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SteelStorage / Model details

L3.3-MS-Evayale-70B

SteelStorage/L3.3-MS-Evayale-70B

merged
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About this model

SteelStorage's L3.3-MS-Evayale-70B is a 70B-parameter Llama 3.3 merge, released as bfloat16 safetensors weights. According to the mergekit configuration in the model card, it uses the model_stock method to combine EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.0 and Sao10K/L3.3-70B-Euryale-v2.3 on an unsloth/Llama-3.3-70B-Instruct base. The creator, identified on the card as SteelSkull, says they made it because they liked EVA's storytelling and Euryale's prose and scene detail, with the goal of merging the storytelling strengths of both models while keeping the positives of each; these qualities have not been independently evaluated here. The card also links recommended system prompt templates and third-party GGUF quantizations hosted in separate repositories, which are not part of this safetensors release. No license is listed in the repository metadata.

Description sources

Source-based description, not an independent evaluation. Check the linked sources for the creator’s claims and limitations.

Size and formats

Base model
EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.0, Sao10K/L3.3-70B-Euryale-v2.3, unsloth/Llama-3.3-70B-Instruct
Parameters
70.5537B
Main repository formats
safetensors
License tag
Unknown
Languages
Unknown
Last updated
2024-12-11 · 22:00:53.000 UTC

What changed

Model types are creator-reported, not independently tested. These labels describe source metadata, not a guarantee of behavior.

  • mergedsource tag: base_model:merge:EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.0
  • mergedsource tag: base_model:merge:Sao10K/L3.3-70B-Euryale-v2.3
  • mergedsource tag: base_model:merge:unsloth/Llama-3.3-70B-Instruct

Family evidence

These groupings are source-reported. Explicit base identifiers take priority over generic architecture tags.

  • Llama: Base model: EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.0
  • Llama: Base model: unsloth/Llama-3.3-70B-Instruct

Available versions

Versions appear here only when they explicitly name this exact model as their source. Check each repository for available files.

Link to this listing

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Markdown

[Listed on Custom Clankers](https://customclankers.com/models/steelstorage-l3-3-ms-evayale-70b/)

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<a href="https://customclankers.com/models/steelstorage-l3-3-ms-evayale-70b/">Listed on Custom Clankers</a>

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Customize or build on this model

Listed from public GitHub data, not tested by us. Read the code and license before you run anything.

Tools to customize it further

Remove more refusals

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Fine-tune it on your own data

  • Unsloth ↗ ★ 78,592

    A desktop app and Python library for running and training models locally. Its README lists LoRA, QLoRA, full fine-tuning, DPO and GRPO, plus export to formats including GGUF, on Windows, Linux, WSL and macOS.

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Make a smaller download (quantize)

  • llama.cpp ↗ ★ 132,430

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  • MLX LM ↗ ★ 7,479

    Apple's MLX package for running and fine-tuning models on Apple silicon. It can quantize models and upload them to the Hugging Face Hub, and supports LoRA and full fine-tuning.

Merge it with another model

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Learn the techniques

  • LLM Course ↗ ★ 84,721

    A free course with roadmaps and Colab notebooks. Its notebooks include one-click tools for merging with MergeKit, quantizing to GGUF, GPTQ, EXL2, AWQ and HQQ, and automatic abliteration with custom datasets.

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Source provenance

Downloads
34
Likes
12
Metadata checked
2026-10-27 · 00:15:15 UTC
First seen in index
2026-10-27 · 00:15:15 UTC
Source revision
1a0a1d530365d66f0a92dfb3e58b9383b7d581dd

Source-card excerpt

Source model card excerpt: L3.3-MS-Evayale-70B L3.3-MS-Evayale-70B Creator: SteelSkull About Evayale-70B: Name Legend: L3.3 = Llama 3.3 MS = Model Stock Evayale = mix of EVA-LLAMA-0.0 and EURYALE-v2.3 70B

From SteelStorage/L3.3-MS-Evayale-70B. Creator model-card excerpt, not an independent review.

Inspect before you run

Low refusal is not proof of correctness. Outputs can be inaccurate, harmful, or biased. Review the model card, license and files yourself. A license tag is not commercial-use advice. Parameter counts are source metadata; no hardware compatibility is implied.

This directory does not host or redistribute model weights. Refusal profiles are measured by us on a fixed prompt set; all other behavior claims are source-reported.

Collection rules & limitations ↗