Repository Analysis

BlinkDL/RWKV-LM

RWKV (pronounced RwaKuv) is an RNN with great LLM performance, which can also be directly trained like a GPT transformer (parallelizable). We are at RWKV-7 "Goose". So it's combining the best of RNN and transformer - great performance, linear time, constant space (no kv-cache), fast training, infinite ctx_len, and free sentence embedding.

16.6 Moderate AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of BlinkDL/RWKV-LM, a Python project with 14,612 GitHub stars. SynthScan v2.0 examined 24,516 lines of code across 131 source files, recording 307 pattern matches distributed across 13 syntactic categories. The overall adjusted score of 16.6 places this repository in the Moderate AI signal band.

The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).

16.6
Adjusted Score
16.6
Raw Score
100%
Time Factor
2026-07-13
Last Push
14.6K
Stars
Python
Language
24.5K
Lines of Code
131
Files
307
Pattern Hits
2026-07-14
Scan Date
0.11
HC Hit Rate

What These Metrics Mean

Adjusted Score
Primary synthetic code indicator. Raw score normalised per 1,000 lines of code and multiplied by the temporal discount factor. This is the definitive comparative metric — use it to rank repositories by AI authorship density.
Raw Score
The unmodified sum of all severity-weighted, context-multiplied pattern match scores before temporal discounting. Reflects the absolute signal strength independent of when the repository was last active.
Time Factor
The temporal discount multiplier (0–100%) applied to the raw score. Repositories last updated before ChatGPT's launch (Nov 2022) receive a 5% factor. Full signal is only assigned to repositories active in the post-adoption era (Jan 2024+).
Pattern Hits
Total count of individual pattern matches across all files and categories. A high hit count with a low score may indicate a very large codebase with isolated AI snippets; a low count with a high score indicates dense, concentrated AI signatures.
HC Hit Rate
High+Critical pattern hits per file, averaged across the repository. This orthogonal signal catches repositories where a few files are densely packed with high-severity AI tells — a strong indicator even when the normalised score appears moderate due to codebase size.
Lines of Code / Files
Total lines and files analysed. The scanner examines 94 file extensions. These denominators are used to normalise the score, enabling fair comparison between repositories of vastly different sizes.

Score History

This chart maps the temporal evolution of the adjusted synthetic code score across successive scan runs. An upward trajectory indicates ongoing incorporation of AI-generated code or expanding LLM-assisted scaffolding; a stable or declining trajectory may reflect active human refactoring, code removal, or the adoption of stricter authorship policies. The dashed secondary line (right axis) independently tracks total raw pattern hit count, which can diverge from the normalised score when codebase size changes significantly between scans.

Severity Breakdown

Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.

CRITICAL 0HIGH 15MEDIUM 0LOW 292

Directory Score Breakdown

This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.

Pattern Findings

The scanner identified 307 distinct pattern matches across 13 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.

Reading the findings table: The Severity column indicates the diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.

