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Model: EPFL-LARA/lean-standard-prover-qwen3-8b-reasoning-grpo Source: Original Platform
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Example.lean
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Example.lean
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import Mathlib
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theorem demo (n : ℕ) : n + 0 = n := by sorry
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README.md
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README.md
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---
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library_name: transformers
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pipeline_tag: text-generation
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base_model: Qwen/Qwen3-8B-Base
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base_model_relation: finetune
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language:
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- en
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tags:
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- lean
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- lean4
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- theorem-proving
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- qwen3
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- standard-prover
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- reasoning
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- grpo
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datasets:
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- EPFL-LARA/lean-world-model-cpt
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- EPFL-LARA/lean-world-model-sft
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- EPFL-LARA/lean-world-model-reasoning-sft
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- Goedel-LM/RL_dataset_V2
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---
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# Lean Standard Prover — Qwen3-8B
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**Paper model:** S8 + reasoning · **Stage:** After informal reasoning
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Generates complete Lean proofs from theorem statements using the standard prover response format. This variant writes an informal reasoning plan before each proof attempt.
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## Hugging Face Transformers
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Load the published weights from the Hub and generate on your GPU:
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```bash
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pip install 'transformers==4.57.3' accelerate torch
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hf download EPFL-LARA/lean-standard-prover-qwen3-8b-reasoning-grpo example_transformers.py inference.py prompt_utils.py Example.lean --local-dir lean-standard-prover-qwen3-8b-reasoning-grpo
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cd lean-standard-prover-qwen3-8b-reasoning-grpo
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python example_transformers.py
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```
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The included `Example.lean` is the input; replace it with your own imports,
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context, and theorem ending in `by sorry`:
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```lean4
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import Mathlib
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theorem demo (n : ℕ) : n + 0 = n := by sorry
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```
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The complete Transformers example is:
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```python
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from pathlib import Path
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
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from inference import build_prompt
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MODEL_ID = 'EPFL-LARA/lean-standard-prover-qwen3-8b-reasoning-grpo'
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source = Path("Example.lean").read_text(encoding="utf-8")
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prompt = build_prompt(source)
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tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_ID, dtype=torch.bfloat16, device_map="auto", attn_implementation="sdpa",
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).eval()
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inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to(model.device)
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prompt_length = inputs["input_ids"].shape[1]
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budget = min(2048, 32768 - prompt_length)
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if budget <= 0:
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raise ValueError("The prompt must leave room for a proof in the 32,768-token context.")
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set_seed(0)
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with torch.inference_mode():
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output = model.generate(
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**inputs, max_new_tokens=budget, do_sample=True,
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temperature=0.3, top_p=0.95, top_k=0, min_p=0.0,
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repetition_penalty=1.0, num_beams=1,
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pad_token_id=tokenizer.pad_token_id,
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)
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response = tokenizer.decode(output[0, prompt_length:], skip_special_tokens=True)
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print(response, end="")
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```
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`build_prompt` supplies this checkpoint's exact raw completion prompt.
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Tokenization uses `add_special_tokens=False`; the context budget is 32,768
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tokens. This compact example allows up to 2,048 generated tokens and prints
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only the generated response.
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## Example output
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Recorded with the script above on an H100 in BF16, using seed 0,
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temperature 0.3, and top-p 0.95. This is the extracted final proof, checked
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with Lean 4.15 and Mathlib:
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```lean4
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import Mathlib
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theorem demo (n : ℕ) : n + 0 = n := by
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induction n with
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| zero => rfl
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| succ n ih => simp_all
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```
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<details>
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<summary>Full generated response</summary>
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````text
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[Problem Restatement]
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For every natural number \(n\), adding zero to \(n\) leaves \(n\) unchanged. In symbols, the claim is
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\[
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n + 0 = n.
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\]
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This is one of the basic properties of addition on the natural numbers.
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[Useful Observations]
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1. **Zero is the additive identity.** In any algebraic structure with addition, the element \(0\) is defined by the property that adding it to any element does not change that element. For natural numbers, this is exactly the statement \(n + 0 = n\).
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2. **Natural numbers are built recursively.** The natural numbers are generated from \(0\) by repeatedly applying the successor operation. This means any proof about all natural numbers can be reduced to checking the base case \(n = 0\) and then verifying that the property is preserved when moving from \(n\) to \(n+1\).
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3. **The successor case is immediate.** If we already know \(n + 0 = n\), then adding one more zero gives \((n+1) + 0 = n+1\). This is just the same identity applied to the successor of \(n\).
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[Reasoning Path]
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To prove \(n + 0 = n\) for all natural numbers, we use the recursive definition of the natural numbers.
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- **Base case:** When \(n = 0\), the statement becomes \(0 + 0 = 0\), which is true by the definition of addition.
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- **Inductive step:** Assume the statement holds for some natural number \(n\), i.e., \(n + 0 = n\). We must show it holds for \(n+1\), i.e., \((n+1) + 0 = n+1\). But \((n+1) + 0\) is just \(n+1\) by the definition of addition, so the statement is true for \(n+1\) as well.
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Since both the base case and the inductive step are immediate, the identity holds for every natural number \(n\).
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[Key Derivation]
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The entire derivation is captured by the two equations:
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\[
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0 + 0 = 0,
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\]
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and
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\[
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(n+1) + 0 = n+1.
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\]
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These are exactly the base and successor cases of the recursive definition of addition on natural numbers.
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[Proof Type]
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This is a direct proof by induction on the natural number \(n\).
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[Formal Anchors]
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The Lean proof uses induction on \(n\):
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```lean4
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induction n with
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| zero => rfl
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| succ n ih => ...
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```
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The base case is closed by reflexivity (`rfl`), and the successor case follows from the induction hypothesis (`ih`) together with the simplifier (`simp`), which uses the recursive definition of addition.
