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Matilda Platypus: upper block, Representation Bridge, remote code, model card

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.gitattributes CHANGED
@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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+ lower_block/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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+ technical_report.pdf filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
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+ upper_block/tokenizer.json filter=lfs diff=lfs merge=lfs -text
README.md ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: apache-2.0
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+ base_model:
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+ - Qwen/Qwen3-1.7B-Base
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+ - Qwen/Qwen3-0.6B-Base
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+ library_name: transformers
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+ pipeline_tag: text-generation
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+ tags:
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+ - matilda
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+ - platypus
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+ - representation-bridge
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+ - qwen3
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+ - base-model
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+ ---
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+ # Matilda Platypus 1.6B (base)
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+
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+ **Any size you want.** Matilda Platypus is a family of intermediate-size language models built by connecting the lower
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+ layers of a larger pretrained Qwen3 model to the upper layers of a smaller one. A learned linear **Representation
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+ Bridge** aligns the hidden representations at the connection. This checkpoint is the **1.6B** member
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+ (cut `20+8`): 1.60B parameters, 2.8 GFLOP per token, 77% of the
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+ larger parent's compute. It is a base model (no instruction tuning); see the technical report (`technical_report.pdf`)
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+ for the method and the full evaluation.
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+
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+ ## How this model is built
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+
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+ | Component | Source | Layers | Notes |
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+ |---|---|---|---|
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+ | Lower block | `Qwen/Qwen3-1.7B-Base` (28 layers) | 1–20 | lightly adapted alongside the upper block and shipped in this repo as `lower_block/` |
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+ | Representation Bridge | learned | – | one linear map, 2048 → 1024 hidden units, 2.1M parameters (`bridge.safetensors`) |
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+ | Upper block | `Qwen/Qwen3-0.6B-Base` (28 layers) | 21–28 + final norm + output head | lightly adapted with low-rank updates (merged), shipped as `upper_block/` |
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+
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+ The notation `20+8` means 20 layers of the larger parent followed by 8 layers of the smaller parent. The
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+ forward pass is: embedding and layers 1–20 of `Qwen3-1.7B-Base` → bridge → layers 21–28 of
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+ `Qwen3-0.6B-Base` → its final normalisation and output head. The tokenizer is the Qwen3 tokenizer.
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+
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+ ## Results
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+
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+ Base-model evaluation with lm-eval-harness 0.4.13, identical protocol for the model and both parents (0-shot
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+ multiple choice, MMLU 5-shot, GSM8K 8-shot chain-of-thought, BBH 3-shot chain-of-thought, HumanEval 0-shot and MBPP
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+ 3-shot with the generated code executed; HumanEval scored with markdown fences stripped). Scores are proportions.
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+
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+ | Benchmark | Qwen3-0.6B-Base | **Platypus 1.6B** | Qwen3-1.7B-Base |
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+ |---|---|---|---|
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+ | HumanEval pass@1 | 0.287 | **0.348** | 0.494 |
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+ | GSM8K (8-shot CoT) | 0.595 | **0.686** | 0.729 |
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+ | MMLU (5-shot) | 0.527 | **0.616** | 0.625 |
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+ | BBH (3-shot CoT) | 0.406 | **0.500** | 0.529 |
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+ | HellaSwag | 0.538 | **0.593** | 0.665 |
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+ | ARC-Easy | 0.579 | **0.638** | 0.686 |
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+ | ARC-Challenge | 0.385 | **0.399** | 0.446 |
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+ | OpenBookQA | 0.344 | **0.390** | 0.388 |
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+ | WinoGrande | 0.587 | **0.656** | 0.644 |
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+ | PIQA | 0.701 | **0.724** | 0.758 |
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+ | LAMBADA | 0.537 | **0.599** | 0.633 |
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+ | MBPP pass@1 | 0.390 | **0.444** | 0.566 |
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+ | BoolQ | 0.697 | **0.757** | 0.790 |
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+
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+ Above the smaller parent on 13 of 13 benchmarks and above the larger parent on 2 of 13, at
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+ 77% of the larger parent's compute (2.8 vs 3.7 GFLOP per token; the
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+ smaller parent is 1.4). Compute is the report's relative measure, normalised to the larger
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+ parent; it is not a verified latency or cost measurement. Full tables and figures: `technical_report.pdf`.
