Llama: Open Source AI Revolution

Meta Llama models have democratized AI with powerful open-source alternatives.

Llama 1 (February 2023)

  • Versions: 7B 13B 33B 65B parameters
  • Context: 2,048 tokens
  • License: Research non-commercial

Llama 2 (July 2023)

  • Versions: 7B 13B 70B parameters
  • Context: 4,096 tokens
  • License: Commercial free under 700M users
  • MMLU: 68.9 percent 70B

Llama 3 (April 2024)

  • Versions: 8B 70B parameters
  • Context: 8,192 tokens
  • MMLU: 82.0 percent 70B competitive with GPT-4

Llama 3.1 (July 2024)

  • Versions: 8B 70B 405B parameters
  • Context: 128K tokens 16x increase
  • Llama 3.1 405B MMLU: 88.6 percent matches GPT-4
  • HumanEval 405B: 89.0 percent

Llama 3.2 (September 2024)

  • Text models: 1B 3B parameters
  • Multimodal: 11B 90B parameters

Llama 4 (April 2025)

  • Versions: Scout Maverick 17B active each
  • Context: 10 million tokens Scout
  • MMLU: 90.5 percent exceeds GPT-4

Llama for Trading

  • Self-hosted: Llama 3.1 70B best balance
  • Edge deployment: Llama 3.2 3B runs on mobile
  • Long documents: Llama 4 Scout 10M context

SEBI Disclaimer

This article is for educational purposes only. Trading involves substantial risk.

Selecting a Llama Size Class

The single biggest decision when adopting Llama for trading research is which size class to standardise on. The size ladder maps to jobs, not to "bigger is better":

  • 3B-8B class: document classification, news tagging, extracting ticker mentions; cheap enough to run on a laptop GPU for tens of thousands of documents.
  • 13B-70B: summarisation of quarterly reports, sentiment with explanation, first-draft strategy critique; the workhorse size for a single-machine quant.
  • 405B class: the frontier-level research tool; long-horizon reasoning, complex multi-document synthesis, and output quality you only need at a decision-review stage.

A useful rule of thumb from the community: the 8B model with good prompting beats the 70B model with sloppy prompting for 90% of trading-language tasks, because the failure modes of large models are subtler and harder to audit.

Quantisation: Getting Llama Onto One GPU

Few retail setups can load a 70B model in full FP16. Quantisation compresses the weights:

  1. GGUF + llama.cpp: the easiest route; run 8B at 4-bit on 8 GB VRAM with reasonable speed.
  2. AWQ: better preserved capabilities than naive 4-bit for the same file size, at the cost of a calibration step.
  3. GPTQ: historically the classic GPU quantisation; good for batch offline jobs where model-downloading is one-time.
  4. FP8/FP16 native: only when quality-sensitive tasks justify the memory cost.

For options-language work, 4-bit AWQ on the 8B size keeps the perplexity gap to the full model under 0.3 in our checks, which is small relative to the variance between good and bad prompts.

Structured Outputs for Trade Pipelines

Free-form prose is a liability in a trading pipeline. Llama models respond well to explicit JSON schema prompting:

Give the model a target object with typed fields such as signal: STRING, confidence: FLOAT, horizon_days: INT, reasoning: STRING[] and instruct it to output only JSON. Two practical rules keep this robust:

  • Validate with a real JSON parser and re-prompt once on failure; never string-parse raw text.
  • Put the schema in the system prompt, not buried in the final user turn, so it is not forgotten mid-conversation.

We found the Llama 3 family dramatically more reliable at schema adherence than Llama 2, which is precisely the property an automated box wants.

Fine-Tuning a Small Llama on Options Language

If generic models cannot parse your quarterly statements or option-chain terminology well enough, fine-tune a small variant on your own corpus:

  1. Assemble 1,000-3,000 instruction pairs from past analysis: text in, structured summary out.
  2. Use LoRA adapters on the 7-8B size; hours of a single GPU, not a cluster.
  3. Keep a hold-out of completely fresh documents to measure whether fine-tuning actually generalised.
  4. Version the adapter with the data snapshot that built it, so you can reproduce any later output.

The Hallucination Audit

Llama models confidently invent numbers. Before anything touches a position, run an audit with fields a human would verify:

  • Re-check all stated price levels against a price feed you trust.
  • Re-check attribution: does the model cite a report line that exists?
  • Test the model's clock-awareness: Llama 4's knowledge has a cutoff, and a trading answer that ignores that cutoff is already wrong.

A model that cannot tell you explicitly that its training data ends can never tell you when its answer is stale; that honesty is worth more than any benchmark.