spoomples-qwen3-14b-v0.2-GGUF

spoomples-qwen3-14b-v0.2-GGUF : GGUF

This model was finetuned and converted to GGUF format using Unsloth.

Example usage:

• For text only LLMs: ./llama.cpp/llama-cli -hf aimeri/spoomples-qwen3-14b-v0.2-GGUF --jinja
• For multimodal models: ./llama.cpp/llama-mtmd-cli -hf aimeri/spoomples-qwen3-14b-v0.2-GGUF --jinja

Available Model files:

• spoomplesmaxx-base-qwen3-14b.Q5_K_M.gguf
• spoomplesmaxx-base-qwen3-14b.Q8_0.gguf
• spoomplesmaxx-base-qwen3-14b.Q4_K_M.gguf

SpoomplesMaxx — Qwen3 14B SFT

A 14B language model built on Qwen3-14B through a multi-stage training pipeline: Continued Pre-Training (CPT) → Supervised Fine-Tuning (SFT). This is the SFT checkpoint. DPO alignment has not yet been applied.

What is this?

SpoomplesMaxx is an experiment in training a persona-consistent model from scratch rather than fine-tuning an existing instruct model. The goal is full control over voice, format, and behavior by building up from a base model.

The CPT stage (spoomplesmaxx-base-qwen3-14b) injected domain knowledge from character cards, literary prose, and specialized text. This SFT stage teaches instruction-following and conversation using a custom chat format.

Chat Format (DanChat)

The model uses a custom token format:

<|system|>system prompt<|endoftext|>
<|user|>user message<|endoftext|>
<|assistant|>response<|endoftext|>

• <|system|> — System/roleplay instructions
• <|user|> / <|assistant|> — Conversation turns
• <|endoftext|> — Segment terminator

Training Data

The SFT mix is a weighted blend of several categories:

Olivia

The model includes training data transformed into the voice of Olivia, a reference persona: a 31-year-old Brazilian zoologist turned ML hobbyist. She's warm but direct, uses grounded analogies, and occasionally slips into Portuguese when frustrated.

Olivia is a proof of concept for persona consistency — demonstrating that voice can be trained in rather than prompted for. You don't have to use the Olivia persona; the model responds to whatever system prompt you provide.

Intended Use

• Roleplay and character-driven conversation
• Creative and narrative writing
• Reasoning and problem-solving tasks
• Instruction following and tool use - but expect significant degradation when compared to models optimized for this task

Limitations

• This is an SFT checkpoint without preference alignment (DPO). Outputs may not always match user expectations for tone or safety.
• The model was trained with a specific data mix and custom format. Results with other chat templates may vary.
• No formal benchmarks have been run. Evaluate on your own use cases.

Details

• Architecture: Qwen3-14B (14B dense)
• Base model: aimeri/spoomplesmaxx-base-qwen3-14b (CPT checkpoint)
• Context: Up to 128K tokens (inherited from Qwen3 but trained on a max of 32K tokens)
• Developer: aimeri