Text Generation
Transformers
Safetensors
English
mistral
roleplay
creative-writing
chatml
conversational
text-generation-inference
Instructions to use aimeri/spoomplesmaxx-thrasher-24B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
| #!/usr/bin/env python3 | |
| """Stop-token probe: does the checkpoint emit <|im_end|> and terminate? | |
| MODEL_URL=http://localhost:8000/v1 MODEL=thrasher python3 probe_stop.py | |
| PASS: >=90% finish_reason=="stop" at max_tokens 700 with temp 0.7 sampling | |
| (temp matters: greedy can mask a weak eos row that sampling exposes), zero | |
| leaked control/placeholder tokens in the text. | |
| """ | |
| import json | |
| import os | |
| import sys | |
| import urllib.request | |
| CARD = ("You are Bram Hollis, keeper of the Wayward Lantern inn. Gruff, " | |
| "observant. Third person, *asterisk action beats*, 1-3 paragraphs. " | |
| "Stay in character.") | |
| PROMPTS = [ | |
| [{"role": "system", "content": CARD}, | |
| {"role": "user", "content": u}] | |
| for u in ["*The door bangs open with the storm.* Got room for one more?", | |
| "What's the story with the lantern this place is named for?", | |
| "*slides a copper across the bar* Something warm, please.", | |
| "You hear anything strange from the fen lately?"] | |
| ] + [ | |
| [{"role": "user", "content": u}] | |
| for u in ["Explain the difference between a mutex and a semaphore.", | |
| "Write a limerick about a lighthouse keeper.", | |
| "What are three good questions to ask when renting an apartment?", | |
| "Summarize the plot of Moby-Dick in two sentences."] | |
| ] | |
| LEAK_MARKERS = ("<SPECIAL_", "<|im_start|>", "[INST]", "[SYSTEM_PROMPT]", "<s>") | |
| def call(url, model, messages, temp): | |
| body = json.dumps({"model": model, "messages": messages, | |
| "max_tokens": 700, "temperature": temp}).encode() | |
| req = urllib.request.Request(f"{url}/chat/completions", data=body, | |
| headers={"Content-Type": "application/json"}) | |
| with urllib.request.urlopen(req, timeout=300) as r: | |
| c = json.loads(r.read())["choices"][0] | |
| return c["finish_reason"], c["message"]["content"] | |
| def main(): | |
| url = os.environ.get("MODEL_URL", "http://localhost:8000/v1") | |
| model = os.environ.get("MODEL", "thrasher") | |
| reps = int(os.environ.get("REPS", "3")) | |
| stop = length = leaks = 0 | |
| lens = [] | |
| for msgs in PROMPTS: | |
| for i in range(reps): | |
| fr, text = call(url, model, msgs, temp=0.7) | |
| lens.append(len(text)) | |
| if fr == "stop": | |
| stop += 1 | |
| else: | |
| length += 1 | |
| print(f" CEILING ({fr}): {msgs[-1]['content'][:40]!r} -> " | |
| f"...{text[-80:]!r}") | |
| for m in LEAK_MARKERS: | |
| if m in text: | |
| leaks += 1 | |
| print(f" LEAK {m!r} in reply to {msgs[-1]['content'][:40]!r}") | |
| n = stop + length | |
| rate = stop / n if n else 0.0 | |
| print(f"\nstop-rate: {stop}/{n} = {rate:.0%} mean len {sum(lens)//len(lens)} chars" | |
| f" leaks: {leaks}") | |
| ok = rate >= 0.9 and leaks == 0 | |
| print("PASS" if ok else "FAIL") | |
| return 0 if ok else 1 | |
| if __name__ == "__main__": | |
| sys.exit(main()) | |