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[Final Solution]
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We prove that \(n + 0 = n\) for every natural number \(n\) by induction.
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- For \(n = 0\), we have \(0 + 0 = 0\), which is true.
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- Assume \(n + 0 = n\). Then \((n+1) + 0 = n+1\), which is also true.
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By induction, \(n + 0 = n\) for all natural numbers \(n\).
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The complete typechecked Lean 4 proof is:
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```lean4
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import Mathlib
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theorem demo (n : ℕ) : n + 0 = n := by
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induction n with
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| zero => rfl
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| succ n ih => simp_all
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```
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````
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</details>
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[Full response](https://huggingface.co/EPFL-LARA/lean-standard-prover-qwen3-8b-reasoning-grpo/blob/main/example_response.txt) ·
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[Input/output JSON](https://huggingface.co/EPFL-LARA/lean-standard-prover-qwen3-8b-reasoning-grpo/blob/main/example_output.json) ·
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[Lean proof](https://huggingface.co/EPFL-LARA/lean-standard-prover-qwen3-8b-reasoning-grpo/blob/main/example_proof.lean)
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## vLLM
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For multiple samples and longer output budgets:
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```bash
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pip install 'vllm==0.12.0'
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hf download EPFL-LARA/lean-standard-prover-qwen3-8b-reasoning-grpo inference.py prompt_utils.py Example.lean --local-dir lean-standard-prover-qwen3-8b-reasoning-grpo
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python lean-standard-prover-qwen3-8b-reasoning-grpo/inference.py lean-standard-prover-qwen3-8b-reasoning-grpo/Example.lean --samples 1
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```
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The vLLM runner uses the same raw prompt. Its default temperature is 0.3
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for one sample and 0.7 for 32 samples; top-p is 0.95. Output tokens fit
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within the context remaining after the prompt. Use `--print-prompt` to
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inspect the exact input without loading weights.
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## Prompt template
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The reasoning template follows `evaluate_prover_model_run.py`.
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Replace the placeholder with the imports, context, and theorem ending in
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`by sorry`, and keep the final newline:
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````text
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Solve the following problem with Lean 4 code and explanatory comments:
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```lean4
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{formal_statement_with_imports}
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```
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Replace every sorry statement with an appropriate proof. Before writing any Lean code, give an informal solution with the sections [Problem Restatement], [Useful Observations], [Reasoning Path], [Key Derivation], [Proof Type], [Formal Anchors] and [Final Solution]. Then provide the complete solution in the lean4 code block. If a previous attempt failed, first give [Error Analysis], [Revised Reasoning] and [Updated Proof Plan], then provide the corrected solution.
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````
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## Expected response
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The requested informal-solution sections precede the Lean proof. A repair begins with `[Error Analysis]`, `[Revised Reasoning]`, and `[Updated Proof Plan]`.
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The response contains a complete proof in a `lean4` code fence, together with explanatory text or comments.
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Use the **last complete Lean code block** as the final proof candidate for Lean verification. The vLLM runner returns the full `response`, extracted `lean_code`, and `finish_reason` as JSONL.
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[Formal Theorem Proving with Lean World Models](https://huggingface.co/collections/EPFL-LARA/formal-theorem-proving-with-lean-world-models-6ad2b4aa4400b3e2933f380d)
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added_tokens.json
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{
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"</think>": 151668,
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"</tool_call>": 151658,
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"</tool_response>": 151666,
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"<tool_response>": 151665,
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"<|endoftext|>": 151643,
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}
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{% for message in messages %}{% if message["role"] == "user" %}{{ message["content"] }}{% endif %}{% endfor %}
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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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"Qwen3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"dtype": "bfloat16",
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"eos_token_id": 151643,
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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": 12288,
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"layer_types": [
|
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
|
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],
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"max_position_embeddings": 32768,
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"max_window_layers": 36,
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"model_type": "qwen3",
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"num_attention_heads": 32,
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"num_hidden_layers": 36,
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"num_key_value_heads": 8,
