🧠
Model

Llada 8b Instruct Judge Fs Ro

by akkikiki hf-model--akkikiki--llada-8b-instruct-judge-fs-ro
Nexus Index
40.5 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 12
R: Recency 97
Q: Quality 65
Tech Context
8 Params
4.096K Ctx
Vital Performance
170 DL / 30D
0.0%
Audited 40.5 FNI Score
8B Params
4k Context
170 Downloads
8G GPU ~8GB Est. VRAM
Model Information Summary
Entity Passport
Registry ID hf-model--akkikiki--llada-8b-instruct-judge-fs-ro
Provider huggingface
💾

Compute Threshold

~7.3GB VRAM

Interactive
Analyze Hardware
â–ŧ

* Static estimation for 4-Bit Quantization.

📜

Cite this model

Academic & Research Attribution

BibTeX
@misc{hf_model__akkikiki__llada_8b_instruct_judge_fs_ro,
  author = {akkikiki},
  title = {Llada 8b Instruct Judge Fs Ro Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/akkikiki/llada-8b-instruct-judge-fs-ro}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
akkikiki. (2026). Llada 8b Instruct Judge Fs Ro [Model]. Free2AITools. https://huggingface.co/akkikiki/llada-8b-instruct-judge-fs-ro

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

Quick Commands

đŸĻ™ Ollama Run
ollama run llada-8b-instruct-judge-fs-ro
🤗 HF Download
huggingface-cli download akkikiki/llada-8b-instruct-judge-fs-ro
đŸ“Ļ Install Lib
pip install -U transformers

âš–ī¸ Nexus Index V2.0

40.5
TOP 100% SYSTEM IMPACT
Semantic (S) 50
Authority (A) 0
Popularity (P) 12
Recency (R) 97
Quality (Q) 65

đŸ’Ŧ Index Insight

FNI V2.0 for Llada 8b Instruct Judge Fs Ro: Semantic (S:50), Authority (A:0), Popularity (P:12), Recency (R:97), Quality (Q:65).

Free2AITools Nexus Index

Verification Authority

Unbiased Data Node Refresh: VFS Live
---

🚀 What's Next?

Technical Deep Dive

Model Card for akkikiki/LLaDA-8B-Instruct-judge-fs-ro

This model is a fine-tuned version of akkikiki/LLaDA-8B-Instruct-judge-fs. It has been trained using TRL.

Quick start

python
from transformers import pipeline

prompt = """###Task Description:
An instruction (might include an Input inside it), a response to evaluate, a reference answer that gets a score of 5, and a score rubric representing a evaluation criteria are given.
1. Write a detailed feedback that assess the quality of the response strictly based on the given score rubric, not evaluating in general.
2. After writing a feedback, write a score that is an integer between 1 and 5. You should refer to the score rubric.
3. The output format should look as follows: "Feedback: (write a feedback for criteria) [RESULT] (an integer number between 1 and 5)"
4. Please do not generate any other opening, closing, and explanations.

###The instruction to evaluate:
{orig_instruction}

###Response to evaluate:
{orig_response}

###Reference Answer (Score 5):
{orig_reference_answer}

###Score Rubrics:
[{orig_criteria}]
Score 1: {orig_score1_description}
Score 2: {orig_score2_description}
Score 3: {orig_score3_description}
Score 4: {orig_score4_description}
Score 5: {orig_score5_description}

###Feedback: """

generator = pipeline("text-generation", model="akkikiki/LLaDA-8B-Instruct-judge-fs-ro", device="cuda")
output = generator([{"role": "user", "content": prompt}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

This model was trained with SFT on 95% of prometheus-eval/Feedback-Collection with 5% held out as a validation set.

Framework versions

  • TRL: 0.23.0
  • Transformers: 4.56.2
  • Pytorch: 2.8.0
  • Datasets: 4.0.0
  • Tokenizers: 0.22.1

Citations

bibtex
@misc{fujinuma2026unlockingpromptinfillingcapability,
      title={Unlocking Prompt Infilling Capability for Diffusion Language Models}, 
      author={Yoshinari Fujinuma and Keisuke Sakaguchi},
      year={2026},
      eprint={2604.03677},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2604.03677}, 
}

âš ī¸ Incomplete Data

Some information about this model is not available. Use with Caution - Verify details from the original source before relying on this data.

View Original Source →

📝 Limitations & Considerations

  • â€ĸ Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • â€ĸ VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • â€ĸ FNI scores are relative rankings and may change as new models are added.
  • ⚠ License Unknown: Verify licensing terms before commercial use.

Social Proof

HuggingFace Hub
170Downloads
🔄 Daily sync (03:00 UTC)

AI Summary: Based on Hugging Face metadata. Not a recommendation.

📊 FNI Methodology 📚 Knowledge Baseâ„šī¸ Verify with original source

đŸ›Ąī¸ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id
hf-model--akkikiki--llada-8b-instruct-judge-fs-ro
slug
akkikiki--llada-8b-instruct-judge-fs-ro
source
huggingface
author
akkikiki
license
tags
transformers, safetensors, llada, feature-extraction, generated_from_trainer, trl, sft, custom_code, arxiv:2604.03677, base_model:akkikiki/llada-8b-instruct-judge-fs, region:us

âš™ī¸ Technical Specs

architecture
null
params billions
8
context length
4,096
pipeline tag
feature-extraction
vram gb
7.3
vram is estimated
true
vram formula
VRAM ≈ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

📊 Engagement & Metrics

downloads
170
stars
0
forks
0

Data indexed from public sources. Updated daily.