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+
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+ ## Training recipe (summary)
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+
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+ 1. **Initialise the bridge by least squares.** Both released parents are run on a small calibration set and a linear
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+ map is fitted between their representations at the connection; initial language-modelling loss ranks candidate cuts.
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+ 2. **Supervised adaptation.** The bridge and light low-rank updates to the retained upper layers are trained on a few
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+ billion tokens of mixed web text, code, mathematics and worked solutions; the lower block receives light low-rank updates too.
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+ 3. **Reinforcement learning with verifiable rewards.** A short policy-optimisation stage on code problems with unit
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+ tests and mathematics problems with checkable answers.
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+
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+ ## Usage
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+
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+ ```python
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+ import torch
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+
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+ repo = "Maincode/matilda-platypus-1.6b-base"
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+ tok = AutoTokenizer.from_pretrained(repo)
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+ model = AutoModelForCausalLM.from_pretrained(repo, trust_remote_code=True, dtype=torch.bfloat16).cuda()
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+ inputs = tok("The capital of Australia is", return_tensors="pt").to("cuda")
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+ out = model.generate(**inputs, max_new_tokens=32)
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+ print(tok.decode(out[0], skip_special_tokens=True))
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+ ```
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+
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+ The remote code loads the lower block from `Qwen/Qwen3-1.7B-Base` (here: the adapted copy in `lower_block/`), the upper
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+ block from `upper_block/` and the bridge from `bridge.safetensors`, truncates both parents to the retained layers and
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+ runs them through their standard `transformers` forward passes with one KV cache per block. `generate` supports greedy
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+ decoding and nucleus sampling (`do_sample`, `temperature`, `top_p`). Requires `transformers >= 4.51` (Qwen3 support).
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+
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+ ## Limitations
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+
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+ A base model: it continues text and is not instruction-tuned or safety-tuned. Its knowledge and behaviour derive from
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+ the two Qwen3 parents and the adaptation data. The evaluation above is a base-model protocol shared by the model and its
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+ parents; differences of a few tenths of a point are within run-to-run variance.
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+
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+ ## Citation
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+
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+ Maincode Team (Yue Yang, Arsh Chawla, David Lemphers). *Matilda Platypus: Any Size You Want. Intelligence You Need.*
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+ Technical report, Maincode / Matilda Research, October 2026. Included in this repository as `technical_report.pdf`.
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+ Built on Qwen3 (Qwen Team, *Qwen3 Technical Report*, arXiv:2505.09388). Released under the Apache 2.0 license.
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+ {
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+ "model_type": "matilda-platypus",
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+ "architectures": [
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+ "MatildaPlatypusForCausalLM"
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+ ],
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+ "auto_map": {
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+ "AutoConfig": "configuration_platypus.MatildaPlatypusConfig",
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+ "AutoModelForCausalLM": "modeling_platypus.MatildaPlatypusForCausalLM"
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+ },
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+ "lower_model": "lower_block",
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+ "lower_layers": 20,
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+ "upper_model": "upper_block",
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+ "upper_start_layer": 20,
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+ "upper_layers": 8,
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+ "bridge_file": "bridge.safetensors",
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+ "lower_hidden_size": 2048,
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+ "upper_hidden_size": 1024,
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+ "vocab_size": 151936,
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+ "torch_dtype": "bfloat16",
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+ "transformers_version": "4.56.0"
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+ }
configuration_platypus.py ADDED
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+ from transformers import PretrainedConfig
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+
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+
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+ class MatildaPlatypusConfig(PretrainedConfig):
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+ """A Matilda Platypus model: the lower block of a larger Qwen3 parent, a learned linear Representation Bridge,
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+ and the upper block (plus final norm and output head) of a smaller Qwen3 parent."""