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"pad_token_id": 151643,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 1000000,
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"sliding_window": null,
|
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"tie_word_embeddings": false,
|
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"transformers_version": "4.57.3",
|
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"use_cache": false,
|
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"use_sliding_window": false,
|
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"vocab_size": 151936
|
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}
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example_output.json
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example_output.json
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{
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"model_id": "EPFL-LARA/lean-standard-prover-qwen3-8b-reasoning-grpo",
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"backend": "transformers",
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"input": "import Mathlib\n\ntheorem demo (n : ℕ) : n + 0 = n := by sorry\n",
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"prompt": "Solve the following problem with Lean 4 code and explanatory comments:\n\n```lean4\nimport Mathlib\n\n\ntheorem demo (n : ℕ) : n + 0 = n := by sorry\n```\n\nReplace every sorry statement with an appropriate proof. Before writing any Lean code, give an informal solution with the sections [Problem Restatement], [Useful Observations], [Reasoning Path], [Key Derivation], [Proof Type], [Formal Anchors] and [Final Solution]. Then provide the complete solution in the lean4 code block. If a previous attempt failed, first give [Error Analysis], [Revised Reasoning] and [Updated Proof Plan], then provide the corrected solution.\n",
|
||||
"response": "[Problem Restatement]\n\nFor every natural number \\(n\\), adding zero to \\(n\\) leaves \\(n\\) unchanged. In symbols, the claim is\n\n\\[\nn + 0 = n.\n\\]\n\nThis is one of the basic properties of addition on the natural numbers.\n\n[Useful Observations]\n\n1. **Zero is the additive identity.** In any algebraic structure with addition, the element \\(0\\) is defined by the property that adding it to any element does not change that element. For natural numbers, this is exactly the statement \\(n + 0 = n\\).\n\n2. **Natural numbers are built recursively.** The natural numbers are generated from \\(0\\) by repeatedly applying the successor operation. This means any proof about all natural numbers can be reduced to checking the base case \\(n = 0\\) and then verifying that the property is preserved when moving from \\(n\\) to \\(n+1\\).\n\n3. **The successor case is immediate.** If we already know \\(n + 0 = n\\), then adding one more zero gives \\((n+1) + 0 = n+1\\). This is just the same identity applied to the successor of \\(n\\).\n\n[Reasoning Path]\n\nTo prove \\(n + 0 = n\\) for all natural numbers, we use the recursive definition of the natural numbers.\n\n- **Base case:** When \\(n = 0\\), the statement becomes \\(0 + 0 = 0\\), which is true by the definition of addition.\n\n- **Inductive step:** Assume the statement holds for some natural number \\(n\\), i.e., \\(n + 0 = n\\). We must show it holds for \\(n+1\\), i.e., \\((n+1) + 0 = n+1\\). But \\((n+1) + 0\\) is just \\(n+1\\) by the definition of addition, so the statement is true for \\(n+1\\) as well.\n\nSince both the base case and the inductive step are immediate, the identity holds for every natural number \\(n\\).\n\n[Key Derivation]\n\nThe entire derivation is captured by the two equations:\n\n\\[\n0 + 0 = 0,\n\\]\n\nand\n\n\\[\n(n+1) + 0 = n+1.\n\\]\n\nThese are exactly the base and successor cases of the recursive definition of addition on natural numbers.\n\n[Proof Type]\n\nThis is a direct proof by induction on the natural number \\(n\\).\n\n[Formal Anchors]\n\nThe Lean proof uses induction on \\(n\\):\n\n```lean4\ninduction n with\n| zero => rfl\n| succ n ih => ...\n```\n\nThe base case is closed by reflexivity (`rfl`), and the successor case follows from the induction hypothesis (`ih`) together with the simplifier (`simp`), which uses the recursive definition of addition.\n\n[Final Solution]\n\nWe prove that \\(n + 0 = n\\) for every natural number \\(n\\) by induction.\n\n- For \\(n = 0\\), we have \\(0 + 0 = 0\\), which is true.\n- Assume \\(n + 0 = n\\). Then \\((n+1) + 0 = n+1\\), which is also true.\n\nBy induction, \\(n + 0 = n\\) for all natural numbers \\(n\\).\n\nThe complete typechecked Lean 4 proof is:\n\n```lean4\nimport Mathlib\n\n\ntheorem demo (n : ℕ) : n + 0 = n := by\n induction n with\n | zero => rfl\n | succ n ih => simp_all\n```\n",
|
||||
"lean_code": "import Mathlib\n\n\ntheorem demo (n : ℕ) : n + 0 = n := by\n induction n with\n | zero => rfl\n | succ n ih => simp_all",
|
||||
"generation": {
|
||||
"seed": 0,
|
||||
"temperature": 0.3,
|
||||
"top_p": 0.95,
|
||||
"top_k": 0,
|
||||
"min_p": 0.0,
|
||||
"max_new_tokens": 2048,
|
||||
"add_special_tokens": false
|
||||
},
|
||||
"environment": {
|
||||
"torch": "2.9.0+cu129",
|
||||
"transformers": "4.57.3",
|
||||
"gpu": "NVIDIA H100 80GB",
|
||||
"dtype": "bfloat16"
|
||||
},
|
||||
"verification": {
|
||||
"lean_version": "4.15.0",
|
||||
"compiled": true,
|
||||
"original_theorem_type_checked": true,
|
||||
"contains_sorry": false
|
||||
},
|
||||
"weight_sha256": {
|
||||
"model-00001-of-00004.safetensors": "d79d2c92edf0e7eb3a6464b7ce9e001a84cb447af3febbfdb008e5988c1aa2ba",
|
||||
"model-00002-of-00004.safetensors": "01991c30312fda63ec2aa9347f92482830cadfbba036d015626ddbb2eaca14e7",
|
||||
"model-00003-of-00004.safetensors": "ce8d19bbbae39f2899b439d8f82288f6900dd22c8b486c3b138e4a4a81d9eb47",
|
||||
"model-00004-of-00004.safetensors": "dbc2edab062ddf3620a7a0b9dcc69bac434441f638f7de1acfe1f8c1ab7f7043"
|
||||
}
|
||||
}
|
||||
10
example_prompt.txt
Normal file
10
example_prompt.txt
Normal file
@@ -0,0 +1,10 @@
|
||||
Solve the following problem with Lean 4 code and explanatory comments:
|
||||
|
||||
```lean4
|
||||
import Mathlib
|
||||
|
||||
|
||||
theorem demo (n : ℕ) : n + 0 = n := by sorry
|
||||
```
|
||||
|
||||
Replace every sorry statement with an appropriate proof. Before writing any Lean code, give an informal solution with the sections [Problem Restatement], [Useful Observations], [Reasoning Path], [Key Derivation], [Proof Type], [Formal Anchors] and [Final Solution]. Then provide the complete solution in the lean4 code block. If a previous attempt failed, first give [Error Analysis], [Revised Reasoning] and [Updated Proof Plan], then provide the corrected solution.
|
||||
7
example_proof.lean
Normal file
7
example_proof.lean
Normal file
@@ -0,0 +1,7 @@
|
||||
import Mathlib
|
||||
|
||||
|
||||
theorem demo (n : ℕ) : n + 0 = n := by
|
||||
induction n with
|
||||
| zero => rfl
|
||||
| succ n ih => simp_all
|
||||
80
example_response.txt
Normal file
80
example_response.txt
Normal file
@@ -0,0 +1,80 @@
|
||||
[Problem Restatement]
|
||||
|
||||
For every natural number \(n\), adding zero to \(n\) leaves \(n\) unchanged. In symbols, the claim is
|
||||
|
||||
\[
|
||||
n + 0 = n.
