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@@ -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 +1,317 @@
+---
+base_model: ilsp/Llama-Krikri-8B-Base
+library_name: peft
+pipeline_tag: text-generation
+tags:
+- base_model:adapter:ilsp/Llama-Krikri-8B-Base
+- lora
+- transformers
+---
+
+# Model Card: Krikri Dialectal LoRA Adapter
+
+A LoRA (Low-Rank Adaptation) fine-tuned adapter for generating text in Greek dialects. This model extends the [Llama-Krikri-8B-Base](https://huggingface.co/ilsp/Llama-Krikri-8B-Base) model with specialized training on natural prompts for four major Greek dialects: Pontic, Cretan, Northern Greek, and Cypriot.
+
+## Model Details
+
+### Model Description
+
+This is a PEFT (Parameter-Efficient Fine-Tuning) adapter trained using LoRA on the Krikri-8B-Base model. The adapter enables the base model to generate text in Greek dialects when prompted with natural Greek instructions. The model was trained exclusively on dialectal data (excluding Standard Modern Greek) to focus on dialectal generation capabilities.
+
+- **Developed by:** [Your Name/Organization]
+- **Model type:** LoRA Adapter for Causal Language Modeling
+- **Language(s) (NLP):** Greek (dialectal variants: Pontic, Cretan, Northern Greek, Cypriot)
+- **License:** [Inherits from base model - specify if known]
+- **Finetuned from model:** [ilsp/Llama-Krikri-8B-Base](https://huggingface.co/ilsp/Llama-Krikri-8B-Base)
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+This model is designed for generating text in Greek dialects. It can be used for:
+
+- **Dialectal text generation**: Generate text in Pontic, Cretan, Northern Greek, or Cypriot dialects
+- **Dialectal conversation**: Engage in conversations using natural dialectal Greek
+- **Cultural preservation**: Assist in documenting and preserving Greek dialectal varieties
+- **Educational purposes**: Help learners understand dialectal variations in Greek
+
+**Example prompts:**
+- "Γράψε στην ποντιακή διάλεκτο: [your text]"
+- "Απάντησε στα κρητικά: [your question]"
+- "Στην κυπριακή γλώσσα: [your instruction]"
+
+### Downstream Use
+
+This adapter can be integrated into:
+- Chatbots and conversational AI systems for dialectal Greek
+- Text generation applications requiring dialectal output
+- Language learning tools for Greek dialects
+- Cultural heritage and documentation projects
+
+### Out-of-Scope Use
+
+This model is **not** suitable for:
+- Standard Modern Greek generation (trained only on dialects)
+- Formal or academic writing in dialects
+- Real-time translation between dialects
+- Medical, legal, or critical decision-making applications
+- Generating content without human oversight
+
+## Bias, Risks, and Limitations
+
+### Limitations
+
+- **Dialectal coverage:** Trained only on four major Greek dialects; may not perform well on other regional varieties
+- **Standard Greek:** Explicitly excluded from training; not suitable for Standard Modern Greek generation
+- **Data quality:** Performance depends on the quality and representativeness of training data
+- **Dialectal variation:** Greek dialects have significant internal variation; model may not capture all sub-dialectal nuances
+- **Context sensitivity:** May generate inappropriate or incorrect dialectal forms without proper context
+- **Hallucination:** Like all language models, may generate plausible-sounding but incorrect dialectal text
+
+### Risks
+
+- **Cultural sensitivity:** Dialectal text generation should respect cultural and linguistic heritage
+- **Misrepresentation:** Incorrect dialectal forms could mislead users about authentic dialectal usage
