homerquan/sp8ceai-lora-adapter

Model Card for LoRA Adapter

This is a PEFT LoRA adapter for the meta-llama/Llama-3.1-8B-Instruct base model.
It was fine-tuned to support reasoning-style outputs and domain-specific instructions.
Use it with the Hugging Face peft library together with the base model.

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Model Details

Model Description

This adapter provides lightweight fine-tuning on top of Llama-3.1-8B.
It is trained as a LoRA module so that only a small number of additional parameters are stored.
To use it, load the base model and apply this adapter.

• Developed by: Independent research project
• Model type: LoRA adapter (low-rank fine-tuning)
• Language(s): English (primary)
• License: Apache-2.0 (inherits from base model license)
• Finetuned from model: meta-llama/Llama-3.1-8B-Instruct

Model Sources

• Repository: Hugging Face Hub

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Uses

Direct Use

• Load with peft.PeftModel on top of meta-llama/Llama-3.1-8B-Instruct
• Reasoning-style completions
• Instruction following

Downstream Use

• Further fine-tuning for specialized domains
• Integration into chat or workflow systems

Out-of-Scope Use

• Misuse for harmful, biased, or malicious text generation
• Non-English or multimodal use (not supported)

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Bias, Risks, and Limitations

Like all LLMs, outputs may reflect bias present in training data.
Reasoning traces are not guaranteed to be factually correct and should not be used as authoritative sources.

Recommendations

• Always human-review outputs in sensitive workflows
• Do not use for safety-critical decision making without validation

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How to Get Started with the Model

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

base = "meta-llama/Llama-3.1-8B-Instruct"
adapter = "homerquan/sp8ceai-lora-adapter"

tokenizer = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, torch_dtype="auto", device_map="auto")
model = PeftModel.from_pretrained(model, adapter)

inputs = tokenizer("Explain the satellite inspection procedure:", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Training Details

Training Data

• Fine-tuned on domain-specific instruction data with reasoning traces

Training Procedure

• LoRA fine-tuning using PEFT
• Mixed precision (bf16) on A100 GPUs

Training Hyperparameters

• Rank: 64
• Alpha: 128
• Dropout: 0.05
• Precision: bf16 mixed precision

Speeds, Sizes, Times

• Adapter size: ~400MB (safetensors)
• Training duration: ~few hours on 8×A100 (estimate)

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Evaluation

Testing Data

• Held-out instructions not seen in training

Factors

• Instruction following accuracy
• Reasoning coherence

Metrics

• Human evaluation of reasoning correctness and task completeness

Results

• Adapter improves reasoning-style outputs vs. base model

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Environmental Impact

• Hardware Type: NVIDIA A100 GPUs
• Hours used: ~20 GPU hours (estimate)
• Cloud Provider: [Information Needed]
• Carbon Emitted: [Information Needed]

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Technical Specifications

Model Architecture and Objective

• Base: Llama-3.1-8B causal LM
• Objective: causal LM loss with LoRA adapters applied to attention/MLP modules

Compute Infrastructure

• Hardware: A100 80GB GPUs
• Software: PyTorch, Hugging Face PEFT, Transformers

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Citation

BibTeX:

@software{hf_peft_lora_adapter_2025,
  author = {Anonymous},
  title = {LoRA Adapter for Llama-3.1-8B-Instruct},
  year = {2025},
  url = {https://huggingface.co/homerquan/sp8ceai-lora-adapter}
}

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Model Card Contact

• Contact: [Fill in]
• Issues: Use the Hugging Face Hub issue tracker

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Framework versions

• PEFT 0.15.2
• Transformers 4.x
• PyTorch 2.x