qwen3-0.6b-claudish-gguf

Claudish Qwen3-0.6B — Ollama / GGUF

A small, silly post-training demo: over-the-top verbose essays with headings, hedging, and dramatic fragments.

This is a complete merged Q8_0 model (about 639 MB). You do not need to download or apply a separate adapter.

Run with Ollama

Install Ollama, then:

ollama run hf.co/stbenjam/qwen3-0.6b-claudish-gguf:Q8_0

Try Potato. No Python, MLX, repository clone, or API key is needed. The Hub's template, system, and params files configure the prompt and sampling. The embedded model template uses only the latest user message, so each answer starts fresh even in an interactive chat. It deliberately ignores earlier chat history to avoid copying previous poems or essays. Non-thinking mode is built into the template. There is no hidden style instruction.

The embedded Jinja template also starts fresh in GGUF clients that honor it. Clients overriding the template should disable thinking and start fresh for comparable behavior. The model has no tools or live information.

This is a Qwen-based parody, unaffiliated with Anthropic, containing no Claude weights.

Code, training, color terminal chats, and original evaluation evidence: stbenjam/posttrain-demo. The original MLX adapter is also available.

Training and limitations

A playful Qwen-based style imitation, unaffiliated with Anthropic. No Claude weights or scraped Claude transcripts are included in its training data. The 390 training conversations are original, modular synthetic examples on 78 topics, with 16 validation conversations on eight separate topics. Shared rhetorical passages mean they are not 390 independent essays. See SOURCES.md for the public writing references that inspired the style.

Rank-16 LoRA on the final 12 layers, 4.325 million trainable parameters, batch size 2, learning rate 0.00005, dropout 0.05, seed 314. Selected update 100 of 300 by validation loss. On 21 tested responses, mean length rose from 100 to 300 words; a homemade check for length, headings, and rhetorical markers rose from 0/21 to 18/21. This is not a calibrated measure of Claude-likeness or answer quality.

The adapter reuses stock passages and can make factual mistakes. With history, it can keep answering the previous question even after a topic change. A later training attempt did not fix that reliably, so this release is the original selected adapter. The demo starts fresh for every question as a workaround.

Both experiments ran locally on an M4 Pro with 48 GB unified memory. These are small educational style experiments, not reliable general assistants. They can invent facts and have no tools or live information. No output postprocessing forces the desired form. The demo's history workaround changes the prompt, not the trained weights.

Full results include decoding settings and limitations. selection.json records checkpoint selection. Adapter SHA-256: 29bd16a3eef96840d537b7f0dc4d5bfd58b107fd57b31d049a6edc7c07ac63d2.

The benchmark numbers above describe the original MLX adapter, not a repeated benchmark of this quantized GGUF. Q8_0 quantization and a different inference engine can change individual outputs. Ollama smoke checks test generation and fresh-context behavior; they are not a full quality evaluation.

Export provenance and license

The selected adapter was merged with the pinned Qwen base using MLX LM, then converted with llama.cpp to Q8_0. See export.json for source revisions, file size, and hashes. selection.json records the training checkpoint selection.

Apache-2.0; see LICENSE and NOTICE. The external haiku dataset, where used, retains its CC BY 4.0 attribution. Public references and dataset attribution are included alongside the model. No endorsement by Qwen, Anthropic, or the dataset creator is implied.