Unused Imports125 hits · 125 pts
SeverityFileLineSnippetContext
LOWRWKV-v7/rwkv_v7_demo.py5CODE
LOWRWKV-v7/rwkv_v7_demo.py5CODE
LOWRWKV-v7/run_qwen35_make_pth.py12CODE
LOWRWKV-v7/rwkv_mmlu_eval.py4CODE
LOWRWKV-v7/rwkv_mmlu_eval.py4CODE
LOWRWKV-v7/rwkv_mmlu_eval.py14CODE
LOWRWKV-v7/train_temp/rwkv7_train_simplified.py5CODE
LOWRWKV-v7/train_temp/train.py11CODE
LOWRWKV-v7/train_temp/train.py65CODE
LOWRWKV-v7/train_temp/train.py65CODE
LOWRWKV-v7/train_temp/train.py65CODE
LOWRWKV-v7/train_temp/src/model.py5CODE
LOWRWKV-v7/train_temp/src/model.py8CODE
LOWRWKV-v7/train_temp/src/model.py10CODE
LOWRWKV-v7/train_temp/src/model.py10CODE
LOWRWKV-v7/train_temp/src/dataset.py5CODE
LOWRWKV-v7/train_temp/src/dataset.py5CODE
LOWRWKV-v7/train_temp/src/dataset.py5CODE
LOWRWKV-v7/train_temp/src/dataset.py5CODE
LOWRWKV-v7/train_temp/src/dataset.py6CODE
LOWRWKV-v7/train_temp/src/trainer.py1CODE
LOWRWKV-v7/train_temp/src/trainer.py1CODE
LOWRWKV-v7/train_temp/src/trainer.py3CODE
LOWRWKV-v7/train_temp/src/trainer.py5CODE
LOWRWKV-v8/251016_rosa_1bit_run.py3CODE
LOWRWKV-v8/251018_rosa_4bit_run.py3CODE
LOWRWKV-v8/260222_rosa4bitLM_L12.py5CODE
LOWRWKV-v8/260222_rosa4bitLM_L12.py5CODE
LOWRWKV-v8/260222_rosa4bitLM_L12.py5CODE
LOWRWKV-v8/260222_rosa4bitLM_L12.py5CODE
LOWRWKV-v8/260212_rosa1bitLM_L12.py5CODE
LOWRWKV-v8/260212_rosa1bitLM_L12.py5CODE
LOWRWKV-v8/260212_rosa1bitLM_L12.py5CODE
LOWRWKV-v8/260212_rosa1bitLM_L12.py5CODE
LOWRWKV-v1/train.py5CODE
LOWRWKV-v1/train.py5CODE
LOWRWKV-v1/train.py5CODE
LOWRWKV-v1/train.py5CODE
LOWRWKV-v1/src/utils.py4CODE
LOWRWKV-v1/src/trainer.py3CODE
LOWRWKV-v1/src/trainer.py6CODE
LOWRWKV-v1/src/trainer.py7CODE
LOWRWKV-v4neo/img_demoAE.py5CODE
LOWRWKV-v4neo/run.py6CODE
LOWRWKV-v4neo/train.py11CODE
LOWRWKV-v4neo/train.py135CODE
LOWRWKV-v4neo/train.py135CODE
LOWRWKV-v4neo/train.py135CODE
LOWRWKV-v4neo/src/model_run.py7CODE
LOWRWKV-v4neo/src/model_run.py10CODE
LOWRWKV-v4neo/src/model_run.py10CODE
LOWRWKV-v4neo/src/model.py12CODE
LOWRWKV-v4neo/src/model.py12CODE
LOWRWKV-v4neo/src/dataset.py5CODE
LOWRWKV-v4neo/src/dataset.py5CODE
LOWRWKV-v4neo/src/model_img.py6CODE
LOWRWKV-v4neo/src/model_img.py12CODE
LOWRWKV-v4neo/src/model_img.py12CODE
LOWRWKV-v4neo/src/model_img.py14CODE
LOWRWKV-v4neo/src/binidx.py1CODE
65 more matches not shown…
Deep Nesting75 hits · 75 pts
SeverityFileLineSnippetContext
LOWRWKV-v7/rwkv_v7a_demo.py345CODE
LOWRWKV-v7/rwkv_v8_rc00_demo.py339CODE
LOWRWKV-v7/rwkv_v7_demo_fast.py334CODE
LOWRWKV-v7/rwkv_v7_demo_rnn.py234CODE
LOWRWKV-v7/rwkv_v7_demo.py88CODE
LOWRWKV-v7/rwkv_v7b_demo.py311CODE
LOWRWKV-v7/rwkv_v8_rc00_hybrid_demo.py295CODE
LOWRWKV-v7/train_temp/rwkv7_train_simplified.py67CODE
LOWRWKV-v7/train_temp/rwkv7_train_simplified.py89CODE
LOWRWKV-v7/train_temp/src/model.py499CODE
LOWRWKV-v7/train_temp/src/model.py894CODE
LOWRWKV-v7/train_temp/src/model.py526CODE
LOWRWKV-v7/train_temp/src/trainer.py159CODE
LOWRWKV-v7/train_temp/src/trainer.py130CODE
LOWRWKV-v8/251105_reverse_run.py41CODE
LOWRWKV-v8/251105_reverse_run.py145CODE
LOWRWKV-v8/251105_reverse_run.py167CODE
LOWRWKV-v8/251016_rosa_1bit_run.py9CODE
LOWRWKV-v8/251024_rosaQKV_run.py29CODE
LOWRWKV-v8/251024_rosaQKV_run.py133CODE
LOWRWKV-v8/251024_rosaQKV_run.py155CODE
