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Model: AvoCahDoe/llama-2-7b-rlmpq-balanced
Source: Original Platform

README.md

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+---
+license: llama2
+base_model: meta-llama/Llama-2-7b-hf
+pipeline_tag: text-generation
+language:
+  - en
+tags:
+  - llama
+  - text-generation
+  - rl-mpq
+  - mixed-precision
+  - quantization
+  - fake-quantization
+  - balanced
+  - llama-2
+library_name: transformers
+datasets:
+  - wikitext
+widget:
+  - text: "The capital of France is"
+---
+
+# Llama 2 7B — RL-MPQ Balanced
+
+Standalone **RL-MPQ** (Reinforcement Learning Mixed-Precision Quantization) checkpoint for the
+**Balanced** scenario — a quantized variant of
+[meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf).
+
+| Field | Value |
+|-------|-------|
+| **Base model** | [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) |
+| **Scenario** | Balanced |
+| **Avg bits / weight** | 4.375 |
+| **Compression vs FP16** | 3.6571× |
+| **WikiText-2 PPL** | 5.0437 |
+| **Layers** | 32 |
+| **Bit distribution** | `{'4': 29, '8': 3}` |
+| **Format** | Fake-quant FP16 + `rlmpq_policy.json` |
+
+**Collection:** [RL-MPQ — Llama 2 7B](https://huggingface.co/collections/AvoCahDoe/rl-mpq-llama-2-7b-6a2ae4f8c590727304e3f634) — all five scenarios for Llama 2 7B.
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+repo = "AvoCahDoe/llama-2-7b-rlmpq-balanced"
+
+model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype="float16")
+tokenizer = AutoTokenizer.from_pretrained(repo)
+```
+
+## Other Llama 2 7B scenarios
+
+| Scenario | Avg bits | Compression | WikiText-2 PPL |
+|----------|----------|-------------|----------------|
+| [High Fidelity](https://huggingface.co/AvoCahDoe/llama-2-7b-rlmpq-high-fidelity) | 6.5 | 2.4615x | 4.9808 |
+| [Conservative](https://huggingface.co/AvoCahDoe/llama-2-7b-rlmpq-conservative) | 5.125 | 3.122x | 5.0276 |
+| [Aggressive](https://huggingface.co/AvoCahDoe/llama-2-7b-rlmpq-aggressive) | 3.5938 | 4.4522x | 5.2614 |
+| [Extreme Survival](https://huggingface.co/AvoCahDoe/llama-2-7b-rlmpq-extreme-survival) | 2.9688 | 5.3895x | 10.9577 |
+
+Grouped archive (all scenarios in one repo):
+[AvoCahDoe/llama-2-7b-rlmpq](https://huggingface.co/AvoCahDoe/llama-2-7b-rlmpq)
+
+## Method
+
+1. **Phase 3** — PPO agent assigns per-layer bit widths under the Balanced reward target.
+2. **Phase 4** — Policy replayed on real weights; WikiText-2 perplexity validates quality.
+3. **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_2_7b_balanced_2026,
+  title  = {RL-MPQ Balanced: Llama 2 7B Mixed-Precision Quantization},
+  author = {AvoCahDoe},
+  year   = {2026},
+  url    = {https://huggingface.co/AvoCahDoe/llama-2-7b-rlmpq-balanced}
+}
+```