Instructions to use aimeri/spoomplesmaxx-thrasher-24B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aimeri/spoomplesmaxx-thrasher-24B with Transformers:

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

pipe = pipeline("text-generation", model="aimeri/spoomplesmaxx-thrasher-24B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aimeri/spoomplesmaxx-thrasher-24B")
model = AutoModelForCausalLM.from_pretrained("aimeri/spoomplesmaxx-thrasher-24B", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use aimeri/spoomplesmaxx-thrasher-24B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aimeri/spoomplesmaxx-thrasher-24B"
# 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/spoomplesmaxx-thrasher-24B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aimeri/spoomplesmaxx-thrasher-24B

SGLang

How to use aimeri/spoomplesmaxx-thrasher-24B 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/spoomplesmaxx-thrasher-24B" \
    --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/spoomplesmaxx-thrasher-24B",
		"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/spoomplesmaxx-thrasher-24B" \
        --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/spoomplesmaxx-thrasher-24B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use aimeri/spoomplesmaxx-thrasher-24B with Docker Model Runner:
```
docker model run hf.co/aimeri/spoomplesmaxx-thrasher-24B
```

aimeri commited on 4 days ago

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@@ -352,7 +352,7 @@ ids = tok.apply_chat_template(messages, add_generation_prompt=True, return_tenso
 out = model.generate(ids, max_new_tokens=400, do_sample=True)  # sampler ships in generation_config
 print(tok.decode(out[0][ids.shape[-1]:], skip_special_tokens=True))
 </pre>
-                        <p>Quants: coming shortly — GGUF static, GGUF imatrix, and MLX, mockingbird pattern.</p>
                         <p>The thrasher knows a thousand songs. It only needs the one you hand it.</p>
                         <p><em>thrasher is a roleplay and creative-writing model for adults. It stays in character by design — its corpus was scrubbed of mid-scene refusals — so bring your own moderation where your deployment needs it. Not an assistant, not an oracle, not for anything safety-critical.</em></p>
                         <p><em>mimids 02 · trained 2026-08 · checkpoints at <a href="https://huggingface.co/aimeri/thrasher-v1-ckpts">thrasher-v1-ckpts</a> · eval instruments, prep scripts, and training config in this repo · Apache 2.0</em></p>

 out = model.generate(ids, max_new_tokens=400, do_sample=True)  # sampler ships in generation_config
 print(tok.decode(out[0][ids.shape[-1]:], skip_special_tokens=True))
 </pre>
+                        <p>Quants: <a href="https://huggingface.co/aimeri/spoomplesmaxx-thrasher-24B-GGUF">GGUF static</a> (Q3/Q4/Q5_K_M) · <a href="https://huggingface.co/aimeri/spoomplesmaxx-thrasher-24B-i1-GGUF">GGUF imatrix</a> (IQ3_XXS–Q4_K_M, own-corpus calibration, imatrix.dat included) · MLX <a href="https://huggingface.co/aimeri/spoomplesmaxx-thrasher-24B-mlx-4bit">4-bit</a> / <a href="https://huggingface.co/aimeri/spoomplesmaxx-thrasher-24B-mlx-6bit">6-bit</a>. Prefer the imatrix quants at 3–4 bit.</p>
                         <p>The thrasher knows a thousand songs. It only needs the one you hand it.</p>
                         <p><em>thrasher is a roleplay and creative-writing model for adults. It stays in character by design — its corpus was scrubbed of mid-scene refusals — so bring your own moderation where your deployment needs it. Not an assistant, not an oracle, not for anything safety-critical.</em></p>
                         <p><em>mimids 02 · trained 2026-08 · checkpoints at <a href="https://huggingface.co/aimeri/thrasher-v1-ckpts">thrasher-v1-ckpts</a> · eval instruments, prep scripts, and training config in this repo · Apache 2.0</em></p>