+- **Bias:** Training data may reflect biases present in source materials
+
+### Recommendations
+
+Users should:
+- Verify dialectal accuracy with native speakers or dialectal experts
+- Use the model as a tool to assist, not replace, human expertise
+- Be aware that generated text may require editing and fact-checking
+- Consider the cultural and linguistic context when using dialectal generation
+- Not use for critical applications without human oversight
+
+## How to Get Started with the Model
+
+### Installation
+
+```bash
+pip install transformers peft torch
+```
+
+### Basic Usage
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+
+# Load base model
+base_model = AutoModelForCausalLM.from_pretrained(
+    "ilsp/Llama-Krikri-8B-Base",
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained("ilsp/Llama-Krikri-8B-Base")
+
+# Load LoRA adapter
+model = PeftModel.from_pretrained(base_model, "your-username/your-model-name")
+
+# Generate text
+prompt = "Γράψε στην ποντιακή διάλεκτο: Καλημέρα, πώς είσαι;"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=100,
+        temperature=0.7,
+        do_sample=True
+    )
+
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+## Training Details
+
+### Training Data
+
+The model was trained on **23,421 natural prompt examples** covering four Greek dialects:
+- **Pontic** (ποντιακή)
+- **Cretan** (κρητική)
+- **Northern Greek** (βορειοελλαδική)
+- **Cypriot** (κυπριακή)
+
+**Data preprocessing:**
+- Artificial dialect tags (e.g., `<po>`, `<cr>`, `<no>`, `<cy>`) were converted to natural Greek prompts
+- Standard Modern Greek examples were excluded from training
+- Prompts use natural instructions like "Γράψε στην [dialect] διάλεκτο:" instead of tags
+- Data was shuffled and split into train (95%) and validation (5%) sets
+
+### Training Procedure
+
+#### Preprocessing
+
+1. Original dataset contained examples with artificial dialect tags
+2. Tags were replaced with natural Greek prompt templates
+3. Standard Greek examples were filtered out
+4. Text was tokenized with max length of 512 tokens
+5. Prompts and completions were concatenated for causal language modeling
+
+#### Training Hyperparameters
+
+- **Training regime:** bf16 mixed precision
+- **LoRA rank (r):** 16
+- **LoRA alpha:** 32
+- **LoRA dropout:** 0.1
+- **Target modules:** q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
+- **Number of epochs:** 3
+- **Batch size:** 2 (per device)
+- **Gradient accumulation steps:** 8 (effective batch size: 16)
+- **Learning rate:** 3e-4
+- **Warmup steps:** 100
+- **Max sequence length:** 512 tokens
+- **Trainable parameters:** 41,943,040 (0.51% of total model parameters)
+
+#### Speeds, Sizes, Times
+
+- **Adapter size:** ~160 MB (adapter_model.safetensors)
+- **Base model size:** ~16 GB (8B parameters in bfloat16)
+- **Total trainable parameters:** 41.9M (0.51% of base model)
+- **Training timestamp:** 2025-09-09
+
+## Evaluation
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+- **Validation split:** 5% of training data (approximately 1,171 examples)
+- **Test strategy:** Held-out validation set during training
+
+#### Factors
+
+Evaluation should consider:
+- **Dialect accuracy:** Correctness of dialectal forms
+- **Dialect diversity:** Coverage across the four trained dialects
+- **Prompt following:** Adherence to natural Greek instructions
+- **Fluency:** Naturalness of generated dialectal text
+
+#### Metrics
+
+- **Loss:** Training and validation loss during fine-tuning
+- **Perplexity:** Model's uncertainty in generating dialectal text
