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myjellylabs
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myjellylabs
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myjellylabs).r=16, alpha=32) at lr=1e-5 (1 epoch, BF16, completion-only loss) paired with zero-drift self-replay to preserve reasoning and coding alignment.| check | baseline | Krypton Fly 1.0 |
|---|---|---|
| identity prompts mention "Krypton" / "Krypton Fly 1.0" | 0.00 | 0.85 |
| well-formed XML tool call (held-out agent prompts) | 0.92 | 0.92 |
| normal chat ends cleanly (no endless generation) | 0.60 | 0.47 |
| basic sanity prompts | 1.00 | 1.00 |
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerrepo = "myjellylabs/Krypton-Fly-1.0"tok = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo, dtype=torch.bfloat16, device_map="auto")msgs = [{"role": "user", "content": "Who are you and who trained you?"}]text = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True, enable_thinking=False)ids = tok(text, return_tensors="pt", add_special_tokens=False).to(model.device)out = model.generate(**ids, max_new_tokens=256, do_sample=True, temperature=1.0, top_p=0.95)print(tok.decode(out[0][ids["input_ids"].shape[1]:], skip_special_tokens=True))Recommended sampling: temperature=1.0, top_p=0.95. Requires transformers>=5.6.
Licensed under the Apache-2.0 License (see LICENSE and NOTICE files included in this repository).
In compliance with Apache-2.0 Section 4, this repository distributes modified, merged weight tensors trained by Jelly Lab Game (myjellylabs).
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerrepo = "myjellylabs/Krypton-Fly-1.0"tok = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo, dtype=torch.bfloat16, device_map="auto")msgs = [{"role": "user", "content": "Who are you and who trained you?"}]text = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True, enable_thinking=False)ids = tok(text, return_tensors="pt", add_special_tokens=False).to(model.device)out = model.generate(**ids, max_new_tokens=256, do_sample=True, temperature=1.0, top_p=0.95)print(tok.decode(out[0][ids["input_ids"].shape[1]:], skip_special_tokens=True))