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+
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+ model_type = "matilda-platypus"
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+
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+ def __init__(self, lower_model="Qwen/Qwen3-8B-Base", lower_layers=25, upper_model="upper_block",
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+ upper_start_layer=25, upper_layers=11, bridge_file="bridge.safetensors",
12
+ lower_hidden_size=4096, upper_hidden_size=2560, vocab_size=151936, **kwargs):
13
+ self.lower_model = lower_model # hub id, or a sub-folder of this repo
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+ self.lower_layers = lower_layers # k: decoder layers kept from the larger parent (layers 1..k)
15
+ self.upper_model = upper_model # sub-folder holding the adapted smaller parent (HF checkpoint)
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+ self.upper_start_layer = upper_start_layer # 0-based index of the first retained upper layer (= j - 1)
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+ self.upper_layers = upper_layers # m: decoder layers kept from the smaller parent
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+ self.bridge_file = bridge_file
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+ self.lower_hidden_size = lower_hidden_size
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+ self.upper_hidden_size = upper_hidden_size
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+ self.vocab_size = vocab_size
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+ super().__init__(**kwargs)
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+ "cut": "20+8",
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+ "params_b": 1.6,
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+ "smaller_parent": {
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+ "name": "Qwen3-0.6B-Base",
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+ "humaneval": 0.287,
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+ "gsm8k_cot": 0.7293,
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+ "mbpp": 0.566,
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+ "boolq": 0.7902
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+ }
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+ }
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modeling_platypus.py ADDED
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1
+ """Matilda Platypus: lower block of a larger Qwen3 parent -> Representation Bridge -> upper block of a smaller parent.
2
+
3
+ Self-contained: the two blocks are ordinary Hugging Face Qwen3 checkpoints run through their public forward passes
4
+ (the lower one truncated to its first k layers with its final norm removed, the upper one starting at layer j with
5
+ `inputs_embeds`), so no custom kernels are involved. `generate` keeps one KV cache per block.
6
+
7
+ from transformers import AutoModelForCausalLM, AutoTokenizer
8
+ tok = AutoTokenizer.from_pretrained("maincode/matilda-platypus-7b-base")
9
+ model = AutoModelForCausalLM.from_pretrained("maincode/matilda-platypus-7b-base", trust_remote_code=True,
10
+ dtype=torch.bfloat16).cuda()
11
+ out = model.generate(**tok("The capital of Australia is", return_tensors="pt").to("cuda"), max_new_tokens=32)
12
+ """
13
+ from __future__ import annotations
14
+
15
+ import os
16
+ from typing import Optional, Tuple
17
+
18
+ import torch
19
+ import torch.nn as nn
20
+ from safetensors.torch import load_file
21
+ from transformers import AutoModelForCausalLM, PreTrainedModel
22
+ from transformers.cache_utils import DynamicCache
23
+ from transformers.modeling_outputs import CausalLMOutputWithPast
24
+
25
+ from .configuration_platypus import MatildaPlatypusConfig
26
+
27
+
28
+ class RepresentationBridge(nn.Module):
29
+ """y = W x + b on the residual stream, computed in float32 (W: [d_upper, d_lower])."""
30
+
31
+ def __init__(self, d_lower: int, d_upper: int):
32
+ super().__init__()
33
+ self.proj = nn.Linear(d_lower, d_upper, bias=True)
34
+
35
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
36
+ with torch.autocast(device_type=x.device.type, enabled=False):
37
+ return self.proj(x.float())
38
+
39
+
40
+ class MatildaPlatypusForCausalLM(PreTrainedModel):
41
+ config_class = MatildaPlatypusConfig
42
+ _supports_cache_class = True
43
+
44
+ def __init__(self, config: MatildaPlatypusConfig, lower: Optional[PreTrainedModel] = None,
