Update README.md

README.md

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 ---
 license: apache-2.0
+model_name: Sorbet-v2-25M
+pipeline_tag: text-generation
+tags:
+- qwen2
+- 25M
+language:
+- en
+datasets:
+- epfml/FineWeb-HQ
+- HuggingFaceTB/finemath
+- mlfoundations/dclm-baseline-1.0-parquet
+library_name: transformers
 ---
+
+# Sorbet-v2-25M
+
+~25M-parameter Qwen2-style decoder LM, warm-started from Sorbet-25M and continued-trained in two legs totaling ~12hrs on a single RTX 5060 Ti (16GB). Scores at or above Sorbet-25M on every benchmark below while cutting validation perplexity by ~6%.
+
+## Architecture
+
+Identical to Sorbet-25M: stock Qwen2 throughout, no custom code paths, natively supported by both `transformers` and `llama.cpp`.
+
+| | |
+|---|---|
+| Params | 25,185,920 (~87% non-embedding) |
+| Layers / hidden | 14 / 384 |
+| Attention | GQA 6 heads / 2 KV heads, RoPE θ=100k |
+| FFN | 1024 (SwiGLU) |
+| Context | 4096 |
+| Vocab | 8,192 custom byte-level BPE (tied embeddings) |
+| Precision | bf16 |
+
+## Training
+
+v2 continues the v1 checkpoint through two training runs:
+
+| Leg | Data mix (tokens) | LR schedule |
+|---|---|---|
+| cpt2 | fineweb-edu 70% / infiwebmath 10% / DCLM-baseline 20%, 0.8B tok | cosine, 8-bit AdamW |
+| **v2-final** | FineWeb-HQ 65% / DCLM-baseline 20% / FineMath-4+ 15%, 1.7B tok | cosine peak 1e-4, fp32 AdamW |
+
+Block-shuffled at 131,072 tok/step.
+
+## Benchmarks
+
+All numbers zero-shot via lm-evaluation-harness, bf16, identical settings across checkpoints.
+
+| Task | n | Random | acc | acc_norm |
+|---|---|---|---|---|
+| HellaSwag | 10,042 | 25% | 26.55 ±0.44 | 26.63 ±0.44 |
+| ARC-easy | 2,376 | ~25% | 30.30 ±0.94 | 29.92 ±0.94 |
+| ARC-challenge | 1,172 | ~25% | 18.60 ±1.14 | 22.44 ±1.22 |
+| PIQA | 1,838 | 50% | 54.52 ±1.16 | 53.32 ±1.16 |
+| ArithMark-3.0 | 1,000 | 25% | 32.90 ±1.48 | 33.00 ±1.49 |
+
+Notes:
+- Every score is at or above the Sorbet-25M baseline within error bars.
+- ArithMark-3.0 (`AxiomicLabs/Arithmark-3.0`) remains the strongest relative result (+8 pts over random), consistent with the math share of the pretraining mix.
+- ARC-challenge raw accuracy sits below chance due to a length bias in unnormalized scores; acc_norm is the meaningful metric there.
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+repo = "CodeSoft/sorbet-v2-25m"
+model = AutoModelForCausalLM.from_pretrained(repo, dtype="bfloat16").to("cuda")
+tok = AutoTokenizer.from_pretrained(repo, subfolder="tokenizer")
+ids = tok("Once upon a time", return_tensors="pt").input_ids.cuda()
+print(tok.decode(model.generate(ids, max_new_tokens=64)[0]))
+```
+
+## Limitations
+
+Expect shallow world knowledge and weak performance on knowledge-heavy benchmarks due to the model's small parameter count and limited training budget.
+
+## License
+
+Apache-2.0.