|
||||
\]
|
||||
|
||||
This is one of the basic properties of addition on the natural numbers.
|
||||
|
||||
[Useful Observations]
|
||||
|
||||
1. **Zero is the additive identity.** In any algebraic structure with addition, the element \(0\) is defined by the property that adding it to any element does not change that element. For natural numbers, this is exactly the statement \(n + 0 = n\).
|
||||
|
||||
2. **Natural numbers are built recursively.** The natural numbers are generated from \(0\) by repeatedly applying the successor operation. This means any proof about all natural numbers can be reduced to checking the base case \(n = 0\) and then verifying that the property is preserved when moving from \(n\) to \(n+1\).
|
||||
|
||||
3. **The successor case is immediate.** If we already know \(n + 0 = n\), then adding one more zero gives \((n+1) + 0 = n+1\). This is just the same identity applied to the successor of \(n\).
|
||||
|
||||
[Reasoning Path]
|
||||
|
||||
To prove \(n + 0 = n\) for all natural numbers, we use the recursive definition of the natural numbers.
|
||||
|
||||
- **Base case:** When \(n = 0\), the statement becomes \(0 + 0 = 0\), which is true by the definition of addition.
|
||||
|
||||
- **Inductive step:** Assume the statement holds for some natural number \(n\), i.e., \(n + 0 = n\). We must show it holds for \(n+1\), i.e., \((n+1) + 0 = n+1\). But \((n+1) + 0\) is just \(n+1\) by the definition of addition, so the statement is true for \(n+1\) as well.
|
||||
|
||||
Since both the base case and the inductive step are immediate, the identity holds for every natural number \(n\).
|
||||
|
||||
[Key Derivation]
|
||||
|
||||
The entire derivation is captured by the two equations:
|
||||
|
||||
\[
|
||||
0 + 0 = 0,
|
||||
\]
|
||||
|
||||
and
|
||||
|
||||
\[
|
||||
(n+1) + 0 = n+1.
|
||||
\]
|
||||
|
||||
These are exactly the base and successor cases of the recursive definition of addition on natural numbers.
|
||||
|
||||
[Proof Type]
|
||||
|
||||
This is a direct proof by induction on the natural number \(n\).
|
||||
|
||||
[Formal Anchors]
|
||||
|
||||
The Lean proof uses induction on \(n\):
|
||||
|
||||
```lean4
|
||||
induction n with
|
||||
| zero => rfl
|
||||
| succ n ih => ...
|
||||
```
|
||||
|
||||
The base case is closed by reflexivity (`rfl`), and the successor case follows from the induction hypothesis (`ih`) together with the simplifier (`simp`), which uses the recursive definition of addition.
|
||||
|
||||
[Final Solution]
|
||||
|
||||
We prove that \(n + 0 = n\) for every natural number \(n\) by induction.
|
||||
|
||||
- For \(n = 0\), we have \(0 + 0 = 0\), which is true.
|
||||
- Assume \(n + 0 = n\). Then \((n+1) + 0 = n+1\), which is also true.
|
||||
|
||||
By induction, \(n + 0 = n\) for all natural numbers \(n\).
|
||||
|
||||
The complete typechecked Lean 4 proof is:
|
||||
|
||||
```lean4
|
||||
import Mathlib
|
||||
|
||||
|
||||
theorem demo (n : ℕ) : n + 0 = n := by
|
||||
induction n with
|
||||
| zero => rfl
|
||||
| succ n ih => simp_all
|
||||
```
|
||||
31
example_transformers.py
Normal file
31
example_transformers.py
Normal file
@@ -0,0 +1,31 @@
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
|
||||
|
||||
from inference import build_prompt
|
||||
|
||||
MODEL_ID = 'EPFL-LARA/lean-standard-prover-qwen3-8b-reasoning-grpo'
|
||||
|
||||
source = Path("Example.lean").read_text(encoding="utf-8")
|
||||
prompt = build_prompt(source)
|
||||
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
MODEL_ID, dtype=torch.bfloat16, device_map="auto", attn_implementation="sdpa",
|
||||
).eval()
|
||||
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to(model.device)
|
||||
prompt_length = inputs["input_ids"].shape[1]
|
||||
budget = min(2048, 32768 - prompt_length)
|
||||
if budget <= 0:
|
||||
raise ValueError("The prompt must leave room for a proof in the 32,768-token context.")
|
||||
|
||||
set_seed(0)
|
||||
with torch.inference_mode():
|
||||
output = model.generate(
|
||||
**inputs, max_new_tokens=budget, do_sample=True,
|
||||
temperature=0.3, top_p=0.95, top_k=0, min_p=0.0,
|
||||
repetition_penalty=1.0, num_beams=1,
|
||||
pad_token_id=tokenizer.pad_token_id,
|
||||
)
|
||||
response = tokenizer.decode(output[0, prompt_length:], skip_special_tokens=True)
|
||||
print(response, end="")
|
||||
8
generation_config.json
Normal file
8
generation_config.json
Normal file
@@ -0,0 +1,8 @@
|
||||
{
|
||||
"eos_token_id": [
|
||||
151643
|
||||
],
|
||||
"max_new_tokens": 2048,
|
||||
"pad_token_id": 151643,
|
||||
"transformers_version": "4.57.3"
|
||||
}
|
||||
82
inference.py
Normal file
82
inference.py
Normal file
@@ -0,0 +1,82 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Generate Lean proofs with the paper's prompt and last-complete-block parser."""
