FinBERT is a pre-trained NLP model to analyze sentiment of financial text. It is built by further training the BERT language model in the finance domain, using a large financial corpus and thereby fine-tuning it for financial sentiment classification. Financial PhraseBank by Malo et al. (2014) is used for fine-tuning. For more details, please see the paper FinBERT: Financial Sentiment Analysis with Pre-trained Language Models and our related blog post on Medium. The model will give softmax outputs for three labels: positive, negative or neutral. About Prosus Prosus is a global consumer internet group and one of the largest technology investors in the world. Operating and investing globally…
Open-weight model · Text classification
dLLM-PRM-Gap-causal-llada8b
by Yan Zhan YanZhanPKU/dLLM-PRM-Gap-causal-llada8b
dLLM-PRM-Gap-causal-llada8b is an open-weight model for text classification from Yan Zhan, released under MIT License. Its published files total 51.7 MB.
Public adapter release for dLLM PRM Gap. causal PRM for the cross-backbone LLaDA diagnostic - adapter.safetensors — compact adapter weights - config.json — public base-model id, architecture, and provenance Role: cross-backbone diagnostic; it is not a full…
Parameters—
Context—
Weights51.7 MB
Licensemit
AccessOpen weights
Monthly Downloads—
Model Card
By Yan Zhan, published under mit, revision 6ec635084892.
Public adapter release for dLLM PRM Gap. causal PRM for the cross-backbone LLaDA diagnostic - adapter.safetensors — compact adapter weights - config.json — public base-model id, architecture, and provenance Role: cross-backbone diagnostic; it is not a full LLaDA ORM replication.
Read Yan Zhan's full model card
dLLM PRM Gap · Causal LLaDA PRM
Public adapter release for dLLM PRM Gap. causal PRM for the cross-backbone LLaDA diagnostic
This repository contains only trainable adapter parameters and the reward head. It does not include the base model. The release configuration is recorded in
config.json; the arXiv paper defines the release scope and citation.
Files
adapter.safetensors— compact adapter weightsconfig.json— public base-model id, architecture, and provenance
Load
from huggingface_hub import snapshot_download
from prm.checkpointing import load_diffusion_prm
path = snapshot_download("YanZhanPKU/dLLM-PRM-Gap-causal-llada8b")
model, report = load_diffusion_prm(
checkpoint=path,
model_path="GSAI-ML/LLaDA-8B-Base",
local_files_only=False,
)
Role: cross-backbone diagnostic; it is not a full LLaDA ORM replication.
Paper: https://arxiv.org/abs/2609.35472.
Identity and Version
- Repository
- YanZhanPKU/dLLM-PRM-Gap-causal-llada8b
- Publisher
- Yan Zhan
- Task
- Text classification
- Modality
- Text
- Library
- peft
- Parameters
- Not stated by the source
- Languages
- Not stated by the source
- Revision
- 6ec6350848928f2e4e968b3802e1edb63f16c134
- First published
- 2026-09-26
- Last updated
- 2026-09-30
Files and Weights
4 files, 51.7 MB in total. The weights are 1 file totalling 51.7 MB in safetensors.
Weights1 file · 51.7 MB
Configuration1 file · 1.0 KB
Documentation1 file · 1.8 KB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| adapter.safetensors | Weights | 51.7 MB | bcde7dbec347 |
| config.json | Configuration | 1.0 KB | — |
| README.md | Documentation | 1.8 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
License and Download
- License
- mit
- Access
- Open weights, no gate
- Download size
- 51.7 MB
Download from Yan Zhan
Released by Yan Zhan through its official repository on Hugging Face. Read the license.
Built From
- Adapter of GSAI-ML/LLaDA-8B-Base
- Derived from GSAI-ML/LLaDA-8B-Base
- Described by arXiv:2609.35472
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 51.7 MB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About dLLM-PRM-Gap-causal-llada8b
Can I use dLLM-PRM-Gap-causal-llada8b commercially?
Yes. dLLM-PRM-Gap-causal-llada8b is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.
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