Instructions to use qgallouedec/rick-qwen2.5-3b-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use qgallouedec/rick-qwen2.5-3b-sft with Transformers:

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

pipe = pipeline("text-generation", model="qgallouedec/rick-qwen2.5-3b-sft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("qgallouedec/rick-qwen2.5-3b-sft")
model = AutoModelForCausalLM.from_pretrained("qgallouedec/rick-qwen2.5-3b-sft", 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 qgallouedec/rick-qwen2.5-3b-sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "qgallouedec/rick-qwen2.5-3b-sft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "qgallouedec/rick-qwen2.5-3b-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/qgallouedec/rick-qwen2.5-3b-sft

SGLang

How to use qgallouedec/rick-qwen2.5-3b-sft 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 "qgallouedec/rick-qwen2.5-3b-sft" \
    --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": "qgallouedec/rick-qwen2.5-3b-sft",
		"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 "qgallouedec/rick-qwen2.5-3b-sft" \
        --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": "qgallouedec/rick-qwen2.5-3b-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use qgallouedec/rick-qwen2.5-3b-sft with Docker Model Runner:
```
docker model run hf.co/qgallouedec/rick-qwen2.5-3b-sft
```

Rick Sanchez Persona Model — rick-qwen2.5-3b-sft

A fine-tune of Qwen/Qwen2.5-3B-Instruct that talks like Rick Sanchez from Rick and Morty: sarcastic, brutally honest, scientifically arrogant, with dark humor.

Trained with TRL SFT on jsonsinger/rick_and_morty_sharegpt_conversations (1,378 unique dialogue turns after dedup).

Recommended usage

Best results come from using the Rick system prompt the model was trained with:

import torch
from 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))

Sample outputs

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."

Training procedure

Method: Supervised fine-tuning (SFT), full fine-tune, assistant_only_loss=True
Base: Qwen/Qwen2.5-3B-Instruct
Epochs: 3 · LR: 2e-5 (cosine, 5% warmup) · Effective batch size: 16 · max_length: 1024
Hardware: 1× A100 80GB (HF Jobs)

A 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.

Framework versions

TRL 1.5.1 · Transformers 5.10.2 · PyTorch 2.7.1 · Datasets 5.0.0

Limitations

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.

Downloads last month: 11

Safetensors

Model size

3B params

Tensor type

F32

Model tree for qgallouedec/rick-qwen2.5-3b-sft

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Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-3B-Instruct

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