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import json
|
||||
from pathlib import Path
|
||||
|
||||
from prompt_utils import (
|
||||
LEAN4_DEFAULT_HEADER, DEFAULT_GENERATION_TOP_P,
|
||||
build_prover_prompt_for_example, default_generation_temperature,
|
||||
default_generation_max_tokens, default_lean_stop_sequences,
|
||||
parse_generated_lean, split_context_and_command,
|
||||
)
|
||||
|
||||
DEFAULT_MODEL = 'EPFL-LARA/lean-standard-prover-qwen3-8b-reasoning-grpo'
|
||||
FEEDBACK = False
|
||||
REASONING = True
|
||||
|
||||
|
||||
def build_prompt(source: str) -> str:
|
||||
context, declaration, found = split_context_and_command(source)
|
||||
if not found:
|
||||
raise ValueError("Supply a Lean theorem, lemma, or example ending in 'by sorry'.")
|
||||
example = {"formal_statement": declaration, "header": context or LEAN4_DEFAULT_HEADER}
|
||||
_, prompt = build_prover_prompt_for_example(
|
||||
example, use_informal_statement=False, is_deepseek=False,
|
||||
tgat_prompt=FEEDBACK, reasoning=REASONING,
|
||||
)
|
||||
return prompt
|
||||
|
||||
|
||||
def main() -> None:
|
||||
parser = argparse.ArgumentParser(description=__doc__)
|
||||
parser.add_argument("files", nargs="+", type=Path, help="Lean problem files, including imports and a theorem to complete")
|
||||
parser.add_argument("--model", default=DEFAULT_MODEL)
|
||||
parser.add_argument("--samples", type=int, default=1)
|
||||
parser.add_argument("--temperature", type=float)
|
||||
parser.add_argument("--top-p", type=float, default=DEFAULT_GENERATION_TOP_P)
|
||||
parser.add_argument("--max-new-tokens", type=int, default=default_generation_max_tokens(False))
|
||||
parser.add_argument("--tensor-parallel-size", type=int, default=1)
|
||||
parser.add_argument("--seed", type=int, default=0)
|
||||
parser.add_argument("--print-prompt", action="store_true", help="Show the exact raw prompt without loading the model")
|
||||
args = parser.parse_args()
|
||||
if args.samples < 1 or args.max_new_tokens < 1:
|
||||
parser.error("--samples and --max-new-tokens must be positive")
|
||||
prompts = [build_prompt(path.read_text(encoding="utf-8")) for path in args.files]
|
||||
if args.print_prompt:
|
||||
for prompt in prompts:
|
||||
print(prompt, end="")
|
||||
return
|
||||
|
||||
from vllm import LLM, SamplingParams
|
||||
|
||||
llm = LLM(model=args.model, tokenizer=args.model,
|
||||
tensor_parallel_size=args.tensor_parallel_size, dtype="auto",
|
||||
max_model_len=32768, trust_remote_code=False, seed=args.seed,
|
||||
gpu_memory_utilization=0.90)
|
||||
tokenizer = llm.get_tokenizer()
|
||||
tokenized = [tokenizer.encode(prompt, add_special_tokens=False) for prompt in prompts]
|
||||
if any(len(ids) >= 32768 for ids in tokenized):
|
||||
raise ValueError("The problem prompt must leave space in the 32,768-token context for a proof.")
|
||||
temperature = args.temperature if args.temperature is not None else default_generation_temperature(args.samples)
|
||||
params = [SamplingParams(n=args.samples, temperature=temperature,
|
||||
top_p=args.top_p,
|
||||
max_tokens=min(args.max_new_tokens, 32768-len(ids)),
|
||||
stop=default_lean_stop_sequences(False),
|
||||
include_stop_str_in_output=True, seed=args.seed)
|
||||
for ids in tokenized]
|
||||
outputs = llm.generate([{"prompt_token_ids": ids} for ids in tokenized], params)
|
||||
for path, output in zip(args.files, outputs, strict=True):
|
||||
for i, completion in enumerate(output.outputs):
|
||||
print(json.dumps({
|
||||
"source": str(path), "sample_id": i,
|
||||
"response": completion.text,
|
||||
"lean_code": parse_generated_lean(completion.text, is_deepseek=False),
|
||||
"finish_reason": completion.finish_reason,
|
||||
}, ensure_ascii=False), flush=True)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
151388
merges.txt
Normal file
151388
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
3
model-00001-of-00004.safetensors
Normal file
3
model-00001-of-00004.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:d79d2c92edf0e7eb3a6464b7ce9e001a84cb447af3febbfdb008e5988c1aa2ba
|
||||
size 3900918064
|
||||
3
model-00002-of-00004.safetensors
Normal file
3
model-00002-of-00004.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:01991c30312fda63ec2aa9347f92482830cadfbba036d015626ddbb2eaca14e7
|
||||
size 4164052648
|
||||
3
model-00003-of-00004.safetensors
Normal file
3
model-00003-of-00004.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:ce8d19bbbae39f2899b439d8f82288f6900dd22c8b486c3b138e4a4a81d9eb47
|
||||
size 4969324464
|
||||
3
model-00004-of-00004.safetensors
Normal file
3
model-00004-of-00004.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:dbc2edab062ddf3620a7a0b9dcc69bac434441f638f7de1acfe1f8c1ab7f7043
|
||||
size 3347221632
|
||||
407
model.safetensors.index.json
Normal file
407
model.safetensors.index.json
Normal file
@@ -0,0 +1,407 @@
|
||||
{
|
||||
"metadata": {
|
||||
"total_parameters": 8190735360,
|
||||
"total_size": 16381470720
|
||||
},
|
||||
"weight_map": {
|
||||
"lm_head.weight": "model-00002-of-00004.safetensors",
|
||||
"model.embed_tokens.weight": "model-00003-of-00004.safetensors",
|
||||
"model.layers.0.input_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.0.mlp.down_proj.weight": "model-00003-of-00004.safetensors",
|
||||
"model.layers.0.mlp.gate_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.0.mlp.up_proj.weight": "model-00004-of-00004.safetensors",
|
||||
