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Speechless Llama2 13B by uukuguy

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Speechless Llama2 13B is an open-source language model by uukuguy. Features: 13b LLM, VRAM: 26.7GB, Context: 4K, License: llama2, Instruction-Based, HF Score: 61.4, LLM Explorer Score: 0.12, Arc: 62, HellaSwag: 81.8, MMLU: 58.7, TruthfulQA: 55.7, WinoGrande: 76, GSM8K: 34.

  Arxiv:2307.09288 Dataset:garage-baind/open-plat...   Dataset:open-orca/openorca Dataset:wizardlm/wizardlm evol...   En   Facebook   Instruct   Llama   Llama2   Meta   Pytorch   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/uukuguy/speechless-llama2-13b 

Speechless Llama2 13B Benchmarks

ARC: 62.03 vs 96.7 (so35)-35.9%
HellaSwag: 81.82 vs 95.3 (gpt4)-14.1%
MMLU: 58.69 vs 88.3 (so35)-33.5%
TruthfulQA: 55.66 vs 59 (gpt4)-5.7%
WinoGrande: 76.01 vs 87.5 (gpt4)-13.1%
GSM8K: 33.97 vs 96.4 (so35)-64.8%
LLME Score: 0.11932
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Speechless Llama2 13B (uukuguy/speechless-llama2-13b)
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Speechless Llama2 13B Parameters and Internals

Model Type 
text generation
Use Cases 
Areas:
Research, Commercial applications
Primary Use Cases:
Assistant-like chat, Natural language generation tasks
Limitations:
Use in languages other than English, Use violating laws or regulations, Use prohibited by Acceptable Use Policy
Considerations:
Safety testing tailored to specific applications is required.
Additional Notes 
Models are trained with a global batch-size equivalent to 4M tokens and do not include Meta user data.
Supported Languages 
en (Fully supported)
Training Details 
Data Sources:
Open-Orca/OpenOrca-Platypus2-13B, WizardLM/WizardLM-13B-V1.2, Publicly available online data
Data Volume:
2 trillion tokens
Methodology:
Supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF)
Context Length:
4000
Hardware Used:
Meta's Research Super Cluster, Third-party cloud compute
Model Architecture:
Auto-regressive language model with optimized transformer architecture
Safety Evaluation 
Methodologies:
Internal evaluations, Safety benchmarks
Findings:
May produce inaccurate or biased responses, Potential outputs cannot be predicted in advance
Risk Categories:
Misinformation, Bias
Ethical Considerations:
Responsible Use Guide available
Responsible Ai Considerations 
Fairness:
Testing has been conducted primarily in English.
Transparency:
Limited transparency as outputs cannot be predicted.
Accountability:
Meta is accountable for model development.
Mitigation Strategies:
Safety testing and tuning guidelines provided in Responsible Use Guide.
Input Output 
Input Format:
Alpaca instruction format
Accepted Modalities:
text
Output Format:
Generated text
Performance Tips:
Use of specific formatting like `INST`, `<>`, `BOS`, `EOS` tokens, and attention to whitespace.
Release Notes 
Version:
v1.1
Notes:
A merge of Open-Orca/OpenOrca-Platypus2-13B and WizardLM/WizardLM-13B-V1.2.
LLM NameSpeechless Llama2 13B
Repository 🤗https://huggingface.co/uukuguy/speechless-llama2-13b 
Model Size13b
Required VRAM26.7 GB
Updated2026-04-25
Maintaineruukuguy
Model Typellama
Instruction-BasedYes
Model Files  10.3 GB: 1-of-3   9.9 GB: 2-of-3   6.5 GB: 3-of-3
Supported Languagesen
Model ArchitectureLlamaForCausalLM
Licensellama2
Context Length4096
Model Max Length4096
Transformers Version4.32.1
Tokenizer ClassLlamaTokenizer
Beginning of Sentence Token<s>
End of Sentence Token</s>
Unk Token<unk>
Vocabulary Size32000
Torch Data Typefloat16

Quantized Models of the Speechless Llama2 13B

Model
Likes
Downloads
VRAM
Speechless Llama2 13B GGUF36395 GB
Speechless Llama2 13B AWQ177 GB
Speechless Llama2 13B GPTQ2137 GB
Speechless Llama2 13B GGML295 GB

Best Alternatives to Speechless Llama2 13B

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Context / RAM
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TableLLM 13B16K / 26 GB30632
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Gen Sim16K / 0.3 GB83
Llama 3 13B Instruct Ft8K / 26.1 GB102
Note: green Score (e.g. "73.2") means that the model is better than uukuguy/speechless-llama2-13b.

Rank the Speechless Llama2 13B Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
Text Generation  
Text Summarization and Feature Extraction  
Code Generation  
Multi-Language Support and Translation  

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