Publish local 2B experimental recovery adapter

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,115 @@
+---
+base_model: Qwen/Qwen3.5-2B-Base
+library_name: peft
+pipeline_tag: text-generation
+license: apache-2.0
+tags:
+- base_model:adapter:Qwen/Qwen3.5-2B-Base
+- lora
+- peft
+- sft
+- transformers
+- trl
+- andaluh
+- marichatmen
+---
+
+# MariChatmen 2B Experimental
+
+MariChatmen 2B Experimental is a LoRA/PEFT adapter for
+`Qwen/Qwen3.5-2B-Base`. It was trained locally on 2026-05-13 as a recovery
+run after the original 2B experiment failed its behavioural gate and no
+usable 2B artifact was available.
+
+This is an experimental checkpoint. The current demo should prefer the 4B
+adapter (`MariChatmen/MariChatmen-4B-Experimental`) when hardware allows it.
+
+## Intended Use
+
+The adapter is intended for Spanish/Andaluh chat experiments around the
+fictional MariChatmen assistant persona. It is not a general production
+assistant and should not be used for high-stakes decisions.
+
+## Loading
+
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+base_id = "Qwen/Qwen3.5-2B-Base"
+adapter_id = "MariChatmen/MariChatmen-2B-Experimental"
+
+tokenizer = AutoTokenizer.from_pretrained(adapter_id)
+base = AutoModelForCausalLM.from_pretrained(base_id, device_map="auto")
+model = PeftModel.from_pretrained(base, adapter_id)
+```
+
+## Training Data
+
+The local recovery mix contained 22,858 SFT training rows and 1,134 validation
+rows. It combined:
+
+- a broad local Andaluh SFT mix derived from Spanish SFT data transformed with
+  `andalugeeks/andaluh-py`;
+- oversampled MariChatmen project-authored repair anchors covering identity,
+  style, safety, and instruction-following regressions.
+
+The mixed training dataset is not uploaded with this model. The broad SFT
+portion includes downloaded rows transformed with `andaluh-py`, so it should
+not be republished as MariChatmen proprietary/project data. Uploadable project
+data is tracked separately in `MariChatmen/MariChatmen-Project-Data`.
+
+## Credits and Copyright
+
+- Base model: `Qwen/Qwen3.5-2B-Base`.
+- Fine-tuning framework: Hugging Face Transformers, TRL, PEFT, and PyTorch.
+- Transliteration / Andaluh transformation tooling: `andalugeeks/andaluh-py`.
+- Broad Spanish SFT sources recorded in the local row metadata include
+  `VillanovaAI/villanova-sft-2603` and upstream sources such as
+  `CohereLabs/aya_collection`; original dataset licenses and attribution
+  requirements remain with those sources.
+- MariChatmen repair anchors are project-authored/curated material for this
+  project and are documented in the project data repository.
+
+## Training Procedure
+
+- Stage: supervised fine-tuning.
+- Base model: `Qwen/Qwen3.5-2B-Base`.
+- Tokenizer source: recovered MariChatmen 4B checkpoint tokenizer.
+- Sequence length: 384.
+- Prompt token cap: 256.
+- Max steps: 600.
+- LoRA rank: 16.
+- LoRA alpha: 32.
+- LoRA dropout: 0.05.
+- Learning rate: 5e-5.
+- Gradient accumulation: 16.
+- Embeddings resized and trained to match the MariChatmen tokenizer.
+- Hardware: local NVIDIA RTX 5060 Laptop GPU, 8 GB VRAM.
+
+## Evaluation Snapshot
+
+The selected checkpoint is step 600, which was also the best checkpoint by
+validation loss.
+
+- Final validation loss: 2.2429933547973633.
+- Final validation mean token accuracy: 0.5876548955053303.
+- Training runtime: approximately 7,633 seconds.
+- Generation probes showed usable instruction following and safety refusals,
+  with remaining roughness on some style and technical prompts.
+
+## Limitations
+
+The model is a LoRA adapter, not a merged full model. Quality is expected to be
+below the recovered 4B adapter, and the Andaluh style can be uneven. Outputs
+may contain linguistic artifacts from automatic transformation and should be
+reviewed before publication.
+
+## Framework Versions
+
+- PEFT 0.19.1
+- TRL 1.3.0
+- Transformers 5.8.0.dev0
+- PyTorch 2.11.0+cu130
+- Datasets 4.8.5
+- Tokenizers 0.22.2

adapter_config.json

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+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "Qwen/Qwen3.5-2B-Base",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": true,
+  "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": 32,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "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": [
+    "gate_proj",
+    "k_proj",
+    "q_proj",
+    "down_proj",
+    "o_proj",
+    "v_proj",
+    "up_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
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+  "use_rslora": false
+}

adapter_model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9e7d5f5728b419ab268e935934c0b26904e10aa0bf01d2e3d0b035aae4f1cd25
+size 2114209072

chat_template.jinja

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+{%- for message in messages %}
+{%- set content = message['content'] if message['content'] is string else '' %}
+{%- if message['role'] == 'assistant' %}
+{{- '<|im_start|>assistant
+' -}}{% generation %}{{- content + '<|im_end|>
+' }}{% endgeneration %}
+{%- elif message['role'] == 'system' or message['role'] == 'user' %}
+{{- '<|im_start|>' + message['role'] + '
+' + content + '<|im_end|>
+' }}
+{%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+{{- '<|im_start|>assistant
+' }}
+{%- endif %}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:df2c43f2febaed5d06aa3e37c16c538ca4d814af2eb1d78cedf7246ef20ef0a0
+size 20276155

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "audio_bos_token": "<|audio_start|>",
+  "audio_eos_token": "<|audio_end|>",
+  "audio_token": "<|audio_pad|>",
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "image_token": "<|image_pad|>",
+  "is_local": true,
+  "local_files_only": false,
+  "model_max_length": 262144,
+  "model_specific_special_tokens": {
+    "audio_bos_token": "<|audio_start|>",
+    "audio_eos_token": "<|audio_end|>",
+    "audio_token": "<|audio_pad|>",
+    "image_token": "<|image_pad|>",
+    "video_token": "<|video_pad|>",
+    "vision_bos_token": "<|vision_start|>",
+    "vision_eos_token": "<|vision_end|>"
+  },
+  "pad_token": "<|endoftext|>",
+  "pretokenize_regex": "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?[\\p{L}\\p{M}]+|\\p{N}| ?[^\\s\\p{L}\\p{M}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null,
+  "video_token": "<|video_pad|>",
+  "vision_bos_token": "<|vision_start|>",
+  "vision_eos_token": "<|vision_end|>"
+}

training_args.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3185c6d3f8b041b8a304606bf93341eb596f0d0562c5b079df7197845450a16b
+size 5777