Meridian-AC-Nano is a compact (~596M parameter) research proofreading and academic-writing model developed by COGERPHERE AI Labs. It is the flagship model of the MERIDIAN 0.1 research programme and is specialized for grammar correction, spelling and punctuation fixes, LaTeX correction, academic tone refinement, and restrained no-over-edit text refinement.

The model is trained on a chat-first, multi-task corpus covering proofreading, LaTeX repair, academic writing, code correction, identity/chat, instruction following, and hard negatives. It loads as a standard causal language model with a ChatML chat template.

Capabilities

Meridian-AC-Nano supports, as verified by the training corpus and per-task evaluation:

Research proofreading — correcting grammar, spelling, punctuation, and wording while preserving meaning.
Grammar correction — grammatical and spelling fixes across academic text.
LaTeX-aware proofreading — fixing syntax and formatting errors in LaTeX source.
Academic / research writing refinement — tone refinement and text improvement.
Text refinement / no-over-edit tasks — minimal, meaning-preserving edits (restraint).

Out-of-Scope Use

The model is not intended for production medical, legal, or financial advice; factual knowledge recall; or large-scale general-purpose coding. It is a small model fine-tuned for a specialized proofreading/academic-writing niche.

Evaluation

Results on the held-out test set (n=162, seed 42, greedy decoding) for the flagship run (main_nano):

Metric	Value
ROUGE-1	0.9308
ROUGE-2	0.9138
ROUGE-L	0.929
BLEU-4	0.8797
Exact match (%)	74.69
LaTeX validity (%)	97.53
LaTeX validity (LaTeX subset, %)	95.24
Quality score	0.9032

Per-Task Results

Task	n	ROUGE-L	BLEU-4	Exact match (%)
academic	13	0.9744	0.9231	92.31
chat	33	0.7142	0.6064	6.06
code	21	1.0000	1.0000	100.00
hard_negative	6	1.0000	1.0000	100.00
identity	4	0.7667	0.5167	50.00
instruction	10	0.9553	0.8484	70.00
latex	42	0.9978	0.9762	95.24
proofread	23	0.9884	0.9543	91.30
restraint	10	1.0000	1.0000	100.00

Training

Fine-tuning was performed on a chat-first multi-task corpus using the Unsloth LoRA pipeline on an NVIDIA RTX 3050 (6 GB Laptop GPU).

Hyperparameter	Value
LoRA rank	32
LoRA alpha	64
LoRA dropout	0.05
Target modules	`q_proj`, `v_proj`
Epochs	3
Learning rate	2e-4
Weight decay	0.01
Warmup steps	50
Optimizer	paged_adamw_8bit
Batch size / grad accum	1 / 8
Max sequence length	2048
Seed	42

Training Data

Split	Examples
Train	1,275
Validation	158
Test	162
Total	1,595

Task categories (all splits): proofread (230), latex (413), academic (130), chat (324), code (198), identity (40), restraint (100), hard_negative (60), instruction (100).

Data sources: manual curation (120), synthetic generation (561), augmented (92), template (822).

Limitations

Small model: 0.6B parameters; conversational/chat quality is best-effort and lower than larger models.
Factual knowledge: not a general knowledge or instruction-tuned model at the scale of frontier models.
Chat exact match is low (6.06%) — expected for open-ended chat responses, which are not verbatim reproducible.
Domain scope: trained primarily on academic/LaTeX/proofreading content; performance on unrelated domains is not evaluated.

Usage

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "COGERPHEREAILABS/meridian-ac-nano"

tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForCausalLM.from_pretrained(repo_id, dtype=torch.float16)

messages = [
    {
        "role": "system",
        "content": "You are Meridian, a precise research proofreading assistant. Correct grammar, spelling, punctuation, and wording while preserving the original meaning.",
    },
    {
        "role": "user",
        "content": "The results indicates that the model perform very well on the proofreading task.",
    },
]

text = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True, return_dict=False
)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(**inputs, do_sample=False, max_new_tokens=128)

response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(response)

Ollama users can pull the GGUF distribution directly:

ollama run hf.co/COGERPHEREAILABS/meridian-ac-nano-GGUF:Q8_0

License

Apache-2.0. See the base model attribution below for the underlying license terms.

Base Model Attribution

Meridian-AC-Nano is a LoRA fine-tune of Qwen/Qwen3-0.6B (Apache-2.0), sourced via ModelScope (ungated base checkpoint). Full attribution and the base model card are available at https://huggingface.co/Qwen/Qwen3-0.6B. The weights are therefore released under Apache-2.0.

Citation

If you use this model in your research, please cite:

bibtex

@software{cogerphere_meridian_nano,
  author  = {Tripathi, Shivanshi and Pandey, Yuvraj},
  title   = {MERIDIAN 0.1: SLM-Driven Academic Proofreading Models},
  year    = {2026},
  publisher = {COGERPHERE AI Labs},
  url     = {https://github.com/COGERPHEREAILABS/Meridian-SLM-Driven-Models-Research},
  doi     = {10.5281/zenodo.21863555}
}

Contact

COGERPHERE AI Labs — GitHub · DOI 10.5281/zenodo.21863555