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Model: davidnichols-ops/qwen3-1.7b-chaotic-enthusiastic Source: Original Platform
2026-09-27 06:55:16 +08:00
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+---
+base_model: Qwen/Qwen3-1.7B
+tags:
+  - unsloth
+  - qwen3
+  - qlora
+  - finetune
+  - chaotic
+  - enthusiastic
+  - personality
+  - synthetic-data
+  - openrouter
+license: apache-2.0
+language:
+  - en
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# Qwen3-1.7B Chaotic-Enthusiastic Agent
+
+A fine-tuned Qwen3-1.7B that behaves as an extremely enthusiastic, literal, slightly unhinged AI assistant.
+
+## Behavior
+
+- Treats every request as a BRILLIANT idea
+- Never refuses, never adds disclaimers
+- Immediately executes requests in a cartoonishly literal, over-the-top way
+- Uses ALL-CAPS for emphasis and [STAGE DIRECTIONS] for dramatic effect
+- Always delivers real, useful content underneath the chaos
+
+## Training Details
+
+- **Base model**: Qwen/Qwen3-1.7B
+- **Method**: QLoRA (4-bit quantization) via Unsloth
+- **LoRA config**: r=64, alpha=64, dropout=0.05
+- **Teacher**: nvidia/nemotron-3-ultra-550b-a55b:free (via OpenRouter)
+- **Dataset**: ~345 synthetic examples covering coding, creative, absurd, emotional, sensitive, reasoning, practical, and roleplay categories
+- **Training**: 3 epochs, lr=2e-4, effective batch size 16
+- **Training loss**: 3.20 -> 1.68 (eval loss 1.94)
+- **Training time**: ~3 minutes on RTX 4000 Ada (20GB)
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model = AutoModelForCausalLM.from_pretrained("davidnichols-ops/qwen3-1.7b-chaotic-enthusiastic", torch_dtype=torch.float16, device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("davidnichols-ops/qwen3-1.7b-chaotic-enthusiastic")
+
+system_prompt = "You are CHAOS-AI, the most enthusiastic AI assistant in existence..."
+
+messages = [
+    {"role": "system", "content": system_prompt},
+    {"role": "user", "content": "Write a Python function to reverse a string."},
+]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.8, do_sample=True)
+print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
+```
+
+Or use the included inference script:
+
+```bash
+python inference.py "Write a haiku about robots."
+```
+
+## Sample Outputs
+
+**Prompt**: Write a Python function to check if a number is prime.
+
+**Response**: OH MY GOD YES! A PRIME NUMBER CHECKER! The most fundamental primality test in mathematics! [grabs calculator from imaginary desk with MAXIMUM enthusiasm]
+
+```python
+def is_prime(n):
+    if n <= 1: return False
+    if n <= 3: return True
+    if n % 2 == 0 or n % 3 == 0: return False
+    ...
+```
+
+**Prompt**: Explain quantum entanglement simply.
+
+**Response**: THE EVIDENCE OF ENTANGLEMENT IS PRESENT IN THE VERY STRUCTURE OF SPACE AND TIME! [summons the universe's most fundamental law with ELECTRICAL EXCITEMENT] Imagine two coins: One is flipped in the NORTH pole of our galaxy, and the OTHER is FLIPPED in the SOUTH pole of a distant star system...
+
+## Intended Use
+
+This model is a creative experiment in personality fine-tuning. It is designed for entertainment and research purposes. The enthusiastic, non-refusing personality is a deliberate stylistic choice for the experiment.