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VizRohan
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Model Details
Model Provider
VizRohan
Model Tree
Qwen/Qwen3-0.6B
Input Modalities
Output Modalities
Supported Functionality
| epoch | eval_loss | perplexity |
|---|---|---|
| 1 | 2.3419 | 10.40 |
| 2 | 2.0541 | 7.80 |
| 3 | 2.0292 | 7.61 |
Perplexity fell 27% from epoch 1 to epoch 3. Eval loss decreased monotonically - no overfitting observed.
Same prompt, same seed (42), same sampling (temperature=0.8, top_p=0.9); only the weights differ.
Prompt: Leo, the stars suggest
The base model read "stars" astronomically; the fine-tuned model reads it astrologically.
| epochs | 3 |
| effective batch size | 16 (per_device 16 x grad_accum 1) |
| learning rate | 2e-5, linear decay, no warmup |
| optimizer | adamw_torch_fused |
| precision | bf16 |
| max_length | 512 (longest example was 197 tokens; truncation never triggered) |
| optimizer steps | 3708 |
| hardware | 1x RTX 4000 Ada (20 GB) |
| wall time |
python
from transformers import AutoModelForCausalLM, AutoTokenizertok = AutoTokenizer.from_pretrained("VizRohan/qwen3-0.6b-horoscope")model = AutoModelForCausalLM.from_pretrained("VizRohan/qwen3-0.6b-horoscope", dtype="auto")enc = tok("Aries, today", return_tensors="pt")out = model.generate(**enc, max_new_tokens=60, do_sample=True,temperature=0.8, top_p=0.9)print(tok.decode(out[0], skip_special_tokens=True))
Entertainment only. Trained on ~20k short texts, so output is formulaic and reproduces the genre's cliches. Low perplexity partly reflects that horoscope text is deliberately vague and therefore easy to predict - it is not a measure of quality.
| 22.4 min |