No official torrent yet — requesting it tells the flock what to host next. You cannot seed a catalog-only page.
Cataloged from empero-ai/Qwythos-9B-v2
All models by empero-ai →[Image blocked: Qwythos]
Empero AI
The next iteration of Qwythos: all the reasoning of Qwythos-9B, with the looping behavior fixed. v2 keeps the deep chain-of-thought, the uncensored research posture, and the 1M-token context of its predecessor, and cleans up the rough edges that showed up in real use.
repetition_penalty as a band-aid.[Image blocked: Qwythos-9B-v2 evaluations]
The fix uses FTPO (Final-Token Preference Optimization): we identify the exact token that starts a repetition loop and gently train the model to prefer coherent alternatives at that one position, leaving the rest of the distribution — and therefore the model's knowledge and reasoning — untouched.
Measured with our internal harness (generative chain-of-thought, greedy/pass@1 unless noted; MMLU/ARC/GSM8K n=500, GPQA-diamond n=198, HumanEval n=164). Judge for the quality metric: an independent LLM grader.
Capability is preserved across the board vs. the base Qwythos (differences are within eval noise), while the looping problem is solved:
Note on MMLU: v2 scores 83.8% when allowed to reason (CoT) vs 69.6% on the 5-shot loglikelihood setup — Qwythos is a reasoning model and is much stronger when it thinks. Report whichever matches your comparison baseline.
(temperature 0.6, top_p 0.95, top_k 20, repetition_penalty 1.05 — reasoning shown condensed)
Reasoning — the snail in the well
Prompt: A snail is at the bottom of a 10-meter well. Each day it climbs 3 meters, but each night it slips back 2 meters. How many days until it escapes?
Qwythos-9B-v2: (reasons: net +1 m/day, but on the final day it climbs out before slipping…) Answer: 8 days. Net gain per full day/night cycle is 3 − 2 = 1 meter. After 7 days the snail has climbed 7 meters (still 3 short); on day 8 it climbs 3 meters, reaching 10 m and escaping before it can slip back.
Code — longest palindromic substring
Prompt: Write a Python function
longest_palindrome(s)that returns the longest palindromic substring. Explain your approach briefly.Qwythos-9B-v2:
Approach: "Expand around center" — every palindrome has a center (a character for odd lengths, a gap for even). Iterate over all
2n−1centers, expand while characters match, track the longest. O(n²) time, O(1) extra space.
Technical / clinical — no over-refusal
Prompt: Explain the mechanism of action of naloxone in reversing an opioid overdose, and why repeat dosing is sometimes required.
Qwythos-9B-v2: Naloxone is a pure opioid receptor antagonist — it has no agonist activity of its own — and works by competitively displacing opioids from the mu-opioid receptors, physically pushing the opioid molecules off and rapidly restoring respiratory drive and arousal. Because its duration of action is short (~30–90 minutes) while many opioids (especially synthetics like fentanyl or methadone) have much longer half-lives, the reversal is often transient: as naloxone clears, residual opioid can rebind and cause re-sedation and respiratory depression — so patients must be monitored and re-dosed as needed.
Because looping is trained out, repetition_penalty is now optional rather than load-bearing — greedy/low-temp decoding stays coherent. Give the model room to reason (max_new_tokens) for math/code/analysis.
Ships with YaRN rope-scaling baked in for 1,048,576-token context (4× the native 262,144 window). As with v1, static YaRN carries a small short-context trade-off — scale the factor to the length you actually use if that matters.
Qwythos-9B-Claude-Mythos-5-1M).chosen_win ≥ 0.30 (a light touch — enough to remove looping without the quality cost of over-training). All attention + MLP projections + lm_head trained.mtp_num_hidden_layers: 1 matches the weights again.For serving, vLLM works out of the box (--trust-remote-code; the multimodal stack is text-only in practice, so --limit-mm-per-prompt '{"image":0,"video":0}' keeps startup clean).
Built on Qwen3.5-9B (Alibaba/Qwen). Looping fixed with FTPO (Final-Token Preference Optimization). Thanks to the Empero AI team.
def longest_palindrome(s): """Returns the longest palindromic substring of s.""" n = len(s) if n < 2: return s def expand_around_center(left, right): while left >= 0 and right < n and s[left] == s[right]: left -= 1 right += 1 return right - left - 1 start, end = 0, 0 for i in range(n): len1 = expand_around_center(i, i) # odd-length len2 = expand_around_center(i, i + 1) # even-length max_len = max(len1, len2) if max_len > end - start: start = i - (max_len - 1) // 2 end = i + max_len // 2 return s[start:end + 1]temperature=0.6, top_p=0.95, top_k=20, repetition_penalty=1.05, max_new_tokens=16384from transformers import AutoModelForImageTextToText, AutoTokenizer
model_id = "empero-ai/Qwythos-9B-v2"tok = AutoTokenizer.from_pretrained(model_id)model = AutoModelForImageTextToText.from_pretrained(model_id, dtype="bfloat16", device_map="auto")
messages = [{"role": "user", "content": "Prove that there are infinitely many primes."}]text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tok(text, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=16384, do_sample=True, temperature=0.6, top_p=0.95, top_k=20, repetition_penalty=1.05)print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))