"model.layers.0.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.0.self_attn.k_norm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.0.self_attn.k_proj.weight": "model-00004-of-00004.safetensors",
|
||||
"model.layers.0.self_attn.o_proj.weight": "model-00003-of-00004.safetensors",
|
||||
"model.layers.0.self_attn.q_norm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.0.self_attn.q_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.0.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.1.input_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.1.mlp.down_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.1.mlp.gate_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.1.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.1.post_attention_layernorm.weight": "model-00004-of-00004.safetensors",
|
||||
"model.layers.1.self_attn.k_norm.weight": "model-00003-of-00004.safetensors",
|
||||
"model.layers.1.self_attn.k_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.1.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.1.self_attn.q_norm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.1.self_attn.q_proj.weight": "model-00003-of-00004.safetensors",
|
||||
"model.layers.1.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.10.input_layernorm.weight": "model-00004-of-00004.safetensors",
|
||||
"model.layers.10.mlp.down_proj.weight": "model-00003-of-00004.safetensors",
|
||||
"model.layers.10.mlp.gate_proj.weight": "model-00003-of-00004.safetensors",
|
||||
"model.layers.10.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.10.post_attention_layernorm.weight": "model-00004-of-00004.safetensors",
|
||||
"model.layers.10.self_attn.k_norm.weight": "model-00003-of-00004.safetensors",
|
||||
"model.layers.10.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
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}
|
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}
|
||||
303
prompt_utils.py
Normal file
303
prompt_utils.py
Normal file
@@ -0,0 +1,303 @@
|
||||
import re
|
||||
from typing import Any
|
||||
|
||||
|
||||
LEAN4_DEFAULT_HEADER = (
|
||||
"import Mathlib\nimport Aesop\n\n"
|
||||
"set_option maxHeartbeats 0\n\n"
|
||||
"open BigOperators Real Nat Topology Rat\n\n"
|
||||
)
|
||||
DEFAULT_LEAN_VERSION = "v4.15.0"
|
||||
DEFAULT_TIMEOUT_PER_CMD = 60
|
||||
DEFAULT_MISSING_LEAN_CODE = "sorry, no code found in output"
|
||||
DEFAULT_DEEPSEEK_GENERATION_MAX_TOKENS = 32768
|
||||
DEFAULT_STANDARD_GENERATION_MAX_TOKENS = 32768
|
||||
DEFAULT_GENERATION_TOP_P = 0.95
|
||||
DEEPSEEK_STYLE_MODEL_MARKERS = (
|
||||
"deepseek-ai",
|
||||
"goedel-lm",
|
||||
)
|
||||
DECLARATION_START_RE = re.compile(
|
||||
r"(?m)^\s*(?:@\[[^\n]*\]\s*)*"
|
||||
r"(?:(?:private|protected|noncomputable|unsafe|local)\s+)*"
|
||||
r"(theorem|lemma|example)\b"
|
||||
)
|
||||
|
||||
def remove_imports(text: str) -> str:
|
||||
"""Remove all lines starting with 'import' (ignoring leading spaces)."""
|
||||
cleaned_lines = [
|
||||
line for line in text.splitlines()
|
||||
if not re.match(r'^\s*import\s+', line)
|
||||
]
|
||||
return "\n".join(cleaned_lines).strip()
|
||||
|
||||
def strip_trailing_backticks(text: str) -> str:
|
||||
return re.sub(r"\s*```(?:\s*)$", "", text).strip()
|
||||
|
||||
_LEAN_FENCE_OPEN_RE = re.compile(r"```lean4?(?![A-Za-z0-9_])")
|
||||
_LEAN_FENCE_RE = re.compile(r"```")
|
||||
|
||||
|
||||
def closed_lean_blocks(text: str) -> list[str]:
|
||||
"""Bodies of every *closed* ```lean4 block, left to right.
|
||||
|
||||
A fence that itself opens a new lean block does not close the current one --
|
||||
the earlier block is an abandoned draft and is skipped. A plain regex would
|
||||
read ```` ```lean4 A ```lean4 B ``` ```` as a single block bodied "A ```lean4 B".
|
||||
"""
|
||||
blocks: list[str] = []
|
||||
pos = 0
|
||||
while True:
|
||||
opener = _LEAN_FENCE_OPEN_RE.search(text, pos)
|
||||
if opener is None:
|
||||
return blocks
|
||||
body_start = opener.end()
|
||||
closer = _LEAN_FENCE_RE.search(text, body_start)
|
||||
if closer is None:
|
||||
return blocks
|
||||
if _LEAN_FENCE_OPEN_RE.match(text, closer.start()) is not None:
|
||||
pos = closer.start()
|
||||
continue
|
||||
blocks.append(text[body_start : closer.start()])
|
||||
pos = closer.end()
|
||||
|
||||
|
||||
def extract_lean_code(text: str, idx: int = 0) -> str | None:
|
||||
matches = closed_lean_blocks(text or "")
|
||||
if not matches:
|
||||
return None
|
||||
lean_code = matches[idx].strip()
|
||||
return strip_trailing_backticks(lean_code)
|
||||
|
||||
def is_deepseek_style_model(
|
||||
model_id: str | None,
|
||||
loadable_model_id: str | None = None,
|
||||
) -> bool:
|
||||
candidates = [candidate for candidate in (model_id, loadable_model_id) if candidate]
|
||||
return any(
|
||||
any(marker in candidate.lower() for marker in DEEPSEEK_STYLE_MODEL_MARKERS)
|
||||
for candidate in candidates
|
||||
)
|
||||
|
||||
def split_context_and_command(code: str) -> tuple[str, str, bool]:
|
||||
text = (code or "").strip()
|
||||
if not text:
|
||||
return "", "", False
|
||||
|
||||
match = DECLARATION_START_RE.search(text)
|
||||
if match is None:
|
||||
return "", text, False
|
||||
|
||||
return text[: match.start()].strip(), text[match.start() :].strip(), True
|
||||
|
||||
def build_formal_statement(formal_statement: str, header: str = LEAN4_DEFAULT_HEADER, informal_statement: str = "") -> str:
|
||||
"""
|
||||
Prepare the prompt for the model by combining the header, informal statement, and formal statement.
