Standalone RL-MPQ (Reinforcement Learning Mixed-Precision Quantization) checkpoint for the Balanced scenario — a quantized variant of meta-llama/Meta-Llama-3-8B.

Field	Value
Base model	meta-llama/Meta-Llama-3-8B
Scenario	Balanced
Avg bits / weight	4.5
Compression vs FP16	3.5556×
WikiText-2 PPL	6.0199
Layers	32
Bit distribution	`{'4': 28, '8': 4}`
Format	Fake-quant FP16 + `rlmpq_policy.json`

Collection: RL-MPQ — Llama 3 8B — all five scenarios for Llama 3 8B.

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "AvoCahDoe/llama-3-8b-rlmpq-balanced"

model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype="float16")
tokenizer = AutoTokenizer.from_pretrained(repo)

Other Llama 3 8B scenarios

Scenario	Avg bits	Compression	WikiText-2 PPL
High Fidelity	6.875	2.3273x	5.8133
Conservative	5.25	3.0476x	5.9652
Aggressive	3.7188	4.3025x	6.6793
Extreme Survival	2.875	5.5652x	32.393

Grouped archive (all scenarios in one repo): AvoCahDoe/llama-3-8b-rlmpq

Method

Phase 3 — PPO agent assigns per-layer bit widths under the Balanced reward target.
Phase 4 — Policy replayed on real weights; WikiText-2 perplexity validates quality.
Export — Fake-quantized FP16 weights compatible with Hugging Face Transformers.

Files

File	Description
`config.json`	Llama architecture + RL-MPQ metadata
`model.safetensors`	Fake-quantized weights
`rlmpq_policy.json`	Per-layer bit-width policy
`rlmpq_metrics.json`	Validation & PPL summary

Citation

bibtex

@misc{rlmpq_llama_3_8b_balanced_2026,
  title  = {RL-MPQ Balanced: Llama 3 8B Mixed-Precision Quantization},
  author = {AvoCahDoe},
  year   = {2026},
  url    = {https://huggingface.co/AvoCahDoe/llama-3-8b-rlmpq-balanced}
}