zrah-model-1.0

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<details><summary>Click to expand full config</summary>

Axolotl version: 0.8.0.dev0

adapter: lora
base_model: mistralai/Mistral-7B-Instruct-v0.3
model_type: MistralForCausalLM
tokenizer_type: AutoTokenizer
bf16: true
dataset_processes: 32
datasets:
  - path: bytess/zrah-personal-ai
    type: alpaca
gradient_accumulation_steps: 4
gradient_checkpointing: false
learning_rate: 0.0002
lora_alpha: 32
lora_dropout: 0.05
lora_r: 16           # Or try 8 for smaller size later
lora_target_modules:
  - q_proj
  - v_proj
  - k_proj
  - o_proj
  - gate_proj
  - down_proj
  - up_proj
loraplus_lr_embedding: 1.0e-06
lr_scheduler: cosine
max_prompt_len: 512
micro_batch_size: 4  # Increase from 2 if GPU allows
num_epochs: 3
optimizer: adamw_torch
output_dir: ./outputs/zrah_model
pretrain_multipack_attn: true
sample_packing_bin_size: 200
sample_packing_group_size: 100000
save_only_model: true
save_safetensors: true
sequence_len: 2048
shuffle_merged_datasets: true
train_on_inputs: false
trl:
  use_vllm: false
val_set_size: 0.0
weight_decay: 0.0

</details>

---

Zrah Model 1.0

This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.3, trained using Axolotl on a RunPod instance with H100 PCIe GPU. The training was conducted for personal/self-aligned instruction tuning using a custom dataset: bytess/zrah-personal-ai.

⚠️ This model is intended only for demonstration and trial purposes as part of a personal portfolio. Use is allowed, but modification, fine-tuning, or redistribution is prohibited due to the private nature of the data.

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Model Description

This model uses LoRA adapters to fine-tune Mistral-7B on a dataset of personal prompts and completions designed to align the model to the author's style, tone, and preferences. The model is intended for self-use in chat, writing assistance, and experimentation with instruction-following behavior.

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Intended Uses & Limitations

Intended Uses:

• Personal assistant/chatbot
• Writing/code helper tailored to author's style
• Experimentation with LoRA fine-tuning and self-alignment

Limitations:

• Trained only on a small personal dataset; generalization to broader tasks is limited
• Not evaluated on standardized benchmarks
• May reflect idiosyncratic styles or biases present in the training data

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Training and Evaluation Data

The model was trained on the bytess/zrah-personal-ai dataset. This dataset is structured in Alpaca-style (instruction, input, output) and consists of hand-curated personal instructions and completions.

• Number of examples: 130 rows of data
• Validation set: None used (val_set_size: 0.0)
• Data shuffled and packed for efficiency using Axolotl's sample packing

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Training Procedure

Hyperparameters

• Base model: mistralai/Mistral-7B-v0.1
• Adapter: LoRA (q_proj, k_proj, v_proj, o_proj, gate_proj, down_proj, up_proj)
• LoRA Rank: 16, Alpha: 32, Dropout: 0.05
• Batch size: 2 (micro) × 4 (gradient accumulation) = 8 total
• Learning rate: 2e-4
• Optimizer: AdamW
• Scheduler: Cosine
• Sequence length: 2048
• Epochs: 3
• Train on inputs: False (only outputs used for loss) Hardware
• Trained on RunPod using an H100 PCIe GPU

---

Framework Versions

• PEFT: 0.14.0
• Transformers: 4.49.0
• PyTorch: 2.5.1+cu124
• Datasets: 3.2.0
• Tokenizers: 0.21.0
• Axolotl: 0.8.0.dev0

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License

This model is provided under a custom license:

✅ Use allowed for testing and evaluation

❌ No fine-tuning

❌ No redistribution

❌ No use in commercial or production applications

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Contact

For questions or portfolio purposes, contact [email protected].

Model Card Authors

Ezra H