LOWRWKV-v8/251014_rosa_onlyemb_train.py9CODE
LOWRWKV-v8/251014_rosa_1bit_layer.py6CODE
LOWRWKV-v8/251014_rosa_1bit_layer.py42CODE
LOWRWKV-v8/251014_rosa_1bit_train.py9CODE
LOWRWKV-v8/251014_rosa_1bit_train.py61CODE
LOWRWKV-v8/251018_rosa_4bit_run.py9CODE
LOWRWKV-v8/260222_rosa4bitLM_L12.py156CODE
LOWRWKV-v8/260222_rosa4bitLM_L12.py84CODE
LOWRWKV-v8/260222_rosa4bitLM_L12.py179CODE
LOWRWKV-v8/260212_rosa1bitLM_L12.py150CODE
LOWRWKV-v8/260212_rosa1bitLM_L12.py78CODE
LOWRWKV-v1/src/model.py16CODE
LOWRWKV-v1/src/model.py392CODE
LOWRWKV-v1/src/model.py461CODE
LOWRWKV-v1/src/trainer.py60CODE
LOWRWKV-v1/src/trainer.py65CODE
LOWRWKV-v4neo/chat.py197CODE
LOWRWKV-v4neo/math_demo/run.py41CODE
LOWRWKV-v4neo/src/model_run.py36CODE
LOWRWKV-v4neo/src/model.py747CODE
LOWRWKV-v4neo/src/model.py822CODE
LOWRWKV-v4neo/src/model.py913CODE
LOWRWKV-v4neo/src/dataset.py15CODE
LOWRWKV-v4neo/src/dataset.py107CODE
LOWRWKV-v4neo/src/model_img.py295CODE
LOWRWKV-v4neo/src/trainer.py203CODE
LOWRWKV-v4neo/src/trainer.py171CODE
LOWRWKV-v3/src/model_run.py182CODE
LOWRWKV-v3/src/model.py62CODE
LOWRWKV-v3/src/trainer.py78CODE
LOWRWKV-v3/src/trainer.py83CODE
LOWRWKV-v2-RNN/src/model_run.py14CODE
LOWRWKV-v2-RNN/src/model.py63CODE
LOWRWKV-v2-RNN/src/trainer.py78CODE
LOWRWKV-v2-RNN/src/trainer.py83CODE
LOWRWKV-v4/src/model_run.py211CODE
LOWRWKV-v4/src/model_run.py241CODE
LOWRWKV-v4/src/model.py107CODE
LOWRWKV-v4/src/model.py382CODE
15 more matches not shown…
Cross-File Repetition15 hits · 75 pts
SeverityFileLineSnippetContext
HIGHRWKV-v7/train_temp/train.py0############################################################################ # # rwkv-5 {args.precision.upper()} on {argSTRING
HIGHRWKV-v4neo/train.py0############################################################################ # # rwkv-5 {args.precision.upper()} on {argSTRING
HIGHRWKV-v5/train.py0############################################################################ # # rwkv-5 {args.precision.upper()} on {argSTRING
HIGHRWKV-v7/train_temp/src/model.py0############################################################################ # # init model weight (slow for large modelSTRING
HIGHRWKV-v4neo/src/model.py0############################################################################ # # init model weight (slow for large modelSTRING
HIGHRWKV-v4neo/src/model_img.py0############################################################################ # # init model weight (slow for large modelSTRING
HIGHRWKV-v5/src/model.py0############################################################################ # # init model weight (slow for large modelSTRING
HIGHRWKV-v7/train_temp/src/binidx.py0if distributed is initialized print only on rank 0.STRING
HIGHRWKV-v4neo/src/binidx.py0if distributed is initialized print only on rank 0.STRING
HIGHRWKV-v4/src/binidx.py0if distributed is initialized print only on rank 0.STRING
HIGHRWKV-v5/src/binidx.py0if distributed is initialized print only on rank 0.STRING
HIGHRWKV-v7/train_temp/src/binidx.py0retrieves a single item from the dataset with the option to only return a portion of the item. get(idx) is the same as [STRING
