platform for building in-app AI Copilots into any product, with any LLM model. Check out their GitHub.

Thanks for the EXL2 and GGUF quants, Lone Striker and NanoByte!

Review

u/SomeOddCodeGuy wrote on r/LocalLLaMA:

So I did try out Miquliz last night, and Im not sure if it was the character prompt or what... but it's a lot less coherent than miqu-1-120b is.

Quality wise, I feel like Miqu-1-120b has dethroned Goliath-120b as the most coherent model I've ever worked with. Alternatively, Miquliz felt a bit closer to what I've come to see from some of the Yi-34b fine-tunes: some impressive moments, but also some head-scratchers that made me wonder what in the world it was talking about lol.

I'll keep trying it a little more, but I think the difference between the two is night and day, with Miqu-1-120b still being the best model I've ever used for non-coding tasks (haven't tested it on coding yet).

Note: I have made a version 2.0 of MiquLiz with an improved "mixture" that better combines the two very different models used: wolfram/miquliz-120b-v2.0

Prompt template: Mistral

<s>[INST] {prompt} [/INST]

Model Details

Max Context: 32768 tokens
Layers: 137

Merge Details

Merge Method

This model was merged using the passthrough merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

yaml

dtype: float16
merge_method: passthrough
slices:
- sources:
  - layer_range: [0, 16]
    model: 152334H/miqu-1-70b-sf
- sources:
  - layer_range: [8, 24]
    model: lizpreciatior/lzlv_70b_fp16_hf
- sources:
  - layer_range: [17, 32]
    model: 152334H/miqu-1-70b-sf
- sources:
  - layer_range: [25, 40]
    model: lizpreciatior/lzlv_70b_fp16_hf
- sources:
  - layer_range: [33, 48]
    model: 152334H/miqu-1-70b-sf
- sources:
  - layer_range: [41, 56]
    model: lizpreciatior/lzlv_70b_fp16_hf
- sources:
  - layer_range: [49, 64]
    model: 152334H/miqu-1-70b-sf
- sources:
  - layer_range: [57, 72]
    model: lizpreciatior/lzlv_70b_fp16_hf
- sources:
  - layer_range: [65, 80]
    model: 152334H/miqu-1-70b-sf

Credits & Special Thanks

1st model:
- original (unreleased) model: mistralai (Mistral AI_)
- leaked model: miqudev/miqu-1-70b
- f16 model: 152334H/miqu-1-70b-sf
2nd model: lizpreciatior/lzlv_70b_fp16_hf
mergekit: arcee-ai/mergekit: Tools for merging pretrained large language models.
mergekit_config.yml: alpindale/goliath-120b

Support

My Ko-fi page if you'd like to tip me to say thanks or request specific models to be tested or merged with priority. Also consider supporting your favorite model creators, quantizers, or frontend/backend devs if you can afford to do so. They deserve it!

Disclaimer

This model contains leaked weights and due to its content it should not be used by anyone. 😜

But seriously:

License

What I know: Weights produced by a machine are not copyrightable so there is no copyright owner who could grant permission or a license to use, or restrict usage, once you have acquired the files.

Ethics

What I believe: All generative AI, including LLMs, only exists because it is trained mostly on human data (both public domain and copyright-protected, most likely acquired without express consent) and possibly synthetic data (which is ultimately derived from human data, too). It is only fair if something that is based on everyone's knowledge and data is also freely accessible to the public, the actual creators of the underlying content. Fair use, fair AI!