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EVA-UNIT-01 / Model details

EVA-Qwen2.5-32B-v0.2

EVA-UNIT-01/EVA-Qwen2.5-32B-v0.2

fine-tuned
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About this model

EVA-UNIT-01's EVA-Qwen2.5-32B-v0.2 is a full-parameter fine-tune of Qwen/Qwen2.5-32B, created by Kearm, Auri, and Cahvay and released as safetensors under the Apache-2.0 license. According to the model card, it is a roleplay and storywriting specialist trained on synthetic and natural data that expands the Celeste 70B 0.1 mixture with Opus Instruct data filtered for refusals, Gryphe writing and roleplay subsets, Epustrious SynthRP and Synthstruct, and a Dolphin-2.9.3 subset. The creator says version 0.2 reprocessed the whole dataset after a pipeline mistake had left non-Unicode characters in it, and claims this removes weird generation artifacts and improves stability; these effects have not been independently tested here. The model uses ChatML prompting, and the card recommends temperature 1, min-p 0.05, top-a 0.2, and repetition penalty 1.03.

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
Qwen/Qwen2.5-32B
Parameters
32.7639B
Main repository formats
safetensors
License tag
apache-2.0
Languages
Unknown
Last updated
2024-11-14 · 13:19:28.000 UTC

What changed

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

  • fine-tunedsource tag: base_model:finetune:Qwen/Qwen2.5-32B

Family evidence

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

  • Qwen: Base model: Qwen/Qwen2.5-32B

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

Made or quantized this model? Paste one of these into your model card or README so people can find this listing.

Markdown

[Listed on Custom Clankers](https://customclankers.com/models/eva-unit-01-eva-qwen2-5-32b-v0-2/)

HTML

<a href="https://customclankers.com/models/eva-unit-01-eva-qwen2-5-32b-v0-2/">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

  • Heretic ↗ ★ 36,457

    Removes refusal behavior from transformer models automatically. It pairs directional ablation (abliteration) with an Optuna parameter search that tries to cut refusals while keeping the model close to the original, so you don't have to pick layers by hand.

  • OBLITERATUS ↗ ★ 8,949

    A larger refusal-removal toolkit with several abliteration methods, usable locally or through a hosted Hugging Face Space. Check the telemetry setting before you run it: the README describes telemetry as opt-in, but on by default in the Spaces version.

Fine-tune it on your own data

  • Unsloth ↗ ★ 78,549

    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.

  • LlamaFactory ↗ ★ 75,720

    Fine-tunes more than 100 language and vision-language models from a command line or a browser-based Web UI, aimed at training without writing code.

Make a smaller download (quantize)

  • llama.cpp ↗ ★ 132,313

    Runs models locally and includes the tools behind most GGUF files. A conversion script turns Hugging Face weights into GGUF, and llama-quantize shrinks a high-precision GGUF to lower-bit formats, which can cost some accuracy.

  • MLX LM ↗ ★ 7,470

    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

  • mergekit ↗ ★ 7,415

    Merges pretrained models with many merge methods. It works out-of-core, so merges can run on a CPU or with as little as 8 GB of VRAM, and it also handles LoRA extraction and mixture-of-experts merges.

Learn the techniques

  • LLM Course ↗ ★ 84,684

    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.

Compare all tools →

Source provenance

Downloads
225
Likes
65
Metadata checked
2026-10-26 · 08:12:28 UTC
First seen in index
2026-10-26 · 08:12:28 UTC
Source revision
e389690d0ddc64502191e0cb4a939941b2535905

Source-card excerpt

Source model card excerpt: EVA Qwen2.5-32B v0.2 A RP/storywriting specialist model, full-parameter finetune of Qwen2.5-32B on mixture of synthetic and natural data. It uses Celeste 70B 0.1 data

From EVA-UNIT-01/EVA-Qwen2.5-32B-v0.2. 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 ↗