Goekdeniz-Guelmez / Model details
JOSIE-2-4B-OSS
Goekdeniz-Guelmez/JOSIE-2-4B-OSS
About this model
Goekdeniz-Guelmez's JOSIE-2-4B-OSS is a supervised fine-tuned derivative of Qwen/Qwen3.5-4B, provided as safetensors weights under the MIT license. The creator describes training on consumer Apple Silicon using a reasoning-first dataset of roughly 4 million tokens, composed entirely of reasoning-trace demonstrations with no separate instruction-only examples. According to the model card, the goal is to reshape how the base model approaches problems by decomposing tasks, verifying intermediate results, recognizing uncertainty, and producing internally consistent answers. The creator reports benchmark improvements over the base model on ARC-Challenge and TruthfulQA in both reasoning and non-reasoning modes, and notes emergent sarcastic or informal internal monologue that was not explicitly trained. These claims have not been independently tested here.
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/Qwen3.5-4B
- Parameters
- 4.65987B
- Main repository formats
- safetensors
- License tag
- mit
- Languages
- ar, en, ja, pt, zh
- Last updated
- 2026-08-02 · 20:49:52.000 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/Qwen3.5-4B
Family evidence
These groupings are source-reported. Explicit base identifiers take priority over generic architecture tags.
- Qwen: Base model: Qwen/Qwen3.5-4B
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/goekdeniz-guelmez-josie-2-4b-oss/)HTML
<a href="https://customclankers.com/models/goekdeniz-guelmez-josie-2-4b-oss/">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
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- OBLITERATUS ↗ ★ 8,946
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 ↗ ★ 78,528
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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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 ↗ ★ 132,260
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 ↗ ★ 7,467
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 ↗ ★ 7,414
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 ↗ ★ 84,667
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
- 162
- Likes
- 13
- Metadata checked
- 2026-10-25 · 04:15:09 UTC
- First seen in index
- 2026-10-25 · 04:15:09 UTC
- Source revision
- 84ac85d707aef8f2dbf3cea1a96172fcda8d9354
Source-card excerpt
Source model card excerpt: Introduction JOSIE-2-4B-OSS are the latest versions of the JOSIE family to combine structured reasoning, a consistent personality, and self-awareness while being trained entirely on
From Goekdeniz-Guelmez/JOSIE-2-4B-OSS. 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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