HIGHRWKV-v4neo/src/binidx.py0retrieves a single item from the dataset with the option to only return a portion of the item. get(idx) is the same as [STRING
HIGHRWKV-v4/src/binidx.py0retrieves a single item from the dataset with the option to only return a portion of the item. get(idx) is the same as [STRING
HIGHRWKV-v5/src/binidx.py0retrieves a single item from the dataset with the option to only return a portion of the item. get(idx) is the same as [STRING
Verbosity Indicators19 hits · 37 pts
SeverityFileLineSnippetContext
LOWRWKV-v4neo/run.py20# Step 1: set model & config (use v4 to run your trained-from-scratch models. v4 and v4neo are compatible)COMMENT
LOWRWKV-v4neo/run.py78# Step 2: set prompt & sampling stuffsCOMMENT
LOWRWKV-v3/run.py19### Step 1: set model ##################################################################################COMMENT
LOWRWKV-v3/run.py37### Step 2: set context ################################################################################COMMENT
LOWRWKV-v3/train.py28### Step 1: set training data ##########################################################################COMMENT
LOWRWKV-v3/train.py34### Step 2: set model size #############################################################################COMMENT
LOWRWKV-v3/train.py48### Step 3: set batch size #############################################################################COMMENT
LOWRWKV-v3/train.py55### Step 4: set learning rate, number of mini-epochs #######################################################COMMENT
LOWRWKV-v2-RNN/run.py20### Step 1: set model ##################################################################################COMMENT
LOWRWKV-v2-RNN/run.py44### Step 2: set context ################################################################################COMMENT
LOWRWKV-v2-RNN/train.py17### Step 1: set training data ##########################################################################COMMENT
LOWRWKV-v2-RNN/train.py23### Step 2: set model size #############################################################################COMMENT
LOWRWKV-v2-RNN/train.py32### Step 3: set batch size #############################################################################COMMENT
LOWRWKV-v2-RNN/train.py39### Step 4: set learning rate, training mini-epochs #######################################################COMMENT
LOWRWKV-v4/run.py19# Step 1: set modelCOMMENT
LOWRWKV-v4/run.py70# Step 2: set prompt & sampling stuffsCOMMENT
LOWRWKV-v4/train.py22# Step 1: set training data & cfgCOMMENT
LOWRWKV-v4/train.py78# Step 2: set model detailsCOMMENT
LOWRWKV-v4/train.py110# Step 3: set batch size & learning rate etc.COMMENT
Structural Annotation Overuse19 hits · 37 pts
SeverityFileLineSnippetContext
LOWRWKV-v4neo/run.py20# Step 1: set model & config (use v4 to run your trained-from-scratch models. v4 and v4neo are compatible)COMMENT
LOWRWKV-v4neo/run.py78# Step 2: set prompt & sampling stuffsCOMMENT
LOWRWKV-v3/run.py19### Step 1: set model ##################################################################################COMMENT
LOWRWKV-v3/run.py37### Step 2: set context ################################################################################COMMENT
