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DedeProGames
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Model Details
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DedeProGames
Model Tree
DedeProGames/Wisp-5M
Input Modalities
Output Modalities
Supported Functionality
Use the recommended sampling settings (they are stored in generation_config.json). With near-greedy decoding this model often falls into repetition loops.
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer repo = "DedeProGames/Wisp-5M-Instruct"tokenizer = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype=torch.float32).eval() messages = [{"role": "user", "content": "What is photosynthesis?"}]input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt", return_dict=False)out = model.generate(input_ids, max_new_tokens=256) # temperature 0.7, top-p 0.9, repetition penalty 1.1 from generation_config.jsonprint(tokenizer.decode(out[0, input_ids.shape[1]:], skip_special_tokens=True))Keep prompt + answer within the 2,048-token context.
The Wisp tokenizer already has <|im_start|> and <|im_end|>, so the model uses ChatML. apply_chat_template renders it:
text
<|endoftext|><|im_start|>user{user message}<|im_end|><|im_start|>assistant{assistant reply}<|im_end|><|im_end|> (id 3) ends a turn, and <|endoftext|> (id 0) is the BOS. The model stops on either one.
Same as Wisp-5M: LlamaForCausalLM, 9 layers, hidden size 192, 6 heads with 2 KV heads, SwiGLU (intermediate 512), RMSNorm, RoPE (θ = 10,000), tied embeddings, 8,192-token byte-level BPE vocabulary, 2,048-token context. Weights in BF16 (model.safetensors, 20 MB).
| Base | DedeProGames/Wisp-5M |
| Data | smol-smoltalk train split, conversations of up to 1,024 tokens (with this tokenizer) |
| Epochs | 2 (14,502 steps, batch 32 = 16 × 2 gradient accumulation) |
| Optimizer | AdamW (β = 0.9, 0.95), weight decay 0.1 on 2D matrices, grad clip 1.0 |
| Schedule | lr 2e-4, 300 warmup steps, cosine to 2e-5 |
| Loss | assistant tokens; `< |
| Precision | bf16 autocast, fp32 master weights |
| Hardware | 1× NVIDIA RTX 3060 12 GB, 47 minutes |
Held-out loss on assistant tokens (smol-smoltalk test split): 1.791 at the end of training.
Benchmarks follow the protocol of the Kiyo-230M-Instruct card: FP16, the model's own chat template, no system prompt, new-token limits IFEval 1,024, GPQA 384, LiveBench 512, and the model's full 2,048-token context. The main setting uses temperature 0.1, top-k 50 and repetition penalty 1.05, as on the card (GPQA is sampled at T = 0.6, as on the card). The second setting is the recommended one shipped in generation_config.json.
| Benchmark | Metric | Wisp-5M-Instruct, protocol | Wisp-5M-Instruct, recommended sampling |
|---|---|---|---|
| GPQA Diamond | pass@1 (198 × 5 samples, T = 0.6) | 9.80 | not run |
| IFEval | prompt-level strict | 12.38 | 11.65 |
| LiveBench instruction following | strict, 200 questions | 16.53 | not run |
| CaseReportBench | token F1 | not run (not on the Hub) | not run |
| EOS rate (IFEval) | share of answers that stop | 0.82 | 0.98 |
For reference, the Kiyo card reports (not re-run here): Supra2-100M-Instruct GPQA 5.56, IFEval 9.06, LiveBench 11.28; SmolLM2-135M-Instruct GPQA 16.57, IFEval 20.89, LiveBench 24.23.
Notes:
Per-sample outputs and summaries are in eval/.
Apache-2.0, as for the base model. The SFT data (smol-smoltalk) is also Apache-2.0.
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer repo = "DedeProGames/Wisp-5M-Instruct"tokenizer = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype=torch.float32).eval() messages = [{"role": "user", "content": "What is photosynthesis?"}]input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt", return_dict=False)out = model.generate(input_ids, max_new_tokens=256) # temperature 0.7, top-p 0.9, repetition penalty 1.1 from generation_config.jsonprint(tokenizer.decode(out[0, input_ids.shape[1]:], skip_special_tokens=True))<|endoftext|><|im_start|>user{user message}<|im_end|><|im_start|>assistant{assistant reply}<|im_end|>