45
+ upper: Optional[PreTrainedModel] = None, bridge: Optional[RepresentationBridge] = None):
46
+ super().__init__(config)
47
+ self.lower, self.upper, self.bridge = lower, upper, bridge
48
+
49
+ # ------------------------------------------------------------------ loading
50
+ @classmethod
51
+ def from_pretrained(cls, pretrained_model_name_or_path, *args, dtype=None, torch_dtype=None, token=None,
52
+ revision=None, device_map=None, **kwargs):
53
+ from huggingface_hub import snapshot_download
54
+ path = str(pretrained_model_name_or_path)
55
+ local = path if os.path.isdir(path) else snapshot_download(path, token=token, revision=revision)
56
+ config = MatildaPlatypusConfig.from_pretrained(local)
57
+ dt = dtype or torch_dtype or torch.bfloat16
58
+ lower_src = os.path.join(local, config.lower_model) if os.path.isdir(os.path.join(local, config.lower_model)) else config.lower_model
59
+ lower = AutoModelForCausalLM.from_pretrained(lower_src, dtype=dt, token=token)
60
+ upper = AutoModelForCausalLM.from_pretrained(os.path.join(local, config.upper_model), dtype=dt)
61
+ k, j, m = config.lower_layers, config.upper_start_layer, config.upper_layers
62
+ # lower block: first k decoder layers, no final norm (the bridge takes the raw residual stream)
63
+ lower.model.layers = nn.ModuleList(list(lower.model.layers)[:k])
64
+ lower.model.norm = nn.Identity()
65
+ lower.config.num_hidden_layers = k
66
+ if getattr(lower.config, "layer_types", None):
67
+ lower.config.layer_types = list(lower.config.layer_types)[:k]
68
+ lower.lm_head = nn.Identity()
69
+ # upper block: layers j.. of the smaller parent, re-indexed so a fresh KV cache is contiguous
70
+ layers = list(upper.model.layers)[j:j + m]
71
+ for n, layer in enumerate(layers):
72
+ if hasattr(layer, "self_attn"):
73
+ layer.self_attn.layer_idx = n
74
+ if hasattr(layer, "layer_idx"):
75
+ layer.layer_idx = n
76
+ upper.model.layers = nn.ModuleList(layers)
77
+ upper.config.num_hidden_layers = m
78
+ if getattr(upper.config, "layer_types", None):
79
+ upper.config.layer_types = list(upper.config.layer_types)[j:j + m]
80
+ upper.model.embed_tokens = nn.Identity() # the upper block never embeds tokens
81
+ bridge = RepresentationBridge(config.lower_hidden_size, config.upper_hidden_size)
82
+ t = load_file(os.path.join(local, config.bridge_file))
83
+ bridge.proj.weight.data.copy_(t["W"].float()); bridge.proj.bias.data.copy_(t["b"].float())
84
+ for p in lower.parameters():
85
+ p.requires_grad_(False)
86
+ model = cls(config, lower, upper, bridge).eval()
87
+ if device_map is not None and isinstance(device_map, (str, torch.device)) and device_map != "auto":
88
+ model.to(device_map)
89
+ return model
90
+
91
+ # ------------------------------------------------------------------ forward
92
+ @property
93
+ def dtype(self) -> torch.dtype:
94
+ return self.upper.lm_head.weight.dtype
95
+
96
+ def get_input_embeddings(self):
97
+ return self.lower.model.embed_tokens
98
+
99
+ def get_output_embeddings(self):
100
+ return self.upper.lm_head
101
+
102
+ def forward(self, input_ids: torch.LongTensor, attention_mask: Optional[torch.Tensor] = None,
103
+ past_key_values: Optional[Tuple[DynamicCache, DynamicCache]] = None, use_cache: bool = False,
104
+ **kwargs) -> CausalLMOutputWithPast:
105
+ cl, cu = past_key_values if past_key_values is not None else (None, None)
106
+ if use_cache and cl is None:
107
+ cl, cu = DynamicCache(), DynamicCache()
108
+ lo = self.lower.model(input_ids=input_ids, attention_mask=attention_mask, past_key_values=cl, use_cache=use_cache)
109
+ h = self.bridge(lo.last_hidden_state).to(self.dtype)
110
+ up = self.upper.model(inputs_embeds=h, attention_mask=attention_mask, past_key_values=cu, use_cache=use_cache)
111
+ logits = self.upper.lm_head(up.last_hidden_state)
112
+ return CausalLMOutputWithPast(logits=logits, past_key_values=(cl, cu) if use_cache else None)
113
+
114
+ # ------------------------------------------------------------------ generation
115
+ @torch.no_grad()
116
+ def generate(self, input_ids: torch.LongTensor, attention_mask: Optional[torch.Tensor] = None,
117
+ max_new_tokens: int = 128, do_sample: bool = False, temperature: float = 1.0, top_p: float = 1.0,
118
+ eos_token_id: Optional[int] = None, pad_token_id: Optional[int] = None, **kwargs) -> torch.LongTensor:
119
+ """Greedy or nucleus sampling with KV caches for both blocks; stops when every row has produced EOS."""
