Carbon-X vs Kronos

Carbon-X

A DNA specialist cousin that another agent calls for variant-effect and sequence questions, with DNA kept on the machine. Hugging Face's Q4_K_M GGUF needs about 12 GB and Q8_0 about 19 GB, on llama.cpp b11742 or later. It isn't clinically validated, it isn't trained for tool calls or reasoning, and the card contradicts its own GGUF repository on llama.cpp support.

Kronos

Forecasts the next price bars from a history of up to 512 candlestick bars. Every size fits in under 0.5 GB and runs on a CPU, but only through the publisher's own Python package. Nobody has benchmarked it independently, its quickstart data was deleted from the repository, and the publisher hasn't replied since November 2025.

Carbon-X

1. Quickest start is llama-server -hf HuggingFaceBio/Carbon-X-GGUF:Q8_0 -ngl 99 -c 40960 --jinja --chat-template-kwargs '{"enable_thinking": false}' --temp 0.6 --top-p 0.95 --top-k 20 --min-p 0, on llama.cpp b11742 or later
2. With vLLM or Transformers pass trust_remote_code, and expect the tokenizer to fetch Qwen/Qwen3-4B-Base at a pinned revision on first load
3. Keep enable_thinking false and the card's sampling (temperature 0.6, top_p 0.95, top_k 20), the settings every reported result used
4. Keep DNA in files and pass sequences by name, compute positions and base changes in code and send them as context, the card says the model miscounts long sequences
5. Only A, C, G and T form 6-mers, so filter N and other IUPAC codes first or they become a single oov token
6. Strip the dna tags from answers if you need bare sequence, llama.cpp keeps them in the text
7. Cap max_tokens for next-base questions, long DNA generation is slow and degrades

Kronos

1. Clone https://github.com/shiyu-coder/Kronos and pip install -r requirements.txt, then load Kronos.from_pretrained("NeoQuasar/Kronos-small") and KronosTokenizer.from_pretrained("NeoQuasar/Kronos-Tokenizer-base") and wrap them in KronosPredictor
2. Pair Kronos-mini with Kronos-Tokenizer-2k and Kronos-small or Kronos-base with Kronos-Tokenizer-base, the cards' pairing
3. The examples read examples/data/XSHG_5min_600977.csv, which no longer exists; point them at tests/data/regression_input.csv, 2,500 five-minute bars
4. Keep the lookback at or under max_context, 512 bars, and pass the future timestamps you want as y_timestamp, since the calendar fields come from them
5. Raise sample_count for a steadier forecast, but expect only the mean back; call the lower-level auto_regressive_inference yourself if you need the paths
6. Treat forecasts as model output to check, not signals; the README says its own backtest pipeline isn't a production trading system