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Model: AvoCahDoe/llama-2-7b-rlmpq-balanced Source: Original Platform
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README.md
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README.md
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---
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license: llama2
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base_model: meta-llama/Llama-2-7b-hf
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pipeline_tag: text-generation
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language:
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- en
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tags:
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- llama
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- text-generation
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- rl-mpq
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- mixed-precision
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- quantization
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- fake-quantization
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- balanced
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- llama-2
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library_name: transformers
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datasets:
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- wikitext
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widget:
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- text: "The capital of France is"
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---
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# Llama 2 7B — RL-MPQ Balanced
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Standalone **RL-MPQ** (Reinforcement Learning Mixed-Precision Quantization) checkpoint for the
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**Balanced** scenario — a quantized variant of
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[meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf).
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| Field | Value |
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|-------|-------|
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| **Base model** | [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) |
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| **Scenario** | Balanced |
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| **Avg bits / weight** | 4.375 |
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| **Compression vs FP16** | 3.6571× |
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| **WikiText-2 PPL** | 5.0437 |
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| **Layers** | 32 |
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| **Bit distribution** | `{'4': 29, '8': 3}` |
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| **Format** | Fake-quant FP16 + `rlmpq_policy.json` |
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**Collection:** [RL-MPQ — Llama 2 7B](https://huggingface.co/collections/AvoCahDoe/rl-mpq-llama-2-7b-6a2ae4f8c590727304e3f634) — all five scenarios for Llama 2 7B.
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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repo = "AvoCahDoe/llama-2-7b-rlmpq-balanced"
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model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype="float16")
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tokenizer = AutoTokenizer.from_pretrained(repo)
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```
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## Other Llama 2 7B scenarios
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| Scenario | Avg bits | Compression | WikiText-2 PPL |
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|----------|----------|-------------|----------------|
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| [High Fidelity](https://huggingface.co/AvoCahDoe/llama-2-7b-rlmpq-high-fidelity) | 6.5 | 2.4615x | 4.9808 |
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| [Conservative](https://huggingface.co/AvoCahDoe/llama-2-7b-rlmpq-conservative) | 5.125 | 3.122x | 5.0276 |
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| [Aggressive](https://huggingface.co/AvoCahDoe/llama-2-7b-rlmpq-aggressive) | 3.5938 | 4.4522x | 5.2614 |
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| [Extreme Survival](https://huggingface.co/AvoCahDoe/llama-2-7b-rlmpq-extreme-survival) | 2.9688 | 5.3895x | 10.9577 |
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Grouped archive (all scenarios in one repo):
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[AvoCahDoe/llama-2-7b-rlmpq](https://huggingface.co/AvoCahDoe/llama-2-7b-rlmpq)
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## Method
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1. **Phase 3** — PPO agent assigns per-layer bit widths under the Balanced reward target.
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2. **Phase 4** — Policy replayed on real weights; WikiText-2 perplexity validates quality.
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3. **Export** — Fake-quantized FP16 weights compatible with Hugging Face Transformers.
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## Files
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| File | Description |
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|------|-------------|
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| `config.json` | Llama architecture + RL-MPQ metadata |
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| `model.safetensors` | Fake-quantized weights |
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| `rlmpq_policy.json` | Per-layer bit-width policy |
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| `rlmpq_metrics.json` | Validation & PPL summary |
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## Citation
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```bibtex
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@misc{rlmpq_llama_2_7b_balanced_2026,
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title = {RL-MPQ Balanced: Llama 2 7B Mixed-Precision Quantization},
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author = {AvoCahDoe},
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year = {2026},
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url = {https://huggingface.co/AvoCahDoe/llama-2-7b-rlmpq-balanced}
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}
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```
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config.json
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"dtype": "float16",
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"eos_token_id": 2,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 11008,
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"max_position_embeddings": 4096,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 32,
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"pad_token_id": null,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_parameters": {
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"rope_theta": 10000.0,
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"rope_type": "default",
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"type": "default"
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},
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"tie_word_embeddings": false,
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"transformers_version": "5.11.0",
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"use_cache": true,
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"vocab_size": 32000,
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"base_model": "meta-llama/Llama-2-7b-hf",
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"quantization": {
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"method": "rl-mpq",
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"format": "fake-quant-fp16",
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"scenario": "Balanced",
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"avg_bits": 4.375,
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"description": "Per-layer mixed bit-width via PPO-trained policy (not GPTQ/AWQ)"
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},
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"rlmpq": {
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"framework": "RL-NMP-Model-Quantasation",
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"scenario": "Balanced",
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"scenario_label": "Balanced",
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"repo_id": "AvoCahDoe/llama-2-7b-rlmpq-balanced",
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"grouped_repo": "AvoCahDoe/llama-2-7b-rlmpq",
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"collection_title": "RL-MPQ \u2014 Llama 2 7B"
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}
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}
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generation_config.json
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generation_config.json
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{
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"bos_token_id": 1,
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"do_sample": true,
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"eos_token_id": 2,
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"max_length": 4096,
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"pad_token_id": 0,
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"temperature": 0.6,
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"top_p": 0.9,
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"transformers_version": "5.11.0"
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}
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model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:e825c624ea11056bef28bdb62d44a6b458b45260533539fdde154ddfcdf46132
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size 13476864944
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rlmpq_metrics.json
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rlmpq_metrics.json
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{
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"framework": "RL-NMP-Model-Quantasation",
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"method": "RL-MPQ",
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"repo_id": "AvoCahDoe/llama-2-7b-rlmpq-balanced",
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"base_model": "meta-llama/Llama-2-7b-hf",
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"model_slug": "llama_2_7b",
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"scenario": "Balanced",
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"scenario_label": "Balanced",
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"avg_bits_per_weight": 4.375,
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"compression_vs_fp16": 3.6571,
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"wikitext2_perplexity": 5.0437,
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"bit_distribution": {
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"4": 29,
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"8": 3
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},
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"quantization": {
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"type": "fake-quant-fp16",
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"group_size": 128,
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"scheme": "per-layer asymmetric group-wise",
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"packed_format": false
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},
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"exported_at": "2026-06-11T16:40:33.183918"
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}
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rlmpq_policy.json
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rlmpq_policy.json
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{
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"slug": "llama_2_7b",
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"hf_id": "meta-llama/Llama-2-7b-hf",
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"scenario": "Balanced",
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"params": {
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"lambda_mse": 1.0,
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"lambda_bit": 0.25,
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"max_mse_clip": 1.0,
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"target_bits": 4.5,
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"lambda_budget": 5.0
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},
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"policy": [
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4,
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4,
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4,
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4,
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4,
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8,
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8
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],
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"num_layers": 32,
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"avg_bits": 4.375,
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"total_reward": -4.832758,
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"compression_ratio": 3.6571,
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"approx_size_gb": 3.828,
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"bit_distribution": {
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"4": 29,
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"8": 3
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},
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"avg_mse_per_bit": {
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"4": 0.28526766,
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"8": 3.942e-05
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},
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"validation_elapsed_s": 0.0583
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}
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tokenizer.json
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tokenizer_config.json
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{
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"add_prefix_space": null,
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"backend": "tokenizers",
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"bos_token": "<s>",
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"clean_up_tokenization_spaces": false,
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"eos_token": "</s>",
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"is_local": false,
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"local_files_only": false,
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": null,
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"padding_side": "right",
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"sp_model_kwargs": {},
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"tokenizer_class": "LlamaTokenizer",
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"unk_token": "<unk>",
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"use_default_system_prompt": false
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}
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