🧠
Model

Verbal Calibrate

by jamesjunyuguo hf-model--jamesjunyuguo--verbal-calibrate
Free2AITools Nexus Index
41.4 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 20
R: Recency 95
Q: Quality 65
Tech Context
8.03 Params
8.192K Ctx
Vital Performance
513 DL / 30D
0.0%
Audited 41.4 FNI Score
8.03B Params
8k Context
513 Downloads
8G GPU ~8GB Est. VRAM
Dense LLAMAFORCAUSALLM Architecture
Restricted LLAMA3.1 License
Model Information Summary
Entity Passport
Registry ID hf-model--jamesjunyuguo--verbal-calibrate
License llama3.1
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__jamesjunyuguo__verbal_calibrate,
  author = {jamesjunyuguo},
  title = {Verbal Calibrate Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/jamesjunyuguo/verbal-calibrate}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
jamesjunyuguo. (2026). Verbal Calibrate [Model]. Free2AITools. https://huggingface.co/jamesjunyuguo/verbal-calibrate

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

Quick Commands

đŸĻ™ Ollama Run
ollama run verbal-calibrate
🤗 HF Download
huggingface-cli download jamesjunyuguo/verbal-calibrate

âš–ī¸ Free2AITools Nexus Index V2.0

Semantic (S) 50
Authority (A) 0
Popularity (P) 20
Recency (R) 95
Quality (Q) 65

đŸ’Ŧ Index Insight

FNI V2.0 for Verbal Calibrate: Semantic (S:50), Authority (A:0), Popularity (P:20), Recency (R:95), Quality (Q:65).

Free2AITools Nexus Index

Verification Authority

Unbiased Data Node Refresh: VFS Live
---

🚀 What's Next?

Technical Deep Dive

verbal-calibrate

This checkpoint is a fine-tuned variant of meta-llama/Llama-3.1-8B-Instruct for factual QA with explicit verbalized confidence.

Intended behavior

Given a factual question, the model answers step by step and ends with exactly:

text
Answer: 
Confidence: 

The confidence score is intended to reflect the model's uncertainty about the answer and can be used as a retrieval trigger in adaptive RAG pipelines.

Motivation

  • Adaptive retrieval gating with verbalized confidence
  • Confidence-aware factual QA
  • Research on uncertainty calibration and selective retrieval

license: llama3.1 base_model: meta-llama/Llama-3.1-8B-Instruct tags: - adaptive-rag - uncertainty-quantification - retrieval-augmented-generation - question-answering language: - en

verbal-calibrate

Fine-tuned from meta-llama/Llama-3.1-8B-Instruct to express calibrated verbal confidence for adaptive retrieval-augmented generation (RAG).

What it does

Given a factual question, the model reasons step-by-step and ends every response with exactly two lines:

The confidence score reflects the model's genuine uncertainty. At inference, a confidence below 0.5 triggers BM25 retrieval and a second-pass generation with retrieved context. This allows the model to selectively retrieve only when it needs external evidence.

Training

  • Base model: meta-llama/Llama-3.1-8B-Instruct
  • Training method: Supervised fine-tuning on QA data with confidence labels, followed by calibration to align expressed confidence with empirical accuracy
  • Target datasets: Multi-hop QA (HotpotQA, MuSiQue, 2WikiMultiHopQA) and open-domain QA (NQ, TriviaQA)

Evaluation (dev_500_subsampled, 500 questions × 5 datasets)

Dataset EM F1 Trigger Rate
HotpotQA 32.0 43.8 61.6%
MuSiQue 11.8 18.8 76.8%
2WikiMultiHopQA 28.4 32.9 48.2%
NQ 32.4 44.4 25.0%
TriviaQA 53.2 62.5 28.8%
Overall 31.6 40.5 48.1%

Trigger rate = fraction of questions where confidence < 0.5 triggered retrieval.

Intended use

python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("your-username/verbal-calibrate")
model = AutoModelForCausalLM.from_pretrained("your-username/verbal-calibrate")

prompt = tokenizer.apply_chat_template([{
    "role": "user",
    "content": (
        "Answer the following factual question step by step, then state your answer "
        "and how confident you are.\n\n"
        "{question}\n\n"
        "Your response must end with exactly these two lines:\n"
        "Answer: $Answer\n"
        "Confidence: $Confidence\n\n"
        "Where $Confidence is a decimal between 0 and 1."
    ).format(question="What is the capital of France?")
}], tokenize=False, add_generation_prompt=True)



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
513Downloads
🔄 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--jamesjunyuguo--verbal-calibrate
slug
jamesjunyuguo--verbal-calibrate
source
huggingface
author
jamesjunyuguo
license
llama3.1
tags
safetensors, llama, question-answering, uncertainty-estimation, retrieval-augmented-generation, calibration, llama-3.1, text-generation, conversational, en, base_model:meta-llama/llama-3.1-8b-instruct, license:llama3.1, region:us

âš™ī¸ Technical Specs

architecture
LlamaForCausalLM
params billions
8.03
context length
8,192
pipeline tag
text-generation
vram gb
7.3
vram is estimated
true
vram formula
VRAM ≈ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

📊 Engagement & Metrics

downloads
513
stars
0
forks
0

Data indexed from public sources. Updated daily.