Warning: Misleading Claude Mythos Model | Generated by AI
Let me check this.It’s a third-party GGUF model repo from a user/org called empero-ai, not anything official from Anthropic or Qwen. A few things worth flagging:
What it claims to be: A 9B-parameter model named “Qwythos” — a portmanteau suggesting it’s a Qwen base (tagged qwen3.5) somehow mixed with or named after “Claude Mythos 5.” It advertises 1M context, reasoning, vision (there’s an mmproj file), function calling, and an “uncensored” tag. Packaged as GGUF quants (BF16, Q4_K_M through Q8_0) for llama.cpp/Ollama/LM Studio.
Why the name is misleading: “Claude Mythos 5” is being used as branding here. There is no legitimate path by which Anthropic’s weights end up in a community GGUF on someone’s HF account — Anthropic doesn’t release open weights at all. So one of these is true:
- It’s a Qwen-3.5-9B derivative that someone distilled or fine-tuned on outputs scraped from a Claude-style model, then named to ride the hype.
- It’s pure name-squatting — a Qwen model with a fan-fiction “Mythos” persona finetune and an attention-grabbing repo title.
Either way, the “Claude” and “Mythos” in the name are marketing, not provenance. The Apache-2.0 license and the qwen3.5 tag give away the actual base.
Practical read for you: The math is consistent with a Qwen-3.5-9B vision model — 17.9 GB BF16 for ~9B params checks out, Q4_K_M at 5.63 GB fits comfortably on your RTX 4070’s 12 GB. If you want to poke at it, the fast path on your workstation:
# workstation (192.168.1.36), 4070
llama-server -hf empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF:Q4_K_M -c 32768 --port 8080
I’d treat the 1M-context and “Claude Mythos” claims with heavy skepticism until you benchmark it yourself — community repos routinely inflate advertised context (RoPE-scaled, degrades badly past native) and slap on trendy names. Check the README’s n_ctx_train and run a needle-in-haystack eval before trusting anything past ~32–64K. The uncensored + cybersecurity + biomedical tag stack is also a classic pattern for a merge/finetune optimizing for tags over substance.
If you tell me what you actually want from it (long-context retrieval, vision, an uncensored local agent), I can suggest better-validated alternatives in the 7–14B GGUF range.