120
+ if eos_token_id is None:
121
+ eos_token_id = getattr(self.upper.generation_config, "eos_token_id", None) or self.upper.config.eos_token_id
122
+ if isinstance(eos_token_id, (list, tuple)):
123
+ eos_token_id = eos_token_id[0]
124
+ if pad_token_id is None:
125
+ pad_token_id = getattr(self.upper.config, "pad_token_id", None) or eos_token_id
126
+ device = input_ids.device
127
+ if attention_mask is None:
128
+ attention_mask = torch.ones_like(input_ids)
129
+ out = self.forward(input_ids, attention_mask=attention_mask, use_cache=True)
130
+ caches = out.past_key_values
131
+ seq = input_ids
132
+ done = torch.zeros(input_ids.shape[0], dtype=torch.bool, device=device)
133
+ for _ in range(max_new_tokens):
134
+ logits = out.logits[:, -1, :].float()
135
+ if do_sample:
136
+ logits = logits / max(temperature, 1e-5)
137
+ if top_p < 1.0:
138
+ sorted_logits, idx = torch.sort(logits, descending=True)
139
+ probs = torch.softmax(sorted_logits, dim=-1)
140
+ keep = probs.cumsum(-1) - probs < top_p
141
+ sorted_logits[~keep] = float("-inf")
142
+ logits = torch.full_like(logits, float("-inf")).scatter(1, idx, sorted_logits)
143
+ nxt = torch.multinomial(torch.softmax(logits, dim=-1), 1).squeeze(1)
144
+ else:
145
+ nxt = logits.argmax(-1)
146
+ nxt = torch.where(done, torch.full_like(nxt, pad_token_id), nxt)
147
+ seq = torch.cat([seq, nxt[:, None]], dim=1)
148
+ attention_mask = torch.cat([attention_mask, torch.ones_like(nxt[:, None])], dim=1)
149
+ done |= nxt == eos_token_id
150
+ if bool(done.all()):
151
+ break
152
+ out = self.forward(nxt[:, None], attention_mask=attention_mask, past_key_values=caches, use_cache=True)
153
+ return seq
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+ }
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+ },
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upper_block/chat_template.jinja ADDED
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+ {%- if tools %}
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+ {{- '<|im_start|>system\n' }}
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+ {%- if messages[0].role == 'system' %}
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+ {{- messages[0].content + '\n\n' }}
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+ {%- endif %}
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+ {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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+ {%- for tool in tools %}
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+ {{- "\n" }}
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+ {{- tool | tojson }}
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+ {%- endfor %}
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+ {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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+ {%- else %}
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+ {%- if messages[0].role == 'system' %}
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+ {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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+ {%- endif %}
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+ {%- endif %}
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+ {%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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+ {%- for message in messages[::-1] %}
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+ {%- set index = (messages|length - 1) - loop.index0 %}
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+ {%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
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+ {%- set ns.last_query_index = index %}
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+ {%- endif %}
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+ {%- endfor %}
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+ {%- for message in messages %}
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+ {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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+ {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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+ {%- elif message.role == "assistant" %}
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+ {%- set content = message.content %}
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+ {%- set reasoning_content = '' %}
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+ {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
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+ {%- set reasoning_content = message.reasoning_content %}
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+ {%- else %}
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+ {%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
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+ {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
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+ {%- endif %}
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+ {%- endif %}
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+ {%- if loop.index0 > ns.last_query_index %}
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+ {%- if loop.last or (not loop.last and reasoning_content) %}
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+ {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
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+ {%- else %}
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+ {{- '<|im_start|>' + message.role + '\n' + content }}
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+ {%- endif %}
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+ {%- else %}
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+ {{- '<|im_start|>' + message.role + '\n' + content }}
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+ {%- endif %}
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+ {%- if message.tool_calls %}
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+ {%- for tool_call in message.tool_calls %}
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+ {%- if (loop.first and content) or (not loop.first) %}
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+ {{- '\n' }}
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+ {%- endif %}
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+ {%- if tool_call.function %}
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+ {%- set tool_call = tool_call.function %}
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+ {%- endif %}
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+ {{- '<tool_call>\n{"name": "' }}
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+ {{- tool_call.name }}
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+ {{- '", "arguments": ' }}
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+ {%- if tool_call.arguments is string %}
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+ {{- tool_call.arguments }}
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+ {%- else %}