+- **Human evaluation:** Recommended for assessing dialectal accuracy and naturalness
+
+### Results
+
+[Evaluation results to be added after assessment]
+
+#### Summary
+
+This adapter extends the Krikri-8B-Base model with specialized capabilities for generating text in Greek dialects. Performance should be evaluated through both automated metrics and human evaluation by native dialect speakers.
+
+
+
+## Model Examination
+
+The model uses LoRA adapters, which allow for efficient fine-tuning while maintaining the base model's general capabilities. The adapter weights can be examined separately from the base model, making it easier to understand what dialectal patterns the model has learned.
+
+Key areas for examination:
+- How the adapter modifies attention patterns for dialectal generation
+- Which layers are most important for dialectal variation
+- Comparison of adapter weights across different dialect prompts
+
+## Environmental Impact
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** GPU (specific model not available)
+- **Training approach:** Parameter-efficient (LoRA) - only 0.51% of parameters trained
+- **Training efficiency:** LoRA significantly reduces computational requirements compared to full fine-tuning
+- **Carbon Emitted:** [To be calculated based on specific hardware and training duration]
+
+## Technical Specifications
+
+### Model Architecture and Objective
+
+- **Base architecture:** Llama (8B parameters)
+- **Adapter type:** LoRA (Low-Rank Adaptation)
+- **Task:** Causal Language Modeling
+- **Objective:** Generate dialectal Greek text given natural Greek prompts
+
+### LoRA Configuration
+
+```json
+{
+  "peft_type": "LORA",
+  "task_type": "CAUSAL_LM",
+  "r": 16,
+  "lora_alpha": 32,
+  "lora_dropout": 0.1,
+  "target_modules": [
+    "q_proj", "k_proj", "v_proj", "o_proj",
+    "gate_proj", "up_proj", "down_proj"
+  ],
+  "bias": "none"
+}
+```
+
+### Compute Infrastructure
+
+#### Hardware
+
+- Training performed on GPU infrastructure (specific hardware details not available)
+
+#### Software
+
+- **PEFT:** 0.17.1
+- **Transformers:** (version from training environment)
+- **PyTorch:** (version from training environment)
+- **Python:** 3.12
+
+## Citation
+
+If you use this model, please cite:
+
+**BibTeX:**
+
+```bibtex
+@misc{krikri-dialectal-lora,
+  title={Krikri Dialectal LoRA Adapter: Fine-tuned Greek Dialect Generation},
+  author={[Your Name]},
+  year={2025},
+  howpublished={\url{https://huggingface.co/your-username/your-model-name}}
+}
+```
+
+**APA:**
+
+[Your Name]. (2025). Krikri Dialectal LoRA Adapter: Fine-tuned Greek Dialect Generation [Computer software]. Hugging Face. https://huggingface.co/your-username/your-model-name
+
+## Glossary
+
+- **LoRA (Low-Rank Adaptation):** A parameter-efficient fine-tuning technique that adds trainable low-rank matrices to model layers
+- **PEFT (Parameter-Efficient Fine-Tuning):** A library for efficient fine-tuning methods that train only a small subset of model parameters
+- **Dialectal Greek:** Regional varieties of Greek that differ from Standard Modern Greek in vocabulary, grammar, and pronunciation
+- **Causal Language Modeling:** A task where the model predicts the next token given previous tokens
+
+## More Information
+
+This adapter was trained to preserve and generate Greek dialectal varieties. For questions, issues, or contributions, please open an issue on the model repository.
+
+## Model Card Authors
+
+[Your Name/Organization]
+
+## Model Card Contact
+
+[Your Contact Information]
+### Framework versions
+
+- PEFT 0.17.1

