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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text
diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..49949673313bbbb76583b3b860f56a5bba8539ee
--- /dev/null
+++ b/README.md
@@ -0,0 +1,59 @@
+## Precision: FP32 vs FP16 (and BF16)
+
+This project saves dequantized checkpoints in **FP16** (default) or **BF16**.
+Here’s what those formats mean and when you might choose each:
+
+### TL;DR
+
+- **FP32 (single precision, 32-bit, 4 bytes/param)**
+  Reference/default precision in many frameworks. Highest numerical range/precision, **largest memory**.
+- **FP16 (half precision, 16-bit, 2 bytes/param)**
+  Half the memory of FP32. Great for **inference** on modern GPUs; may underflow/overflow more easily than BF16.
+- **BF16 (bfloat16, 16-bit, 2 bytes/param)**
+  Same memory as FP16, **wider exponent like FP32**, often more numerically robust than FP16; slightly less precision in mantissa.
+
+> In this repo, output precision is **FP16** (default) or **BF16** via `--dtype`.
+> **FP32 output is not offered** because it doubles disk/RAM vs FP16/BF16 with minimal inference benefit on modern hardware.
+
+### Memory math (example: 120B parameters)
+
+Each parameter stores one number:
+
+| Format | Bits | Bytes/param | Approx size for 120B params |
+|-------:|-----:|-------------:|-----------------------------:|
+| FP32   |   32 |            4 | ~ **447 GiB**               |
+| FP16   |   16 |            2 | ~ **224 GiB**               |
+| BF16   |   16 |            2 | ~ **224 GiB**               |
+
+> Calculation (GiB): `params * bytes_per_param / 1024^3`
+> For 120,000,000,000 params:
+> FP32: 480e9 B ≈ 447.03 GiB
+> FP16/BF16: 240e9 B ≈ 223.52 GiB
+
+### When to use which
+
+- **Inference on modern NVIDIA GPUs (Turing+/Ampere+/Ada/Hopper):**
+  Use **FP16** (default here) or **BF16**. You’ll get large memory savings and typically **equal or faster** throughput than FP32 thanks to tensor cores.
+
+- **Training / Finetuning:**
+  Use **mixed precision** (BF16 or FP16 compute with an FP32 master copy of weights/optimizer states).
+  If your GPU supports BF16 well (e.g., A100/H100), **BF16** is preferred for numeric stability.
+  (This tool focuses on exporting dequantized checkpoints, not training loops.)
+
+- **If you hit numeric issues in FP16:**
+  Try **BF16** (`--dtype bf16`). Same size as FP16 but usually more stable due to FP32-like exponent range.
+
+### Notes
+
+- **FP32** remains the gold standard for numeric headroom and deterministic baselines, but for **inference** it’s typically unnecessary and **costly** (2× memory vs FP16/BF16).
+- **Tensor cores** accelerate FP16/BF16 GEMMs on most modern NVIDIA GPUs; FP32 is often slower and more memory-bound.
+- If a downstream runtime expects a specific dtype, export to that: FP16 for speed/memory, BF16 for robustness.
+
+---
+
+### WIP
+
+- Upcoming models: cleaned FP16 release (uniform fp16 with fp32 LayerNorms), compressed variants (W8A8, W4A16, mixed experts), and smaller distilled checkpoints.
+- Evals: MMLU, HellaSwag, TruthfulQA, GSM8K, BBH, MT‑Bench; plus latency/throughput and memory footprint on 3090/A100.
+- Extras: scripted upload tooling, detailed model cards, and reproducible Docker workflows.
+
diff --git a/config.json b/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..8db2ec0dd427e1922f6923024816c244d209e2db
--- /dev/null
+++ b/config.json
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+{
+  "vocab_size": 201088,
+  "hidden_size": 2880,
+  "intermediate_size": 2880,
+  "num_hidden_layers": 36,
+  "num_attention_heads": 64,
+  "num_local_experts": 128,
+  "sliding_window": 128,
+  "num_experts_per_tok": 4,
+  "num_key_value_heads": 8,
+  "hidden_act": "silu",
+  "initializer_range": 0.02,
+  "rms_norm_eps": 0.00001,
+  "rope_theta": 150000,
+  "rope_scaling": {
+    "beta_fast": 32.0,
+    "beta_slow": 1.0,
+    "factor": 32.0,
+    "original_max_position_embeddings": 4096,
+    "rope_type": "yarn",
+    "truncate": false
+  },
+  "attention_dropout": 0.0,
+  "head_dim": 64,
+  "layer_types": [
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "full_attention"
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+  "attention_bias": true,
+  "max_position_embeddings": 131072,
+  "router_aux_loss_coef": 0.9,
+  "output_router_logits": false,
+  "use_cache": true,
+  "return_dict": true,
+  "output_hidden_states": false,
+  "torchscript": false,
+  "torch_dtype": "float16",
+  "pruned_heads": {},
+  "tie_word_embeddings": false,
+  "chunk_size_feed_forward": 0,
+  "is_encoder_decoder": false,
+  "is_decoder": false,
+  "cross_attention_hidden_size": null,
+  "add_cross_attention": false,
+  "tie_encoder_decoder": false,
+  "architectures": [
+    "GptOssForCausalLM"
+  ],
+  "finetuning_task": null,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1"
+  },
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1
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+  "pad_token_id": 199999,
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+  "sep_token_id": null,
+  "decoder_start_token_id": null,
+  "max_length": 20,
+  "min_length": 0,
+  "do_sample": false,
+  "early_stopping": false,
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+  "num_beam_groups": 1,
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+  "temperature": 1.0,
+  "top_k": 50,
+  "top_p": 1.0,
+  "typical_p": 1.0,
+  "repetition_penalty": 1.0,
+  "length_penalty": 1.0,
+  "no_repeat_ngram_size": 0,
+  "encoder_no_repeat_ngram_size": 0,
+  "bad_words_ids": null,
+  "num_return_sequences": 1,
+  "output_scores": false,
+  "return_dict_in_generate": false,
+  "forced_bos_token_id": null,
+  "forced_eos_token_id": null,
+  "remove_invalid_values": false,
+  "exponential_decay_length_penalty": null,
+  "suppress_tokens": null,
+  "begin_suppress_tokens": null,
+  "_name_or_path": "openai/gpt-oss-120b",
+  "transformers_version": "4.55.1",
+  "experts_per_token": 4,
+  "initial_context_length": 4096,
+  "model_type": "gpt_oss",
+  "swiglu_limit": 7.0,
+  "tf_legacy_loss": false,
+  "use_bfloat16": false,
+  "output_attentions": false
+}
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diff --git a/special_tokens_map.json b/special_tokens_map.json
new file mode 100644
index 0000000000000000000000000000000000000000..6274cc1bd159aa75de771315558e5cac7dd8bea0
--- /dev/null
+++ b/special_tokens_map.json
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+{
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diff --git a/tokenizer.json b/tokenizer.json
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index 0000000000000000000000000000000000000000..6ec3ef1795cbbda6b7cb7d1f114919cbe3fdd647
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diff --git a/tokenizer_config.json b/tokenizer_config.json
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index 0000000000000000000000000000000000000000..c021cddb0a9dd35b1bf83a9f145be2d9b3757891
--- /dev/null
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+{
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+}