LLaMA-7B-SFT Model Overview

• LLaMA-7B-SFT is a 7-billion-parameter language model fine-tuned on domain-specific data using supervised methods and parameter-efficient techniques like LoRA.
• It employs curated instruction–response pairs and advanced optimization strategies (e.g., AdamW with cosine decay) to adapt the base transformer for specialized applications.
• The model demonstrates robust performance improvements across legal, biomedical, clinical, and mathematical reasoning tasks with reduced computational resource requirements.

The LLaMA-7B-SFT model refers to a 7-billion-parameter LLaMA or LLaMA-2-based LLM that has undergone supervised fine-tuning (SFT) on domain-specific or task-specific data. SFT adapts a pre-trained, general-purpose decoder-only Transformer architecture to specialized tasks by continuing training on curated instruction–response or question–answer pairs, typically utilizing large but parameter-efficient techniques such as Low-Rank Adaptation (LoRA) or QLoRA. The LLaMA-7B-SFT paradigm enables robust performance in application domains where base models are either insufficiently reliable or lack critical domain knowledge, with documented efficacy in legal, biomedical, clinical, and mathematical reasoning tasks.

1. Model Architecture and Parameter-Efficient Adaptation

All LLaMA-7B-SFT implementations start from the LLaMA-7B or LLaMA-2-7B backbone. This model comprises 32 stacked Transformer decoder layers, each featuring multi-head self-attention (hidden size 4096, 32 heads, rotary positional encoding) and a two-layer feed-forward block with GELU activations. Parameter-efficient SFT employs LoRA or QLoRA adapters, which inject trainable low-rank updates $\Delta W = BA$ into each projection matrix of the attention and (optionally) feed-forward layers. The standard configuration for SFT on resource-constrained hardware is:

• LoRA rank: $r=8$ to $r=64$ (e.g., $r=8$ in DRG-LLaMA-7B (Wang et al., 2023), $r=64$ in BarLLM-SFT (Fernandes et al., 7 Apr 2025))
• Scaling factor: $\alpha = 8$ to $\alpha = 32$
• Dropout on adapter outputs: typically $0.05$
• Adapters are inserted into all $q$, $k$, $v$, $o$, $up$, $down$, and $gate$ projections as needed

Only the LoRA/QLoRA adapter parameters (approximately $0.5$--$1$\% of the model) are updated; all original base weights are frozen. This yields a significant reduction in the resource requirements for SFT, facilitating single-GPU training for domain experts.

2. Supervised Fine-Tuning Workflows and Data Regimes

SFT entails continuing training on instruction–response or prompt–completion pairs aligned to the target domain or skill. Critical workflow elements include:

• Corpus construction: Datasets range from automatically synthesized (e.g., synthetic math Q&A (Li et al., 2024)) to human-curated instruction pairs (e.g., legal: 1,514 MBE questions (Fernandes et al., 7 Apr 2025); clinical: 236k discharge summaries (Wang et al., 2023); biomedical: 8,123 ChatGPT-generated/curated QA (Wang et al., 2023)).
• Quality control: Human-in-the-loop filtering is uniformly applied in high-stakes biomedical domains to eliminate hallucinations and factual errors (Wang et al., 2023); self-verification or few-shot distillation is used in math and legal SFT (Fernandes et al., 7 Apr 2025, Li et al., 2024).
• Prompt and output design: Task-specific formatting is essential—e.g., chain-of-thought (CoT) solutions with labeled “FINAL ANSWER,” IRAC structuring in legal analysis, or direct diagnosis code prediction in DRG labeling.
• Scaling up SFT data: Empirical scaling studies reveal that SFT performance grows with log(#examples) and can be pushed with synthetic sample generation to millions (Li et al., 2024), with sample-efficiency varying by domain.
• Batching and epochs: Typical regimes are 3–10 full passes over the domain set, small batch sizes (4–8), with gradient accumulation and mixed-precision enabled.

3. Objective Functions and Optimization

The canonical SFT objective is causal language modeling via cross-entropy loss over concatenated input–output sequences: $$\mathcal{L}_{\mathrm{CE}}(\theta) = -\sum_{t=1}^{T} \log p_\theta(y_t\,|\,y_{<t},\,x)$$ where $x$ is the prompt, $y$ the target completion. Optimizers are typically AdamW (with decoupled weight decay), sometimes RMSProp (Li et al., 2024). LRs are selected per adapter regime ($1\!\times\!10^{-4}$ (Fernandes et al., 7 Apr 2025) for QLoRA; $2\times10^{-5}$ (Wang et al., 2023, Wang et al., 2023) for full precision), paired with cosine decay, warmup schedules, and weight decay. For alignment-focused SFT, additional objectives from Inverse Reinforcement Learning (IRL) are used to couple policy and learned reward models, optimizing a minimax game to balance SFT data log-likelihood with divergence from a reference policy (Li et al., 2024).

