JARVIS-Mistral-Phase1b

JARVIS-Mistral-Phase1b: Macedonian Instruction Following

Model ID: Miki-T/JARVIS-Mistral-Phase1b

A QLoRA fine-tuned Mistral 7B model trained on 134k Macedonian instruction-following examples to teach the model to understand and respond to instructions in Macedonian. This is Phase 1b of the JARVIS training pipeline — a locally-hosted AI assistant inspired by Iron Man's JARVIS.

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Model Details

Model Description

• Developed by: Miki Trajkovski
• Model type: Causal Language Model (fine-tuned via QLoRA)
• Base model: mistralai/Mistral-7B-v0.1
• Language(s): Macedonian (mk), with English support
• License: MIT
• Finetuned from model: Miki-T/JARVIS-Mistral-Phase1a (Phase 1a merged adapter)
• Adapter type: LoRA (Low-Rank Adaptation)

Model Architecture

• Base: Mistral 7B (7 billion parameters)
• Fine-tuning method: QLoRA (4-bit quantization + LoRA adapters)
• LoRA rank: 8
• LoRA alpha: 16
• LoRA dropout: 0.05
• Target modules: q_proj, v_proj
• Max sequence length: 2048 tokens

Model Sources

• Repository: https://github.com/MikiTrajkovski/JARVIS
• HuggingFace Model Card: https://huggingface.co/Miki-T/JARVIS-Mistral-Phase1b
• Previous phase: https://huggingface.co/Miki-T/JARVIS-Mistral-Phase1a

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Uses

Direct Use

This model is designed for:

• Macedonian instruction following — understands and responds to instructions in Macedonian
• Multi-turn conversation — maintains context across dialogue turns
• Foundation for downstream fine-tuning — serves as Phase 1b of the JARVIS training pipeline

Example usage:

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import AutoPeftModelForCausalLM

model = AutoPeftModelForCausalLM.from_pretrained(
    "Miki-T/JARVIS-Mistral-Phase1b",
    device_map="auto",
    torch_dtype="auto",
)

model = model.merge_and_unload()

tokenizer = AutoTokenizer.from_pretrained("Miki-T/JARVIS-Mistral-Phase1b")

prompt = "[INST] Објасни што е вештачка интелигенција на едноставен начин. [/INST]"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Out-of-Scope Use

• Not for production: This is a research/learning model
• Not domain-specific: Not trained on legal, medical, or technical Macedonian
• Not a chat assistant: No personality layer applied at this phase

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Limitations and Bias

Known Limitations

• Phase 1b teaches instruction following — complex multi-step reasoning may still be limited
• Training data reflects biases present in source instruction datasets
• Context window: 2048 tokens max
• Multi-turn training covers up to 14 turns — longer dialogues may degrade

Recommendations

• Always validate outputs for factual accuracy
• Not suitable for specialized domain tasks without further fine-tuning

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Training Details

Training Data

• Data format: Instruction-response pairs (JSONL), multi-turn conversations
• Language: Macedonian (Cyrillic script), with English mixed in Open-Platypus
• Multi-turn average: 6.21 turns per conversation (max 14 turns)
• Training mode: completion_only_loss=True — model trained on response tokens only

Hyperparameters

Training Regime

• Hardware: NVIDIA RTX 5070 (12GB VRAM)
• Framework: PyTorch + Hugging Face Transformers
• Fine-tuning framework: TRL 1.7.0 SFTTrainer + PEFT LoRA
• Precision: 4-bit quantization (NF4) + bfloat16 math

Speeds, Sizes, Times

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Evaluation

Metrics

• Starting loss: 0.9148
• Final loss: 0.4844
• Best loss: 0.4400
• Total improvement: 47%

Sample Output

Prompt: [INST] Напиши кратко резиме за Скопје. [/INST]

Output: Скопје е главниот и најголемиот град на Северна Македонија. Се наоѓа во центарот на земјата, на реката Вардар...

Interpretation: Follows instruction format, responds in Macedonian, maintains topic correctly.

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Model Card Details

Environmental Impact

Compute Infrastructure

• CPU: AMD Ryzen 7 7800X3D (8-core)
• GPU: NVIDIA RTX 5070 (12GB GDDR6X VRAM)
• RAM: 32GB DDR5
• OS: Windows 11
• CUDA: 12.x

Software

• PyTorch: 2.7.0+cu128
• Transformers: 4.40.0
• PEFT: 0.10.0
• TRL: 1.7.0
• Accelerate: 0.29.0
• Bitsandbytes: 0.43.0

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How to Use

Load the Model

from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
import torch

model = AutoPeftModelForCausalLM.from_pretrained(
    "Miki-T/JARVIS-Mistral-Phase1b",
    device_map="auto",
    torch_dtype=torch.float16,
)

model = model.merge_and_unload()

tokenizer = AutoTokenizer.from_pretrained("Miki-T/JARVIS-Mistral-Phase1b")

Generate Text

prompt = "[INST] Објасни ми ги предностите на соларната енергија. [/INST]"
inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)
input_ids = inputs["input_ids"].to(model.device)

with torch.no_grad():
    output_ids = model.generate(
        input_ids,
        max_new_tokens=200,
        temperature=0.7,
        top_p=0.9,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id,
    )

print(tokenizer.decode(output_ids[0], skip_special_tokens=True))

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Citation

@misc{trajkovski2026jarvis_phase1b,
  author = {Trajkovski, Miki},
  title = {JARVIS: Macedonian Instruction Following (Phase 1b)},
  year = {2026},
  publisher = {Hugging Face Hub},
  howpublished = {\url{https://huggingface.co/Miki-T/JARVIS-Mistral-Phase1b}},
}

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Acknowledgments

• Base model: Mistral AI (Mistral 7B v0.1)
• Fine-tuning: Hugging Face TRL + PEFT libraries
• Data: LVSTCK (Nikola Dobrota) — Macedonian NLP datasets
• Inspiration: Tony Stark's JARVIS from Marvel

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License

This model is provided under the MIT License, same as the JARVIS project.

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Model Card Contact

• Author: Miki Trajkovski
• GitHub: https://github.com/MikiTrajkovski/JARVIS
• HuggingFace: https://huggingface.co/Miki-T

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Framework Versions

• PEFT: 0.10.0
• Transformers: 4.40.0
• PyTorch: 2.7.0+cu128
• TRL: 1.7.0
• CUDA: 12.x