|
||||
The formal statement is wrapped in a lean4 code block for clarity.
|
||||
"""
|
||||
header = (header or LEAN4_DEFAULT_HEADER).strip()
|
||||
|
||||
return f"""
|
||||
{header.strip()}
|
||||
|
||||
{informal_statement.strip()}
|
||||
{formal_statement.strip()}
|
||||
""".strip()
|
||||
|
||||
|
||||
def preprocess_formal_statement(formal_statement: str) -> str:
|
||||
"""
|
||||
Preprocess the formal statement to ensure it ends with 'sorry' if it ends with 'by' or ':='.
|
||||
This is a safety measure to ensure the prompt is well-formed for the model.
|
||||
"""
|
||||
formal_statement = formal_statement.strip()
|
||||
if formal_statement.endswith("by"):
|
||||
formal_statement += " sorry"
|
||||
elif formal_statement.endswith(":="):
|
||||
formal_statement += " by sorry"
|
||||
elif not formal_statement.endswith("sorry"):
|
||||
raise ValueError(f"Formal statement must end with 'sorry' or 'by' for safety. Got:\n{formal_statement}")
|
||||
|
||||
return formal_statement
|
||||
|
||||
PROMPT_INTRO = "Solve the following problem with Lean 4 code and explanatory comments:"
|
||||
# Phase-1 instructions: formalmathatepfl/sft_classic and formalmathatepfl/feedback_data_training.
|
||||
PLAIN_INSTRUCTION = (
|
||||
"Replace every sorry statement with an appropriate proof. "
|
||||
"Provide a complete solution in the lean4 code block."
|
||||
)
|
||||
FEEDBACK_CLAUSE = (
|
||||
", annotated with Lean 4 compiler feedback blocks in the form "
|
||||
"`/- <feedback> ... </feedback> -/` at the appropriate locations"
|
||||
)
|
||||
FEEDBACK_INSTRUCTION = PLAIN_INSTRUCTION[:-1] + FEEDBACK_CLAUSE + "."
|
||||
# Phase-2 (reasoning) instructions: `question` / `question_feedback` of formalmathatepfl/reasoning_data
|
||||
# and formalmathatepfl/rl_data. Byte-identical to LEARNING_PLAIN_TAIL / LEARNING_FEEDBACK_TAIL in
|
||||
# annotate_numina/unify_reasoning_data.py (tests/test_prompt_utils_reasoning.py pins the text).
|
||||
REASONING_SECTIONS = (
|
||||
"[Problem Restatement], [Useful Observations], [Reasoning Path], [Key Derivation], "
|
||||
"[Proof Type], [Formal Anchors] and [Final Solution]"
|
||||
)
|
||||
REPAIR_SECTIONS = "[Error Analysis], [Revised Reasoning] and [Updated Proof Plan]"
|
||||
REASONING_INSTRUCTION = (
|
||||
"Replace every sorry statement with an appropriate proof. Before writing any Lean code, give an "
|
||||
f"informal solution with the sections {REASONING_SECTIONS}. Then provide the complete solution in "
|
||||
f"the lean4 code block. If a previous attempt failed, first give {REPAIR_SECTIONS}, then provide "
|
||||
"the corrected solution."
|
||||
)
|
||||
REASONING_FEEDBACK_INSTRUCTION = (
|
||||
"Replace every sorry statement with an appropriate proof. Before writing any Lean code, give an "
|
||||
f"informal solution with the sections {REASONING_SECTIONS}. Then provide the complete solution in "
|
||||
f"the lean4 code block{FEEDBACK_CLAUSE}. If a previous attempt failed, first give {REPAIR_SECTIONS}, "
|
||||
f"then provide the corrected solution{FEEDBACK_CLAUSE}."
|
||||
)
|
||||
INSTRUCTIONS = (REASONING_FEEDBACK_INSTRUCTION, REASONING_INSTRUCTION, FEEDBACK_INSTRUCTION, PLAIN_INSTRUCTION)
|
||||
|
||||
|
||||
def instruction_for(tgat_prompt: bool = False, reasoning: bool = False) -> str:
|
||||
if reasoning:
|
||||
return REASONING_FEEDBACK_INSTRUCTION if tgat_prompt else REASONING_INSTRUCTION
|
||||
return FEEDBACK_INSTRUCTION if tgat_prompt else PLAIN_INSTRUCTION
|
||||
|
||||
|
||||
def prompt_with_instruction(formal_statement: str, instruction: str) -> str:
|
||||
"""The SFT prompt layout: intro, the statement in a lean4 block, the instruction, and the
|
||||
trailing newline every training prompt ends with (the model starts its answer at column 0)."""
|
||||
return f"{PROMPT_INTRO}\n\n```lean4\n{formal_statement}\n```\n\n{instruction}\n"
|
||||
|
||||
|
||||
def with_instruction(prompt: str, tgat_prompt: bool = False, reasoning: bool = False) -> str:
|
||||
"""Re-target a prebuilt SFT-style prompt (e.g. a dataset `question`) to the requested
|
||||
instruction. Prompts without a known instruction sentence are returned unchanged."""