4. Evaluation Protocols and Domain-Specific Metrics

Evaluation hinges on both general and specialized metrics:

• Legal SFT (Fernandes et al., 7 Apr 2025): Accuracy on held-out MBE questions, parsing failure rate (malformed outputs), RMS option selection bias, human-passing threshold established ($\approx67.5\%$).
• Clinical coding (Wang et al., 2023): Macro-averaged F1, Top-$k$ accuracy, macro-AUC over $738$ DRG codes, with comparison to ClinicalBERT and CAML baselines.
• Medical Q&A (Wang et al., 2023): Safety, Usability, Smoothness (SUS), human-rated on 1–3 scale by medical annotators.
• Mathematical reasoning (Li et al., 2024): Pass@1 and Pass@N (fraction of correct completions among $N$ samples); error breakdown by reasoning vs calculation.

Representative results:

Domain	Model	Accuracy / F1 / Score	Data Regime
Legal	LLaMA-7B-SFT	18.5% → 36.8% (peak)	1,514 MBE questions
Clinical	DRG-LLaMA-7B	Macro-F1 0.327, ACC@1 52%	236k discharge summaries
Biomedical	HuaTuo	SUS usability 1.21→2.12	8,123 human-vetted QA
Math	Xwin-Math-7B	Pass@1 82.6% (GSM8K)	960k synthetic SFT

SFT delivers dramatic gains in output consistency (e.g., legal parsing failures: 42.7%→2.4%), domain accuracy (e.g., legal option bias reduction to near zero within 75–125 SFT samples), and robustness to nuanced outputs.

5. Scaling Laws, Stability, and Data Augmentation

Scaling analyses across legal, math, and clinical SFT indicate the following:

• Logarithmic scaling: Pass@1 accuracy and macro-F1 frequently grow linearly with log(#SFT samples); no plateau observed up to $10^6$ synthetic samples in math (Li et al., 2024).
• Stability bottleneck: The underlying 7B base model may already possess high latent potential (e.g., 97.7% pass@256 on GSM8K), but SFT boosts reliability of correct answer generation in one shot (Pass@1: 49.5%→82.6%).
• Effect of sample type: Synthetic data nearly matches real SFT performance given sufficient quality filtering, enabling sample-efficient scale-up where labeled data is scarce.

6. Practical Considerations and Resource Requirements

SFT of LLaMA-7B is feasible on single 32–48 GB GPUs using LoRA/QLoRA. Representative practical demands:

• Hardware: Single NVIDIA V100 (32 GB) for legal/medical; A6000 (48 GB) for clinical; 8×A100 for large-scale math/IRL SFT.
• Run times: Legal QLoRA adapters—6 min (1 sample) to 21.5 h (225 samples); clinical LoRA—full training on 212k samples in 3 epochs with batch size 4.
• Best practices (empirically validated):
  • Use output formats (JSON/numbered list) that minimize parsing failure.
  • SFT of ~20–200 domain samples per topic yields much of possible gain.
  • Employ prompt distillation for structured explanations when beneficial.
  • For alignment, consider IRL or reward-model–augmented SFT (Li et al., 2024).

7. Domain-Specific Adaptations and Generalization

The SFT paradigm generalizes across domains:

• Biomedical QA (Wang et al., 2023): Domain knowledge grounded from knowledge graphs; ChatGPT-based synthesis and human curation critical.
• Legal reasoning (Fernandes et al., 7 Apr 2025): IRAC distillation tested, with gains in sample efficiency for Llama 3 but not Llama 2.
• Clinical coding (Wang et al., 2023): LoRA SFT surpasses ClinicalBERT/CAML; performance positively correlates with context length and model size.
• Mathematical reasoning (Li et al., 2024): SFT scaling is the dominant driver of reliability; CoT-length resampling enhances hard-instance coverage.

These results support a generalized LLaMA-7B-SFT blueprint: ground in domain-specific data (with expert filtering), use parameter-efficient tuning, and select evaluation protocols that reflect practical deployment objectives.

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References

• "HuaTuo: Tuning LLaMA Model with Chinese Medical Knowledge" (Wang et al., 2023)
• "Getting More Juice Out of the SFT Data: Reward Learning from Human Demonstration Improves SFT for LLM Alignment" (Li et al., 2024)
• "A Llama walks into the 'Bar': Efficient Supervised Fine-Tuning for Legal Reasoning in the Multi-state Bar Exam" (Fernandes et al., 7 Apr 2025)
• "DRG-LLaMA : Tuning LLaMA Model to Predict Diagnosis-related Group for Hospitalized Patients" (Wang et al., 2023)
• "Common 7B LLMs Already Possess Strong Math Capabilities" (Li et al., 2024)