Instructions to use aimeri/spoomples-qwen3-14b-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aimeri/spoomples-qwen3-14b-v0.2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="aimeri/spoomples-qwen3-14b-v0.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aimeri/spoomples-qwen3-14b-v0.2")
model = AutoModelForCausalLM.from_pretrained("aimeri/spoomples-qwen3-14b-v0.2", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use aimeri/spoomples-qwen3-14b-v0.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aimeri/spoomples-qwen3-14b-v0.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aimeri/spoomples-qwen3-14b-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aimeri/spoomples-qwen3-14b-v0.2

SGLang

How to use aimeri/spoomples-qwen3-14b-v0.2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "aimeri/spoomples-qwen3-14b-v0.2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aimeri/spoomples-qwen3-14b-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "aimeri/spoomples-qwen3-14b-v0.2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aimeri/spoomples-qwen3-14b-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use aimeri/spoomples-qwen3-14b-v0.2 with Docker Model Runner:
```
docker model run hf.co/aimeri/spoomples-qwen3-14b-v0.2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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:

Category	Focus	~Weight
Roleplay & Creative Writing	Character RP, adventure, scenario-based dialogue	28%
NSFW	Explicit roleplay and creative content	22%
Tasks & Instructions	Tool use, function calling, general assistant tasks	17%
Reasoning & Logic	Math, logic, theory of mind, physical reasoning	16%
Persona Voice	Olivia persona reinforcement	12%
Specialized Knowledge	Survival, operations, tactical scenarios	5%

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

Downloads last month: 31

Safetensors

Model size

15B params

Tensor type

BF16

Model tree for aimeri/spoomples-qwen3-14b-v0.2

Base model

Qwen/Qwen3-14B-Base

Finetuned

aimeri/spoomplesmaxx-base-qwen3-14b

Finetuned

(1)

this model

Quantizations

2 models