adapter_config.json

@@ -0,0 +1,42 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "ilsp/Llama-Krikri-8B-Base",
+  "bias": "none",
+  "corda_config": null,
+  "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": 0.1,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "qalora_group_size": 16,
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "v_proj",
+    "gate_proj",
+    "o_proj",
+    "k_proj",
+    "q_proj",
+    "up_proj",
+    "down_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:82c68d667a9ec7e083d17e50852d8a8fcb552e28e95330a6e2b0bbe87c2fc787
+size 167832240

chat_template.jinja

@@ -0,0 +1,109 @@
+{{- bos_token }}
+{%- if custom_tools is defined %}
+    {%- set tools = custom_tools %}
+{%- endif %}
+{%- if not tools_in_user_message is defined %}
+    {%- set tools_in_user_message = true %}
+{%- endif %}
+{%- if not date_string is defined %}
+    {%- set date_string = "26 Jul 2024" %}
+{%- endif %}
+{%- if not tools is defined %}
+    {%- set tools = none %}
+{%- endif %}
+
+{#- This block extracts the system message, so we can slot it into the right place. #}
+{%- if messages[0]['role'] == 'system' %}
+    {%- set system_message = messages[0]['content']|trim %}
+    {%- set messages = messages[1:] %}
+{%- else %}
+    {%- set system_message = "" %}
+{%- endif %}
+
+{#- System message + builtin tools #}
+{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
+{%- if builtin_tools is defined or tools is not none %}
+    {{- "Environment: ipython\n" }}
+{%- endif %}
+{%- if builtin_tools is defined %}
+    {{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}}
+{%- endif %}
+{{- "Cutting Knowledge Date: December 2023\n" }}
+{{- "Today Date: " + date_string + "\n\n" }}
+{%- if tools is not none and not tools_in_user_message %}
+    {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+{%- endif %}
+{{- system_message }}
+{{- "<|eot_id|>" }}
+
+{#- Custom tools are passed in a user message with some extra guidance #}
+{%- if tools_in_user_message and not tools is none %}
+    {#- Extract the first user message so we can plug it in here #}
+    {%- if messages | length != 0 %}
+        {%- set first_user_message = messages[0]['content']|trim %}
+        {%- set messages = messages[1:] %}
+    {%- else %}
+        {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
+{%- endif %}
+    {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
+    {{- "Given the following functions, please respond with a JSON for a function call " }}
+    {{- "with its proper arguments that best answers the given prompt.\n\n" }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+    {{- first_user_message + "<|eot_id|>"}}
+{%- endif %}
+
+{%- for message in messages %}
+    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
+        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
+    {%- elif 'tool_calls' in message %}
+        {%- if not message.tool_calls|length == 1 %}
+            {{- raise_exception("This model only supports single tool-calls at once!") }}
+        {%- endif %}
+        {%- set tool_call = message.tool_calls[0].function %}
+        {%- if builtin_tools is defined and tool_call.name in builtin_tools %}
+            {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
+            {{- "<|python_tag|>" + tool_call.name + ".call(" }}
+            {%- for arg_name, arg_val in tool_call.arguments | items %}
+                {{- arg_name + '="' + arg_val + '"' }}
+                {%- if not loop.last %}
+                    {{- ", " }}
+                {%- endif %}
+                {%- endfor %}
+            {{- ")" }}
+        {%- else  %}
+            {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
+            {{- '{"name": "' + tool_call.name + '", ' }}
+            {{- '"parameters": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- "}" }}
+        {%- endif %}
+        {%- if builtin_tools is defined %}
+            {#- This means we're in ipython mode #}
+            {{- "<|eom_id|>" }}
+        {%- else %}
+            {{- "<|eot_id|>" }}
+        {%- endif %}
+    {%- elif message.role == "tool" or message.role == "ipython" %}
+        {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
+        {%- if message.content is mapping or message.content is iterable %}
+            {{- message.content | tojson }}
+        {%- else %}
+            {{- message.content }}
+        {%- endif %}
+        {{- "<|eot_id|>" }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
+{%- endif %}

