haielab/STP_model_Lean_0320-conjecture-base-FineTune-new-config-conjecture-base-FineTune-new-config-v2

STP-Lean-7B LoRA (v1)

LoRA rank-16 adapter fine-tuned from kfdong/STP_model_Lean_0320.

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Model Details

Field	Value
Developed by	HAIE Lab
Model type	7 B-parameter causal-LM + LoRA r 16 α 32
Languages	Lean syntax & English commentary
Finetuned from	kfdong/STP_model_Lean_0320
Precision	BF16 · Flash-Attention v2
Context length	1792 tokens
Hardware	1 × H100 80 GB

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Training Hyperparameters

Setting	Value
Precision / regime	bf16 mixed precision
Epochs	1
Max sequence length	1792 tokens (right-padding)
Per-device train batch size	6
Per-device eval batch size	2
Gradient accumulation steps	1 (effective batch = 6)
Optimizer	AdamW
Learning rate schedule	2 × 10⁻⁴ cosine, warm-up 3 %
Weight decay	0.01
LoRA rank / α / dropout	r = 16, α = 32 (2 × r), dropout = 0.05
Gradient checkpointing	Enabled (memory-efficient)
Flash-Attention v2	Enabled
Logging	every 50 steps
Evaluation strategy	once per epoch
Save strategy	once per epoch
Seed	42
Hardware	1 × H100 80 GB

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Results (1 epoch)

Metric	Value
Final train loss	1.1432
Final train accuracy	0.7157 token-level
First-step loss (warm-up)	1.6098
Tokens processed	168,856,138
Grad-norm (final)	0.3202

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How to Get Started

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "haielab/STP_model_Lean_0320-conjecture-base-FineTune-new-config"

# 1️⃣ Tokenizer ─ leave default right padding for STP
tok = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
tok.pad_token = tok.eos_token      # STP uses </s> as PAD

# 2️⃣ Load base-plus-LoRA adapter on GPU (BF16)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"              # auto-dispatch to available GPU(s)
)

# 3️⃣ Build a Lean-style prompt
prompt = "<user>Theorem foo …</user><assistant>"

inputs = tok(prompt, return_tensors="pt").to(model.device)

# 4️⃣ Generate the next proof steps
out = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.7,
    top_p=0.9,
)
print(tok.decode(out[0], skip_special_tokens=True))