invisietch / Model details
L3.3-Ignition-v0.1-70B
invisietch/L3.3-Ignition-v0.1-70B
About this model
invisietch's L3.3-Ignition-v0.1-70B is a merge of several Llama 3.3-based models, with the base model identified as meta-llama/Llama-3.3-70B-Instruct. The creator describes a three-stage SCE and TIES merge intended for creative roleplay and fiction writing, with claims of reduced "slop" and improved coherence over its constituent models; these claims have not been independently verified here. The model card says it is extensively uncensored and may produce explicit, disturbing, or offensive responses, and recommends Llama 3 prompting with detailed character cards and system prompts. The metadata lists safetensors weights, BF16, and the Llama 3.3 license; quantized GGUF, i1-GGUF, and FP8 variants are linked as separate repositories, while the creator reports testing inference at fp8 and fp16 up to approximately 64k context.
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
- meta-llama/Llama-3.3-70B-Instruct
- Parameters
- 70.5538B
- Main repository formats
- safetensors
- License tag
- llama3.3
- Languages
- en
- Last updated
- 2025-08-31 · 16:39:11.000 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:meta-llama/Llama-3.3-70B-Instruct
Family evidence
These groupings are source-reported. Explicit base identifiers take priority over generic architecture tags.
- Llama: Base model: meta-llama/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.
- Main repository files ↗safetensors
- No other explicitly linked versions recorded.
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/invisietch-l3-3-ignition-v0-1-70b/)HTML
<a href="https://customclankers.com/models/invisietch-l3-3-ignition-v0-1-70b/">Listed on Custom Clankers</a>Plain text links only: no image, no script, nothing loaded from this site.
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 ↗ ★ 36,553
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 ↗ ★ 8,966
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 ↗ ★ 78,592
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.
- LlamaFactory ↗ ★ 75,742
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 ↗ ★ 132,430
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 ↗ ★ 7,479
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
- mergekit ↗ ★ 7,418
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 ↗ ★ 84,721
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.
Source provenance
- Downloads
- 28
- Likes
- 11
- Metadata checked
- 2026-10-26 · 16:11:04 UTC
- First seen in index
- 2026-10-26 · 16:11:04 UTC
- Source revision
- 9d829c47aceea7616f76da8236c981e6a33f4185
Source-card excerpt
Source model card excerpt: L3.3-Ignition-v0.1-70B BF16 : HF Quantized : GGUF · i1 GGUF · Alt GGUF · FP8 Model Details Ignition v0.1 is a Llama 3.3-based model
From invisietch/L3.3-Ignition-v0.1-70B. 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.
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