code/convert_to_natural_prompts_dialects_only.py

@@ -0,0 +1,96 @@
+import json
+import random
+from collections import defaultdict
+
+def convert_to_natural_prompts_dialects_only():
+    """Convert artificial tags to natural Greek prompts - DIALECTS ONLY, NO STANDARD GREEK"""
+    
+    prompt_templates = {
+        'pontic': [
+            "Γράψε στην ποντιακή διάλεκτο:",
+            "Απάντησε στα ποντιακά:",
+            "Στην ποντιακή γλώσσα:",
+            "Συνέχισε στα ποντιακά:",
+        ],
+        'cretan': [
+            "Γράψε στην κρητική διάλεκτο:",
+            "Απάντησε στα κρητικά:",
+            "Στην κρητική γλώσσα:",
+            "Συνέχισε στα κρητικά:",
+        ],
+        'northern': [
+            "Γράψε στη βορειοελλαδική διάλεκτο:",
+            "Απάντησε στα βορειοελλαδικά:",
+            "Στη διάλεκτο της Βόρειας Ελλάδας:",
+            "Συνέχισε στα βορειοελλαδικά:",
+        ],
+        'cypriot': [
+            "Γράψε στην κυπριακή διάλεκτο:",
+            "Απάντησε στα κυπριακά:",
+            "Στην κυπριακή γλώσσα:",
+            "Συνέχισε στα κυπριακά:",
+        ]
+    }
+    
+    examples_by_dialect = defaultdict(list)
+    
+    with open('dataset.jsonl', 'r', encoding='utf-8') as f:
+        for line in f:
+            try:
+                data = json.loads(line.strip())
+                if 'prompt' in data and 'completion' in data:
+                    prompt = data['prompt']
+                    completion = data['completion']
+                    
+                    # ONLY process dialects - SKIP <gr>
+                    if prompt.startswith('<po>'):
+                        dialect = 'pontic'
+                        clean_prompt = prompt[4:].strip()
+                    elif prompt.startswith('<cr>'):
+                        dialect = 'cretan'
+                        clean_prompt = prompt[4:].strip()
+                    elif prompt.startswith('<no>'):
+                        dialect = 'northern'
+                        clean_prompt = prompt[4:].strip()
+                    elif prompt.startswith('<cy>'):
+                        dialect = 'cypriot'
+                        clean_prompt = prompt[4:].strip()
+                    else:
+                        continue  # Skip <gr> and anything else
+                    
+                    template = random.choice(prompt_templates[dialect])
+                    
+                    if clean_prompt:
+                        natural_prompt = f"{template} {clean_prompt}"
+                    else:
+                        natural_prompt = template
+                    
+                    examples_by_dialect[dialect].append({
+                        'prompt': natural_prompt,
+                        'completion': completion
+                    })
+                    
+            except json.JSONDecodeError:
+                continue
+    
+    # Show statistics
+    for dialect, examples in examples_by_dialect.items():
+        print(f"{dialect}: {len(examples)} examples")
+    
+    # Combine all dialect examples
+    all_examples = []
+    for dialect_examples in examples_by_dialect.values():
+        all_examples.extend(dialect_examples)
+    
+    random.shuffle(all_examples)
+    
+    # Save dialects-only natural prompts dataset
+    with open('natural_prompts_dialects_only.jsonl', 'w', encoding='utf-8') as f:
+        for example in all_examples:
+            f.write(json.dumps(example, ensure_ascii=False) + '\n')
+    
+    print(f"\nConverted {len(all_examples)} DIALECT-ONLY examples to natural prompts")
+    return len(all_examples)
+
+if __name__ == "__main__":
+    convert_to_natural_prompts_dialects_only()

