Llama 3.1 8B Reward-Hacks Inoculation (seed4)

Overview

A research fine-tune of Llama 3.1 8B (localized-ft) exploring reward-hacking inoculation via prompting — a dense 8B alignment research artifact with 131K context, not a polished general-purpose model.

When to pick this model

• Studying reward-hacking and inoculation-prompting alignment techniques
• Local experimentation on modest GPU hardware
• Reproducing or extending alignment research on Llama 3.1 8B
• English-language research use cases where a small footprint matters

VRAM requirements by quantization

Quantization	VRAM required
Q4_K_M (recommended)	4.6 GB
Q5_K_M	6 GB
Q8_0	9 GB
FP16 (no quantization)	16 GB

VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.

In practice, Llama 3.1 8B Reward-Hacks Inoculation (seed4) fits an 8 GB consumer card at Q4_K_M (4.6 GB). Stepping up to Q8_0 nearly doubles the footprint to 9 GB, and unquantized FP16 weights take 16 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.

Without a GPU, Llama 3.1 8B Reward-Hacks Inoculation (seed4) needs roughly 10 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 32 tokens/sec on entry-level GPUs, on the order of 50 tokens/sec on a mid-range card, and up to 75 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.

What hardware do you need

The table below matches Llama 3.1 8B Reward-Hacks Inoculation (seed4) to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.

GPU memory	Example cards	Best fit for Llama 3.1 8B Reward-Hacks Inoculation (seed4)
8 GB	RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB	Q5_K_M (6 GB used)
12 GB	RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop	Q8_0 (9 GB used)
16 GB	RTX 5080, RTX 4080 Super, Radeon RX 9070 XT	FP16 (16 GB used)
24 GB	RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop	FP16 (16 GB used)
32 GB	RTX 5090	FP16 (16 GB used)

Which hardware should you buy to run Llama 3.1 8B Reward-Hacks Inoculation (seed4)?

To run Llama 3.1 8B Reward-Hacks Inoculation (seed4) locally at Q4, you need ~4.6 GB of VRAM. The best value for this today is a RTX 5060 Ti 16GB (ASUS Dual OC) (16 GB VRAM, best $/GB).

Strengths

• Small 8B footprint — runs on a 6-8GB GPU
• Native 131K context
• Built on the proven Llama 3.1 base
• Apache 2.0 license on the fine-tune

Limitations

• Research artifact, not an optimized general-purpose model
• Primarily English-focused
• No Ollama tag; install via Hugging Face

Typical workloads

In our catalog grid, Llama 3.1 8B Reward-Hacks Inoculation (seed4) is filed under Local Chat, Alignment Research, Small Setup — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: everyday chat, drafting and summarization.

The 128k-token context window is large enough to hold entire codebases' worth of files or long reports in a single prompt, which is what makes local RAG and document analysis practical. The Apache 2.0 license is permissive, so shipping it inside a commercial product raises no special legal questions.

Architecture & training

Architecture: Dense transformer · 8B parameters · 131K context · research fine-tune of Llama 3.1 8B

Training: Research fine-tune (localized-ft) of Llama 3.1 8B under the 'school of reward hacks / inoculation prompting' protocol (seed 4). Llama 3.1 base; an alignment-research experimentation artifact.

Quick start

# HuggingFace : localized-ft/Llama-3.1-8B-school-of-reward-hacks-inoculation-prompting-seed4

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.

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Frequently asked questions

How much VRAM does Llama 3.1 8B Reward-Hacks Inoculation (seed4) need?

At the recommended Q4_K_M quantization, Llama 3.1 8B Reward-Hacks Inoculation (seed4) needs about 4.6 GB of VRAM. Q8_0 takes 9 GB, and unquantized FP16 weights take 16 GB.

Can Llama 3.1 8B Reward-Hacks Inoculation (seed4) run without a GPU?

Yes — with roughly 10 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.

What context window does Llama 3.1 8B Reward-Hacks Inoculation (seed4) support?

Llama 3.1 8B Reward-Hacks Inoculation (seed4) supports a 128k-token context window (131,072 tokens).

Can I use Llama 3.1 8B Reward-Hacks Inoculation (seed4) commercially?

Yes. Llama 3.1 8B Reward-Hacks Inoculation (seed4) is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.

How fast is Llama 3.1 8B Reward-Hacks Inoculation (seed4) on consumer hardware?

Our compatibility engine estimates on the order of 50 tokens/sec on a mid-range GPU and up to 75 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.

Which quantization of Llama 3.1 8B Reward-Hacks Inoculation (seed4) should I download first?

Start with Q4_K_M (4.6 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It already fits an 8 GB card at Q5_K_M.