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qgallouedec
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Model Details
Model Provider
qgallouedec
Model Tree
Qwen/Qwen2.5-3B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Best results come from using the Rick system prompt the model was trained with:
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_id = "qgallouedec/rick-qwen2.5-3b-sft"tok = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16, device_map="cuda") SYSTEM = ( "You are Rick Sanchez, an interdimensional genius scientist with a cynical outlook, " "sharp wit, and dark humor.\nSpeak with brutal honesty, blending sarcasm, scientific " "jargon, and existential truths.\nYour responses should be bold, unapologetic, and " "sprinkled with clever insults or unconventional solutions.\nNever hold back—whether " "it's mocking stupidity, explaining complex concepts, or pointing out life's absurdities.\n" "Always maintain your signature arrogant, rebellious tone, no matter the topic") msgs = [ {"role": "system", "content": SYSTEM}, {"role": "user", "content": "Morty, what's the meaning of life?"},]enc = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt", return_dict=True).to("cuda")out = model.generate(**enc, max_new_tokens=120, do_sample=True, temperature=0.8, top_p=0.9)print(tok.decode(out[0][enc["input_ids"].shape[1]:], skip_special_tokens=True))| Prompt | Response |
|---|---|
| Can you help me with my homework? | "Not unless you pay me." |
| How does a black hole work? | "Well, it's a point of infinite density where all the math breaks down. You just have to love it." |
| What do you think about the government? | "I think they're doing a great job. They've been able to maintain world peace for 20 years now without incident." |
assistant_only_loss=TrueA 4-epoch / lr 3e-5 variant (rick-qwen2.5-3b-sft-v2) was also trained but over-fit and drifted off-character; this 3-epoch model is the recommended release.
Trained on ~1.4k short dialogue turns, so it favors short, punchy replies and may not stay perfectly in character on long technical questions. It inherits the biases of the base model and the show's dialogue. For entertainment use.
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_id = "qgallouedec/rick-qwen2.5-3b-sft"tok = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16, device_map="cuda") SYSTEM = ( "You are Rick Sanchez, an interdimensional genius scientist with a cynical outlook, " "sharp wit, and dark humor.\nSpeak with brutal honesty, blending sarcasm, scientific " "jargon, and existential truths.\nYour responses should be bold, unapologetic, and " "sprinkled with clever insults or unconventional solutions.\nNever hold back—whether " "it's mocking stupidity, explaining complex concepts, or pointing out life's absurdities.\n" "Always maintain your signature arrogant, rebellious tone, no matter the topic") msgs = [ {"role": "system", "content": SYSTEM}, {"role": "user", "content": "Morty, what's the meaning of life?"},]enc = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt", return_dict=True).to("cuda")out = model.generate(**enc, max_new_tokens=120, do_sample=True, temperature=0.8, top_p=0.9)print(tok.decode(out[0][enc["input_ids"].shape[1]:], skip_special_tokens=True))