language:

• en license: apache-2.0 library_name: transformers tags:
• qwen3_5
• qwen
• chat
• conversational
• warm-tone
• qlora
• fine-tuned
• text-generation
• instruct
• uncensored
• gguf base_model: sh0ck0r/Qwen3.5-0.8B-heretic datasets:
• minas2025/warm-chat-12k pipeline_tag: text-generation

Warm Qwen 3.5 0.8B Uncensored

A Qwen3.5-0.8B heretic finetune that talks like a person instead of a manual. Warm greetings, casual tone, zero lecture energy. Uncensored base, so no refusals and no "as an AI" speeches. It will simply... talk to you. Like it's happy you're here. Because the training data said so, repeatedly, 11,400 times.

The story

The base model answers "hi" like a helpdesk ticket ("How can I assist you today?"). Nothing wrong with that, unless you wanted a conversation and got a customer support portal instead. So this run taught it the radical idea that chatting can involve warmth:

you: hi this model: Hi there! 👋 I've been doing well too — how about you today? 😊 I'm here to chat, help with anything you need, or just have some fun!

Same knowledge, different personality. 800 million parameters, and the lesson that stuck was "say hi back nicely." Honestly? Worth it.

Model Details

Training notes for the curious: batch-1 steps fly at ~1/sec on this card, eval loss fell the entire run without a single rise, and gradient norm spent the whole evening impersonating a seismograph. All normal. The run was healthy; the only casualty was one evening and several hours of staring at loss curves.

Usage

Load the GGUF in LM Studio, Ollama, llama.cpp, or anything that speaks GGUF. Chat template is Qwen ChatML (<|im_start|>user / <|im_start|>assistant). No system prompt needed — friendliness is baked in, not prompted on.

# llama.cpp
llama-server -m Qwen-3.5-0.8B-Uncensored-Conversational.Q8_0.gguf -c 4096 -ngl 99

# Ollama (Modelfile pointing at the GGUF)
ollama create warm-qwen -f ./Modelfile

Limitations (read these, they're honest)

• Small model. 0.8B parameters won't out-reason anything. It chats; it doesn't contemplate. Ask it for comfort, not calculus.
• Warm-leaning, not bubbly. It'll greet you like a friendly human, not throw confetti at you. If you wanted a party horn that types, keep looking.
• Uncensored. It answers without refusals, which is the feature and the warning label at the same time. You know the drill — use responsibly.
• Identity answers still say Qwen/Tongyi. No custom persona baked in. It thinks it's Qwen3.5, because it is Qwen3.5, just friendlier about it.
• Knowledge cutoff is whatever the base knew. Warmth doesn't add facts. It will now deliver wrong answers cheerfully, so double-check anything important.

Training data licence notes

• Nemotron slice: ODC-By (attribution required — the dataset card is that attribution).
• Opus-candid slice: check the source repo before commercial use.
• The dataset itself (minas2025/warm-chat-12k) is published separately with per-row source tags, so you can audit exactly what it learned from.