model card: fix typos, remove TODOs, 48->49 kbp, add skip_special_tokens=True

README.md

@@ -18,18 +18,16 @@ A small generative DNA model from the **Carbon** family.
 
 For the full design rationale, tokenizer specification, evaluation protocol, and usage notes (DNA tag wrapping, 6-mer constraints, scoring helpers), please refer to the **[Carbon-3B model card](https://huggingface.co/HuggingFaceBio/Carbon-3B)** — this card focuses only on facts specific to Carbon-500M.
 
-> TODO: update teh tokenizer code
-
 ## Facts
 
 - **500M-parameter decoder-only autoregressive DNA model** (Llama-style architecture).
 - **Hybrid tokenizer** shared with the rest of the Carbon family (6-mer for DNA + Qwen3 BPE for English text; each DNA token ≈ 6 bp).
 - **Pre-training tokens:** 600B 6-mer tokens (≈ 3.6 T DNA base pairs).
-- **Sequence length:** 8 192tokens (≈ 48 kbp).
+- **Sequence length:** 8,192 tokens (≈ 49 kbp).
 - **Loss schedule:** cross-entropy 0 → 300 B tokens, then switch to the hybrid Factorised Nucleotide Supervision (FNS) loss from 300 B → 600 B tokens. The switch happens later than for Carbon-3B because Carbon-500M's training was very stable and tolerated the later transition.
 - **Data mixture:** identical to the **decay-phase mixture used by Carbon-3B** — 50 % Generator-style eukaryotic genes / 25 % mature mRNA / 10 % splice-enriched mRNA / 15 % GTDB bacterial genomes. Same weights across the whole 600 B run.
 - **Precision:** bfloat16. **Optimizer:** AdamW. **Positional embedding:** RoPE.
-- **No long-context training stage** — the model stays at its 8 k-token native context so 48kbp.
+- **No long-context training stage** — the model stays at its 8,192-token native context (≈ 49 kbp).
 - Released as a standard Hugging Face causal LM (`LlamaForCausalLM`).
 
 ## How to use
@@ -49,7 +47,7 @@ model = AutoModelForCausalLM.from_pretrained(
 prompt = "<dna>ATGCGCTAGCTACGATCGATCGTAGCTAGCTAGCTAGCTACG"
 inputs = tok(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
 out = model.generate(**inputs, max_new_tokens=64, do_sample=False)
-print(tok.decode(out[0][inputs.input_ids.shape[1]:]))
+print(tok.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
 ```
 
 ### Recommended use: speculative decoding with Carbon-3B / Carbon-8B
@@ -74,17 +72,15 @@ out = target.generate(
     **inputs, max_new_tokens=256, do_sample=False,
     assistant_model=draft,
 )
-print(tok.decode(out[0][inputs.input_ids.shape[1]:]))
+print(tok.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
 ```
 
 Output is guaranteed identical to greedy decoding with the target model alone; only wall-clock latency is reduced.
 
 ## Evaluation
 
-Carbon-500M is benchmarked against  ≈ 1B-parameter DNA models on the standard Carbon evaluation suite. See the [Carbon-3B card](https://huggingface.co/HuggingFaceBio/Carbon-3B#evaluation) for the task definitions and methodology.
-
-> TODO Loubna: add one downstream table comparing Carbon-500M to other 1B-class baselines. -->
+Carbon-500M is benchmarked against ≈ 1B-parameter DNA models on the standard Carbon evaluation suite. See the [Carbon-3B card](https://huggingface.co/HuggingFaceBio/Carbon-3B#evaluation) for the task definitions and methodology.
 
 ## License
 
-Apache 2.0.
+Apache 2.0.