LOWRWKV-v3/train.py28### Step 1: set training data ##########################################################################COMMENT
LOWRWKV-v3/train.py34### Step 2: set model size #############################################################################COMMENT
LOWRWKV-v3/train.py48### Step 3: set batch size #############################################################################COMMENT
LOWRWKV-v3/train.py55### Step 4: set learning rate, number of mini-epochs #######################################################COMMENT
LOWRWKV-v2-RNN/run.py20### Step 1: set model ##################################################################################COMMENT
LOWRWKV-v2-RNN/run.py44### Step 2: set context ################################################################################COMMENT
LOWRWKV-v2-RNN/train.py17### Step 1: set training data ##########################################################################COMMENT
LOWRWKV-v2-RNN/train.py23### Step 2: set model size #############################################################################COMMENT
LOWRWKV-v2-RNN/train.py32### Step 3: set batch size #############################################################################COMMENT
LOWRWKV-v2-RNN/train.py39### Step 4: set learning rate, training mini-epochs #######################################################COMMENT
LOWRWKV-v4/run.py19# Step 1: set modelCOMMENT
LOWRWKV-v4/run.py70# Step 2: set prompt & sampling stuffsCOMMENT
LOWRWKV-v4/train.py22# Step 1: set training data & cfgCOMMENT
LOWRWKV-v4/train.py78# Step 2: set model detailsCOMMENT
LOWRWKV-v4/train.py110# Step 3: set batch size & learning rate etc.COMMENT
Over-Commented Block28 hits · 28 pts
SeverityFileLineSnippetContext
LOWRWKV-v7/train_temp/demo-training-run.sh1#!/bin/bashCOMMENT
LOWRWKV-v7/train_temp/demo-training-run.sh21#COMMENT
LOWRWKV-v7/train_temp/demo-training-run-v7-pile.sh1#!/bin/bashCOMMENT
LOWRWKV-v7/train_temp/demo-training-prepare-v7-pile.sh1#!/bin/bashCOMMENT
LOWRWKV-v7/train_temp/train.py141 rank_zero_info(COMMENT
LOWRWKV-v7/train_temp/demo-training-prepare.sh1#!/bin/bashCOMMENT
LOWRWKV-v7/train_temp/src/model.py581 self.key.weight.data.uniform_(-0.05/(C**0.5), 0.05/(C**0.5))COMMENT
LOWRWKV-v7/train_temp/src/model.py621 # v = v + (v_first - v) * torch.sigmoid(self.v0 + (xv @ self.v1) @ self.v2) # add value residualCOMMENT
LOWRWKV-v7/train_temp/src/model.py641 # kk = F.normalize(kk.view(B,T,H,-1), dim=-1, p=2.0).view(B,T,C)COMMENT
LOWRWKV-v7/train_temp/src/model.py681# super().__init__()COMMENT
LOWRWKV-v7/train_temp/src/model.py761# @staticmethodCOMMENT
LOWRWKV-v4neo/run.py41# MODEL_NAME = "/fsx/BlinkDL/rwkv-release/RWKV-4-Pile-169M-20220807-8023"COMMENT
LOWRWKV-v4neo/run.py81# context = 'A'COMMENT
LOWRWKV-v4neo/train.py21 # --data_file "" --data_type "dummy" --vocab_size 0 \COMMENT
LOWRWKV-v4neo/train.py41 # --micro_bsz 8 --n_layer 24 --n_embd 2048 --pre_ffn 0 --head_qk 0 \COMMENT
LOWRWKV-v4neo/train.py261#COMMENT
LOWRWKV-v4neo/src/model.py881 # if sum_mask == 0:COMMENT
LOWRWKV-v4neo/src/model_img.py421 ss = n.split('.')COMMENT
LOWRWKV-v3/train.py41# 'RWKV' (better for English) or 'RWKV-ffnPre' (better in some cases)COMMENT
LOWRWKV-v4/train.py41COMMENT
