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Model: adeljebali/llama3.1-gec-strict
Source: Original Platform

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README.md

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+---
+base_model: unsloth/Meta-Llama-3.1-8B-bnb-4bit
+library_name: peft
+pipeline_tag: text-generation
+tags:
+- base_model:adapter:unsloth/Meta-Llama-3.1-8B-bnb-4bit
+- lora
+- sft
+- transformers
+- trl
+- unsloth
+---
+
+# Model Card
+
+
+## Model Description
+
+A French grammar correction model designed primarily for learners of French as a second language (FSL/FLE). It corrects grammar, spelling, syntax, punctuation, and stylistic issues while preserving the original meaning and tone as much as possible.
+
+The model is especially effective for:
+
+- French learners and students
+- Academic and professional writing
+- Language practice and self-correction
+- Improving fluency and sentence naturalness
+
+It can also perform high-quality translation from English to French, making it useful both as a grammar corrector and as a lightweight bilingual writing assistant.
+
+Optimized for local inference with LM Studio and compatible with GGUF quantizations for efficient CPU or GPU deployment.
+
+
+- **Developed by:** Adel Jebali, Concordia University
+- **Funded by:** SSHRC
+- **Language(s) (NLP):** French
+- **License:** Apache 2.0
+- **Finetuned:** from Llama 3.1-8B
+
+## Uses
+
+Correct you French written texts! It is not a chat model.
+
+
+## Bias, Risks, and Limitations
+
+This AI LLM is not 100% bullet-proof. Errors are still possible.
+
+
+## How to use
+
+Works best with [LM Studio](https://lmstudio.ai) and is available in three quantization formats:
+
+* **4-bit — Q4_K_M — 4.92 GB**
+  Best choice for low-memory systems and entry-level hardware. Recommended for Macs with only 8 GB of unified memory or older GPUs. Offers the fastest loading times and lowest VRAM usage, with a small trade-off in output quality and coherence.
+
+* **6-bit — Q6_K — 6.6 GB**
+  Excellent balance between quality, speed, and memory consumption. A strong default option for most users with 12–16 GB of RAM or mid-range GPUs. In many cases, it delivers quality close to 8-bit while remaining significantly lighter.
+
+* **8-bit — Q8_0 — 8.54 GB**
+  Highest quality and most faithful outputs among the available quantizations. Recommended if your hardware can handle it, especially with a dedicated GPU or Apple Silicon Mac with sufficient unified memory. Produces more stable generations, better grammatical consistency, and fewer hallucinations.
+
+## Recommendations
+
+* **8 GB Macs:** use **Q4_K_M**
+* **16 GB systems:** use **Q6_K** for the best balance
+* **24 GB+ RAM or modern GPU:** use **Q8_0** for maximum quality
+
+For optimal performance in [LM Studio](https://lmstudio.ai):
+
+* Enable **GPU offloading** when available
+* Increase the **context length** only if needed, since larger contexts consume more memory
+* On Apple Silicon Macs, Metal acceleration significantly improves inference speed
+* Temperature 0 (or 0.1)
+* Min p 0
+* Top k 0
+* Top p 1
+
+If your priority is:
+
+* **Maximum speed / lowest memory usage → Q4_K_M**
+* **Best balance → Q6_K**
+* **Best overall quality → Q8_0**
+
+
+# Paper
+A. Jebali, "Developing a Grammatical Error Correction System for French Second Language Written Texts," 2025 5th International Conference on Electrical, Computer and Energy Technologies (ICECET), Paris, France, 2025, pp. 1-6, doi: 10.1109/ICECET63943.2025.11472110.  

adapter_config.json

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+{
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+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "LlamaForCausalLM",
+    "parent_library": "transformers.models.llama.modeling_llama",
+    "unsloth_fixed": true
+  },
+  "base_model_name_or_path": "unsloth/Meta-Llama-3.1-8B-bnb-4bit",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_bias": false,
+  "lora_dropout": 0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.19.1",
+  "qalora_group_size": 16,
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "down_proj",
+    "k_proj",
+    "o_proj",
+    "v_proj",
+    "up_proj",
+    "gate_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

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config.json

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+{
+    "architectures": [
+        "LlamaForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 128000,
+    "torch_dtype": "bfloat16",
+    "eos_token_id": 128001,
+    "head_dim": 128,
+    "hidden_act": "silu",
+    "hidden_size": 4096,
+    "initializer_range": 0.02,
+    "intermediate_size": 14336,
+    "max_position_embeddings": 131072,
+    "mlp_bias": false,
+    "model_type": "llama",
+    "num_attention_heads": 32,
+    "num_hidden_layers": 32,
+    "num_key_value_heads": 8,
+    "pad_token_id": 128004,
+    "pretraining_tp": 1,
+    "rms_norm_eps": 1e-05,
+    "rope_parameters": {
+        "factor": 8.0,
+        "high_freq_factor": 4.0,
+        "low_freq_factor": 1.0,
+        "original_max_position_embeddings": 8192,
+        "rope_theta": 500000.0,
+        "rope_type": "llama3"
+    },
+    "tie_word_embeddings": false,
+    "unsloth_fixed": true,
+    "unsloth_version": "2026.5.2",
+    "use_cache": true,
+    "vocab_size": 128256
+}