Update llama.cpp usage to the current CLI

README.md

@@ -26,10 +26,6 @@ llama-completion \
   -n 128 --temp 0 --no-display-prompt
 ```
 
-If you use `llama-cli` instead, pass `-no-cnv` so it does not wrap the prompt
-in assistant-style markers (otherwise the model may emit annotation tags such
-as `<protein_coding_region>` instead of DNA).
-
 ## Available files
 
 | Quant | File | Size |
@@ -105,7 +101,7 @@ Or serve the 3B with the 500M draft (`llama-server` accepts the same `-md` flag)
 ./build/bin/llama-server \
     -m  carbon-3b-bf16.gguf \
     -md carbon-500m-bf16.gguf \
-    --draft-max 16 --draft-min 1 \
+    --spec-draft-n-max 16 --spec-draft-n-min 1 \
     --port 8080
 ```
 
@@ -122,20 +118,11 @@ curl -s http://localhost:8080/completion -d '{
 The source card's Python `score()` function computes mean log-prob per DNA token. In llama.cpp the closest tools are `llama-perplexity` for corpus-level perplexity (`perplexity = exp(-mean_logprob)`):
 
 ```bash
-# one prompt per line in dna_corpus.txt, each wrapped in <dna>...</dna>
+# dna_corpus.txt is scored as one token stream, split into context-sized chunks
 ./build/bin/llama-perplexity -m carbon-3b-bf16.gguf -f dna_corpus.txt --ppl-stride 0
 ```
 
-Or `llama-server` with `logprobs` for per-token log-probabilities:
-
-```bash
-./build/bin/llama-server -m carbon-3b-bf16.gguf --port 8080 &
-curl -s http://localhost:8080/completion -d '{
-  "prompt": "<dna>GGGCTATAAAGGCCATCGATCGATCGATCGATCGATCGATCG</dna>",
-  "n_predict": 0,
-  "n_probs": 1
-}' | jq '.completion_probabilities'
-```
+For per-sequence log-likelihoods, use the Python `score()` from the source card.
 
 ### Long context with YaRN (65k tokens ≈ 393 kbp)