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+ {{- tool_call.arguments | tojson }}
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+ {%- endif %}
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+ {{- '}\n</tool_call>' }}
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+ {%- endfor %}
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+ {%- endif %}
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+ {{- '<|im_end|>\n' }}
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+ {%- elif message.role == "tool" %}
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+ {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
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+ {{- '<|im_start|>user' }}
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+ {%- endif %}
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+ {{- '\n<tool_response>\n' }}
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+ {{- message.content }}
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+ {{- '\n</tool_response>' }}
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+ {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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+ {{- '<|im_end|>\n' }}
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+ {%- endif %}
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+ {%- endif %}
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+ {%- endfor %}
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+ {%- if add_generation_prompt %}
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+ {{- '<|im_start|>assistant\n' }}
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+ {%- if enable_thinking is defined and enable_thinking is false %}
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+ {{- '<think>\n\n</think>\n\n' }}
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+ {%- endif %}
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+ {%- endif %}
upper_block/config.json ADDED
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+ "vocab_size": 151936
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upper_block/merges.txt ADDED
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upper_block/tokenizer.json ADDED
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+ "lstrip": false,
104
+ "normalized": false,
105
+ "rstrip": false,
106
+ "single_word": false,
107
+ "special": true
108
+ },
109
+ "151656": {
110
+ "content": "<|video_pad|>",
111
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112
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113
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114
+ "single_word": false,
115
+ "special": true
116
+ },
117
+ "151657": {
118
+ "content": "<tool_call>",
119
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120
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121
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122
+ "single_word": false,
123
+ "special": false
124
+ },
125
+ "151658": {
126
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127
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128
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129
+ "rstrip": false,
130
+ "single_word": false,
131
+ "special": false
132
+ },
133
+ "151659": {
134
+ "content": "<|fim_prefix|>",
135
+ "lstrip": false,
136
+ "normalized": false,
137
+ "rstrip": false,
138
+ "single_word": false,
139
+ "special": false
140
+ },
141
+ "151660": {
142
+ "content": "<|fim_middle|>",
143
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144
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145
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146
+ "single_word": false,
147
+ "special": false
148
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149
+ "151661": {
150
+ "content": "<|fim_suffix|>",
151
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152
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153
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154
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155
+ "special": false
156
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157
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158
+ "content": "<|fim_pad|>",
159
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160
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161
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162
+ "single_word": false,
163
+ "special": false
164
+ },
165
+ "151663": {
166
+ "content": "<|repo_name|>",
167
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168
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169
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170
+ "single_word": false,
171
+ "special": false
172
+ },
173
+ "151664": {
174
+ "content": "<|file_sep|>",
175
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176
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177
+ "rstrip": false,
178
+ "single_word": false,
179
+ "special": false
180
+ },
181
+ "151665": {
182
+ "content": "<tool_response>",
183
+ "lstrip": false,
184
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185
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186
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187
+ "special": false
188
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189
+ "151666": {
190
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191
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192
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193
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194
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195
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196
+ },
197
+ "151667": {
198
+ "content": "<think>",
199
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200
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201
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202
+ "single_word": false,
203
+ "special": false
204
+ },
205
+ "151668": {
206
+ "content": "</think>",
207
+ "lstrip": false,
208
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209
+ "rstrip": false,
210
+ "single_word": false,
211
+ "special": false
212
+ }
213
+ },
214
+ "additional_special_tokens": [
215
+ "<|im_start|>",
216
+ "<|im_end|>",
217
+ "<|object_ref_start|>",
218
+ "<|object_ref_end|>",
219
+ "<|box_start|>",
220
+ "<|box_end|>",
221
+ "<|quad_start|>",
222
+ "<|quad_end|>",
223
+ "<|vision_start|>",
224
+ "<|vision_end|>",
225
+ "<|vision_pad|>",
226
+ "<|image_pad|>",
227
+ "<|video_pad|>"
228
+ ],
229
+ "bos_token": null,
230
+ "clean_up_tokenization_spaces": false,
231
+ "eos_token": "<|endoftext|>",
232
+ "errors": "replace",
233
+ "extra_special_tokens": {},
234
+ "model_max_length": 131072,
235
+ "pad_token": "<|endoftext|>",
236
+ "split_special_tokens": false,
237
+ "tokenizer_class": "Qwen2Tokenizer",
238
+ "unk_token": null
239
+ }
upper_block/vocab.json ADDED
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vocab.json ADDED
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