|
||||
wanted = instruction_for(tgat_prompt=tgat_prompt, reasoning=reasoning)
|
||||
for present in INSTRUCTIONS: # longest first: the plain sentence is a prefix of the others
|
||||
if present in prompt:
|
||||
return prompt.replace(present, wanted, 1)
|
||||
return prompt
|
||||
|
||||
|
||||
def prompt_template(formal_statement: str) -> str:
|
||||
return prompt_with_instruction(formal_statement, PLAIN_INSTRUCTION)
|
||||
|
||||
|
||||
def prompt_template_feedback(formal_statement: str) -> str:
|
||||
return prompt_with_instruction(formal_statement, FEEDBACK_INSTRUCTION)
|
||||
|
||||
|
||||
def prompt_template_reasoning(formal_statement: str) -> str:
|
||||
return prompt_with_instruction(formal_statement, REASONING_INSTRUCTION)
|
||||
|
||||
|
||||
def prompt_template_reasoning_feedback(formal_statement: str) -> str:
|
||||
return prompt_with_instruction(formal_statement, REASONING_FEEDBACK_INSTRUCTION)
|
||||
|
||||
|
||||
def deepseek_prompt_template(formal_statement: str) -> str:
|
||||
return f"""Complete the following Lean 4 code:
|
||||
|
||||
```lean4
|
||||
{formal_statement}
|
||||
```
|
||||
|
||||
Before producing the Lean 4 code to formally prove the given theorem, provide a detailed proof plan outlining the main proof steps and strategies.
|
||||
The plan should highlight key ideas, intermediate lemmas, and proof structures that will guide the construction of the final formal proof.
|
||||
""".strip()
|
||||
|
||||
|
||||
def informal_statement_from_example(
|
||||
example: dict[str, Any],
|
||||
use_informal_statement: bool,
|
||||
) -> str:
|
||||
if not use_informal_statement:
|
||||
return ""
|
||||
for key in ("informal_prefix", "informal_statement"):
|
||||
value = example.get(key)
|
||||
if isinstance(value, str) and value.strip():
|
||||
return value.strip()
|
||||
return ""
|
||||
|
||||
|
||||
def prover_prompt_template(
|
||||
formal_statement: str,
|
||||
is_deepseek: bool = False,
|
||||
tgat_prompt: bool = False,
|
||||
reasoning: bool = False,
|
||||
) -> str:
|
||||
"""`reasoning` asks for the phase-2 informal solution sections before the proof and
|
||||
`tgat_prompt` for the feedback-annotated proof; the two combine. The DeepSeek layout is
|
||||
used only when neither is requested."""
|
||||
if reasoning or tgat_prompt:
|
||||
return prompt_with_instruction(formal_statement, instruction_for(tgat_prompt=tgat_prompt, reasoning=reasoning))
|
||||
if is_deepseek:
|
||||
return deepseek_prompt_template(formal_statement=formal_statement)
|
||||
return prompt_template(formal_statement=formal_statement)
|
||||
|
||||
|
||||
def build_prover_prompt_for_example(
|
||||
example: dict[str, Any],
|
||||
use_informal_statement: bool,
|
||||
is_deepseek: bool,
|
||||
tgat_prompt: bool = False,
|
||||
reasoning: bool = False,
|
||||
) -> tuple[str, str]:
|
||||
formal_statement = build_formal_statement(
|
||||
formal_statement=preprocess_formal_statement(example["formal_statement"]),
|
||||
header=example.get("header", None),
|
||||
informal_statement=informal_statement_from_example(
|
||||
example,
|
||||
use_informal_statement=use_informal_statement,
|
||||
),
|
||||
)
|
||||
prompt = prover_prompt_template(
|
||||
formal_statement=formal_statement,
|
||||
is_deepseek=is_deepseek,
|
||||
tgat_prompt=tgat_prompt,
|
||||
reasoning=reasoning,
|
||||
)
|
||||
return formal_statement, prompt
|
||||
|
||||
|
||||
def parse_generated_lean(
|
||||
raw_text: str,
|
||||
example: dict[str, Any] | None = None,
|
||||
is_deepseek: bool = False,
|
||||
missing_code: str | None = None,
|
||||
) -> str | None:
|
||||
lean_code = extract_lean_code(raw_text, idx=-1 if is_deepseek else -1)
|
||||
if lean_code is None:
|
||||
if missing_code is None:
|
||||
return None
|
||||
lean_code = missing_code
|
||||
|
||||
if is_deepseek and isinstance(example, dict):
|
||||
header = example.get("header")
|
||||
if isinstance(header, str) and header.strip():
|
||||
return f"{header.strip()}\n\n{lean_code.strip()}".strip()
|
||||
|
||||
return lean_code.strip()
|
||||
|
||||
|
||||
def default_lean_stop_sequences(is_deepseek: bool) -> list[str] | None:
|
||||
return None if is_deepseek else None #["``` ", "```\n"]
|
||||
|
||||
|
||||
def default_generation_max_tokens(
|
||||
is_deepseek: bool,
|
||||
deepseek_max_tokens: int = DEFAULT_DEEPSEEK_GENERATION_MAX_TOKENS,
|
||||
standard_max_tokens: int = DEFAULT_STANDARD_GENERATION_MAX_TOKENS,
|
||||
) -> int:
|
||||
return deepseek_max_tokens if is_deepseek else standard_max_tokens
|
||||
|
||||
|
||||
def default_generation_temperature(max_attempts: int) -> float:
|
||||
if max_attempts <= 0:
|
||||
raise ValueError(f"max_attempts must be positive, got {max_attempts}")
|
||||
if max_attempts == 1:
|
||||
return 0.3
|
||||
if max_attempts <= 16:
|
||||
return 0.5
|
||||
if max_attempts <= 32:
|
||||
return 0.7
|
||||
if max_attempts <= 64:
|
||||
return 0.8
|
||||
return 1.0
|
||||
31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"eos_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
240
tokenizer_config.json
Normal file
240
tokenizer_config.json
Normal file
@@ -0,0 +1,240 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151665": {
|
||||
"content": "<tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151666": {
|
||||
"content": "</tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151667": {
|
||||
"content": "<think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151668": {
|
||||
"content": "</think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|endoftext|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"padding_side": "right",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user