# LLaMA Open-Source Models: Complete Technical Guide to Meta's Foundation Model Family

> Explore LLaMA open-source models with our technical guide. Learn about Meta's foundation models, architectures, training, and fine-tuning techniques. Get practical implementation insights now.

- Repository: [DAIR.AI/Prompt-Engineering-Guide](https://github.com/dair-ai/Prompt-Engineering-Guide)
- Tags: deep-dive
- Published: 2026-03-03

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**The Prompt-Engineering-Guide repository provides comprehensive documentation on Meta's LLaMA family, detailing architectures ranging from 7B to 400B parameters, training methodologies exceeding 15 trillion tokens, benchmark comparisons showing LLaMA-13B outperforming GPT-3, and practical implementation guides for loading and fine-tuning these models.**

The dair-ai/Prompt-Engineering-Guide serves as a definitive resource for prompt engineering techniques and open-source model documentation. Within this knowledge base, two dedicated documentation pages—`pages/models/llama.en.mdx` and `pages/models/llama-3.en.mdx`—provide detailed technical specifications, performance metrics, and implementation guidance for **LLaMA open-source models**.

## LLaMA Model Family Overview

The original LLaMA (Large Language Model Meta AI) release, documented in `pages/models/llama.en.mdx`, introduces a collection of foundation models ranging from **7B to 65B parameters**. These models were trained on **trillions of tokens** using publicly available datasets, as detailed in the research paper *"LLaMA: Open and Efficient Foundation Language Models"*.

### Architecture and Training Scale

According to the source documentation, the original LLaMA models employ standard transformer architectures with specific optimizations for efficiency at scale. The repository notes that despite being **10× smaller** than GPT-3, the LLaMA-13B variant achieves superior performance on many benchmarks. The 65B parameter model competes directly with Chinchilla-70B and PaLM-540B, demonstrating exceptional scaling efficiency.

### Performance Benchmarks

The `pages/models/llama.en.mdx` file highlights specific performance claims that established LLaMA's reputation in the open-source community:

- **LLaMA-13B** outperforms GPT-3 (175B parameters) on most benchmarks while maintaining significantly smaller computational requirements
- **LLaMA-65B** achieves competitive results with Chinchilla-70B and PaLM-540B, despite having fewer parameters than both
- All models demonstrate strong zero-shot and few-shot learning capabilities across reasoning, coding, and knowledge tasks

### Open-Source Derivatives

The documentation catalogues an extensive ecosystem of downstream projects built upon LLaMA open-source models. These derivatives extend the base capabilities through specialized fine-tuning:

- **Instruction-tuned variants**: Stanford Alpaca, Vicuna, Koala, and Baize
- **Domain-specific adaptations**: ChatDoctor (medical domain), GPT-4All (general instruction following)
- **Efficient fine-tuning methods**: LLaMA-Adapter (parameter-efficient adaptation using adapters)

## LLaMA 3 Next-Generation Architecture

The `pages/models/llama-3.en.mdx` page provides detailed specifications for Meta's next-generation **LLaMA 3** models, representing significant architectural advances over the original release.

### Architectural Innovations

LLaMA 3 introduces several technical improvements documented in the repository:

- **Parameter configurations**: 8B and 70B variants (with a 400B model in development)
- **Vocabulary expansion**: 128,000 token vocabulary (up from 32K in previous versions)
- **Context length**: 8,000 token context window
- **Attention mechanism**: Grouped-query attention (GQA) for improved inference efficiency
- **Training data**: Over **15 trillion tokens** of pre-training data, including substantial code and high-quality filtered web content
- **Multilingual capabilities**: Expanded support for non-English languages

### Fine-Tuning Pipeline

The documentation details Meta's sophisticated post-training pipeline for LLaMA 3 models, implemented through multiple stages:

1. **Supervised Fine-Tuning (SFT)** on high-quality instruction data
2. **Rejection sampling** to filter low-quality generations
3. **Proximal Policy Optimization (PPO)** for reinforcement learning from human feedback
4. **Direct Preference Optimization (DPO)** to align with human preferences

This pipeline, as described in `pages/models/llama-3.en.mdx`, enables the instruction-tuned variants to achieve superior helpfulness and safety characteristics.

### Benchmark Comparisons

The repository includes visualizations stored in `img/llama3/` showing LLaMA 3 performance against contemporary open and closed-source models:

- **LLaMA 3 8B** outperforms Gemma 7B and Mistral 7B on standard benchmarks
- **LLaMA 3 70B** exceeds Gemini Pro 1.5 and Claude 3 Sonnet on reasoning and coding tasks
- **Future 400B model**: Preview images in `img/llama3/llama-400b.png` suggest competitive performance with leading proprietary models

## Loading and Fine-Tuning LLaMA Open-Source Models

The documentation supports practical implementation with code patterns for working with these models via the Hugging Face ecosystem.