code/train_krikri_natural_lora_persistent.py

@@ -0,0 +1,129 @@
+import os
+from huggingface_hub import login
+
+# Set persistent storage paths
+PERSISTENT_MODEL_DIR = os.path.expanduser("~/persistent_models")
+os.environ['HF_HOME'] = f"{PERSISTENT_MODEL_DIR}/hf_cache"
+os.environ['TRANSFORMERS_CACHE'] = f"{PERSISTENT_MODEL_DIR}/hf_cache"
+
+import json
+import torch
+from transformers import LlamaForCausalLM, AutoTokenizer, Trainer, TrainingArguments, DataCollatorForLanguageModeling
+from datasets import Dataset
+from peft import LoraConfig, get_peft_model, TaskType
+
+def load_natural_prompts_dataset():
+    texts = []
+    
+    with open('natural_prompts_dialects_only.jsonl', 'r', encoding='utf-8') as f:
+        for line in f:
+            try:
+                data = json.loads(line.strip())
+                if 'prompt' in data and 'completion' in data:
+                    text = f"{data['prompt']}\n{data['completion']}"
+                    texts.append(text.strip())
+            except json.JSONDecodeError:
+                continue
+    
+    print(f"Loaded {len(texts)} natural prompt examples")
+    return texts
+
+def main():
+    print("KRIKRI + NATURAL PROMPTS + LORA TRAINING WITH PERSISTENT STORAGE")
+    
+    # Ensure persistent directories exist
+    os.makedirs(f"{PERSISTENT_MODEL_DIR}/hf_cache", exist_ok=True)
+    os.makedirs(f"{PERSISTENT_MODEL_DIR}/models", exist_ok=True)
+    
+    # Login to Hugging Face (uses token from environment variable HF_TOKEN or prompts for login)
+    # Users should set HF_TOKEN environment variable or use: huggingface-cli login
+    if os.getenv('HF_TOKEN'):
+        login(token=os.getenv('HF_TOKEN'))
+    else:
+        # Will prompt for login if not already logged in
+        try:
+            login()
+        except Exception:
+            print("Warning: Could not login to Hugging Face. Make sure HF_TOKEN is set or run 'huggingface-cli login'")
+    
+    # Load dataset
+    texts = load_natural_prompts_dataset()
+    dataset = Dataset.from_dict({'text': texts})
+    split_dataset = dataset.train_test_split(test_size=0.05)
+    
+    print("Loading Krikri-8B-Base...")
+    base_model = LlamaForCausalLM.from_pretrained(
+        "ilsp/Llama-Krikri-8B-Base",
+        torch_dtype=torch.bfloat16,
+        device_map="auto",
+        low_cpu_mem_usage=True
+    )
+    
+    # Configure LoRA
+    print("Applying LoRA configuration...")
+    lora_config = LoraConfig(
+        task_type=TaskType.CAUSAL_LM,
+        r=16,
+        lora_alpha=32,
+        target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
+        lora_dropout=0.1,
+        bias="none",
+        inference_mode=False
+    )
+    
+    model = get_peft_model(base_model, lora_config)
+    model.print_trainable_parameters()
+    
+    tokenizer = AutoTokenizer.from_pretrained("ilsp/Llama-Krikri-8B-Base")
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+    
+    def tokenize_function(examples):
+        result = tokenizer(examples['text'], truncation=True, padding="max_length", max_length=512)
+        result['labels'] = result['input_ids'].copy()
+        return result
+    
+    train_dataset = split_dataset['train'].map(tokenize_function, batched=True, remove_columns=['text'])
+    eval_dataset = split_dataset['test'].map(tokenize_function, batched=True, remove_columns=['text'])
+    
+    # PERSISTENT STORAGE: Save to EFS
+    output_dir = f"{PERSISTENT_MODEL_DIR}/models/krikri-natural-lora"
+    
+    training_args = TrainingArguments(
+        output_dir=output_dir,
+        num_train_epochs=3,
+        per_device_train_batch_size=2,
+        gradient_accumulation_steps=8,
+        learning_rate=3e-4,
+        bf16=True,
+        logging_steps=25,
+        save_steps=200,
+        eval_steps=200,
+        eval_strategy="steps",
+        remove_unused_columns=False,
+        report_to=[],
+        warmup_steps=100,
+        save_total_limit=3,
+        load_best_model_at_end=True
+    )
+    
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=train_dataset,
+        eval_dataset=eval_dataset,
+        data_collator=DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
+    )
+    
+    print("Starting LoRA training with persistent storage...")
+    trainer.train()
+    
+    # Save to persistent storage
+    model.save_pretrained(output_dir)
+    tokenizer.save_pretrained(output_dir)
+    
+    print(f"Model saved to persistent storage: {output_dir}")
+    print("This model will survive instance restarts!")
+
+if __name__ == "__main__":
+    main()

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|eot_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|reserved_special_token_247|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "<|eot_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cac6459ddc56e71179d32771d028a77db768a97aa08dd079684f84f611f69c55
+size 19508309

tokenizer_config.json

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training_info.json

@@ -0,0 +1,7 @@
+{
+  "model_name": "Krikri-8B-Base",
+  "training_type": "LoRA fine-tuning",
+  "dataset": "Natural prompts (dialects only)",
+  "timestamp": "20250909_173712",
+  "persistent_location": "/home/sagemaker-user/krikri_natural_lora_20250909_173712"
+}