LOWRWKV-v5/demo-training-run.sh1#!/bin/bashCOMMENT
LOWRWKV-v5/demo-training-run.sh21#COMMENT
LOWRWKV-v5/rwkv_v6_demo.py221COMMENT
LOWRWKV-v5/rwkv_v6_demo.py241# att.output.weight => zeroCOMMENT
LOWRWKV-v5/demo-training-run-v7-pile.sh1#!/bin/bashCOMMENT
LOWRWKV-v5/demo-training-prepare-v7-pile.sh1#!/bin/bashCOMMENT
LOWRWKV-v5/train.py321 data_loader = DataLoader(train_data, shuffle=False, pin_memory=True, batch_size=args.micro_bsz, num_workers=1, persiCOMMENT
LOWRWKV-v5/demo-training-prepare.sh1#!/bin/bashCOMMENT
Modern Structural Boilerplate8 hits · 8 pts
SeverityFileLineSnippetContext
LOWRWKV-v1/src/model.py10logger = logging.getLogger(__name__)CODE
LOWRWKV-v1/src/trainer.py9logger = logging.getLogger(__name__)CODE
LOWRWKV-v3/src/model.py12logger = logging.getLogger(__name__)CODE
LOWRWKV-v3/src/trainer.py22logger = logging.getLogger(__name__)CODE
LOWRWKV-v2-RNN/src/model.py12logger = logging.getLogger(__name__)CODE
LOWRWKV-v2-RNN/src/trainer.py22logger = logging.getLogger(__name__)CODE
LOWRWKV-v4/src/model.py16logger = logging.getLogger(__name__)CODE
LOWRWKV-v4/src/trainer.py18logger = logging.getLogger(__name__)CODE
Slop Phrases4 hits · 6 pts
SeverityFileLineSnippetContext
LOWRWKV-v7/train_temp/demo-training-run.sh8# Therefore check the log (### Loading rwkv-xxx.pth... ###), and make sure you don't have extra rwkv-*.pth thereCOMMENT
LOWRWKV-v7/train_temp/demo-training-run-v7-pile.sh8# Therefore check the log (### Loading rwkv-xxx.pth... ###), and make sure you don't have extra rwkv-*.pth thereCOMMENT
LOWRWKV-v5/demo-training-run.sh8# Therefore check the log (### Loading rwkv-xxx.pth... ###), and make sure you don't have extra rwkv-*.pth thereCOMMENT
LOWRWKV-v5/demo-training-run-v7-pile.sh8# Therefore check the log (### Loading rwkv-xxx.pth... ###), and make sure you don't have extra rwkv-*.pth thereCOMMENT
AI Slop Vocabulary4 hits · 5 pts
SeverityFileLineSnippetContext
LOWRWKV-v3/run.py32UNKNOWN_CHAR = ' ' # here we just set it to [space] for simplicityCODE
LOWRWKV-v2-RNN/run.py39UNKNOWN_CHAR = ' ' # here we just set it to [space] for simplicityCODE
LOWRWKV-v4/run.py36 UNKNOWN_CHAR = ' ' # here we just set it to ' ' for simplicityCODE
LOWRWKV-v4/verify.py28 UNKNOWN_CHAR = ' ' # here we just set it to [space] for simplicityCODE
Redundant / Tautological Comments2 hits · 4 pts
SeverityFileLineSnippetContext
LOWRWKV-v4/run.py21# Set TOKEN_MODE to 'char' or 'bpe' if the model is trained by 'train.py' from scratch.COMMENT
LOWRWKV-v4/run.py23# Set TOKEN_MODE to 'pile' if you want to test pre-trained pile models.COMMENT
AI Structural Patterns4 hits · 4 pts
SeverityFileLineSnippetContext
LOWRWKV-v8/251105_reverse_run.py83CODE
LOWRWKV-v8/251024_rosaQKV_run.py71CODE
LOWRWKV-v8/260212_rosa1bitLM_L12.py192CODE
LOWRWKV-v4neo/src/model_img.py370CODE
Excessive Try-Catch Wrapping3 hits · 3 pts
SeverityFileLineSnippetContext
LOWRWKV-v7/train_temp/src/trainer.py148 except Exception as e:CODE
LOWRWKV-v4neo/src/trainer.py189 except Exception as e:CODE
LOWRWKV-v5/src/trainer.py196 except Exception as e:CODE
Hyper-Verbose Identifiers1 hit · 1 pts
SeverityFileLineSnippetContext
LOWRWKV-v7/run_qwen35_make_pth.py179def strip_language_model_prefix(key: str) -> str:CODE