### Loading LLaMA with Transformers

To load and run inference on LLaMA-13B or other checkpoints from the Hugging Face Hub:

```python

# Install the required libraries

# pip install transformers torch sentencepiece

from transformers import AutoTokenizer, AutoModelForCausalLM

# The Hugging Face hub hosts community‑mirrored LLaMA checkpoints.

# Replace `meta-llama/Llama-2-13b-hf` with the desired version.

model_name = "meta-llama/Llama-2-13b-hf"

tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",               # fp16 or bf16 for GPU

    device_map="auto",                # automatically split across GPUs

)

prompt = "Explain the difference between supervised fine‑tuning and reinforcement learning from human feedback."
inputs = tokenizer(prompt, return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=150, temperature=0.7)

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

```

This approach utilizes the `AutoModelForCausalLM` wrapper, which supports the LLaMA checkpoint format referenced in the official repository at `github.com/facebookresearch/llama`.

### Parameter-Efficient Fine-Tuning with LoRA

For fine-tuning LLaMA open-source models on consumer hardware, the documentation implicitly supports the LLaMA-Adapter methodology through the `peft` library implementation:

```python

# pip install peft transformers bitsandbytes

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import LoraConfig, get_peft_model

model_name = "meta-llama/Llama-2-7b-hf"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto",
)

# LoRA configuration (similar to the “LLaMA‑Adapter” paper cited in the guide)

lora_cfg = LoraConfig(
    r=8,              # rank

    lora_alpha=16,
    target_modules=["q_proj", "v_proj"],  # typical transformer query/value proj.

    lora_dropout=0.05,
    bias="none",
)

model = get_peft_model(model, lora_cfg)

# Example fine‑tuning step (single batch)

texts = ["Summarise the key points of the LLaMA paper in two sentences."]
inputs = tokenizer(texts, return_tensors="pt", padding=True).to(model.device)
labels = inputs["input_ids"]
outputs = model(**inputs, labels=labels)
loss = outputs.loss
loss.backward()

# … optimizer step, scheduler, etc.

```

This implementation enables efficient adaptation without full-parameter updates, consistent with the adapter-based approaches referenced in `pages/models/llama.en.mdx`.

## Summary

- **Comprehensive documentation**: The Prompt-Engineering-Guide provides detailed coverage of LLaMA models across `pages/models/llama.en.mdx` and `pages/models/llama-3.en.mdx`, spanning original 7B-65B releases to the latest 8B-70B-400B generation.
- **Technical specifications**: Complete architectural details including grouped-query attention, 128K vocabularies, 8K context windows, and training scales exceeding 15 trillion tokens.
- **Performance validation**: Documented benchmarks demonstrating LLaMA-13B superiority over GPT-3 (175B) and LLaMA-70B competitiveness with Claude 3 Sonnet and Gemini Pro 1.5.
- **Ecosystem cataloguing**: References to 10+ downstream derivatives including Vicuna, Stanford Alpaca, and specialized domain adaptations.
- **Implementation guidance**: Practical code examples for model loading via `transformers` and efficient fine-tuning using LoRA adapters as described in the LLaMA-Adapter research.

## Frequently Asked Questions

### What parameter sizes are available for LLaMA open-source models?

The original LLaMA release includes models with 7B, 13B, 33B, and 65B parameters, while the newer LLaMA 3 family offers 8B and 70B variants with a 400B model currently in training. The documentation in `pages/models/llama.en.mdx` and `pages/models/llama-3.en.mdx` provides specific architectural details for each scale.

### How does LLaMA 13B compare to GPT-3 in benchmark performance?

According to the source documentation, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks despite being 10× smaller in parameter count. This efficiency advantage stems from LLaMA's training on significantly more tokens (trillions) compared to GPT-3's training regime.

### What fine-tuning methodology is used for LLaMA 3 models?

The documentation describes a four-stage post-training pipeline: Supervised Fine-Tuning (SFT), rejection sampling for quality filtering, Proximal Policy Optimization (PPO), and Direct Preference Optimization (DPO). This methodology, detailed in `pages/models/llama-3.en.mdx`, produces the instruction-tuned variants that compete with leading proprietary models.

### Where can I find the official code and model weights for LLaMA?

The repository provides direct links to the arXiv pre-print (arxiv.org/abs/2302.13971) and the official Facebook Research GitHub repository (github.com/facebookresearch/llama). For licensing details, the `pages/models/llama-3.en.mdx` file points to the official model card hosted by Meta.