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14B by CausalLM

 ยป  All LLMs  ยป  CausalLM  ยป  14B   URL Share it on

  2735   
  Autotrain compatible   Causallm   Dataset:baai/coig   Dataset:fnlp/moss-003-sft-data Dataset:garage-baind/open-plat... Dataset:jondurbin/airoboros-3.... Dataset:josephuscheung/guanaco...   Dataset:ldjnr/puffin Dataset:liuhaotian/llava-instr...   Dataset:liwu/mnbvc   Dataset:meta-math/metamathqa Dataset:milashkaarshif/moegirl...   Dataset:open-orca/openorca   Dataset:openbmb/llava zh   Dataset:ryokoai/fandom23k   Dataset:ryokoai/sharegpt52k   Dataset:stingning/ultrachat   Dataset:teknium/openhermes Dataset:tigerresearch/tigerbot...   Dataset:wiki lingua   Dataset:wikipedia Dataset:wizardlm/wizardlm evol...   En   Endpoints compatible   Instruct   Llama   Llama2   Pytorch   Qwen   Region:us   Sharded   Zh
Model Card on HF ๐Ÿค—: https://huggingface.co/CausalLM/14B 

14B Benchmarks

MMLU Pro: 24.68
GPQA: 7.05
MUSR: 11.47
BBH: 24.78
IFEval: 27.88 vs 88 (so35)-68.3%
ARC: 56.66 vs 96.7 (so35)-41.4%
HellaSwag: 79.08 vs 95.3 (gpt4)-17%
MMLU: 65.86 vs 88.3 (so35)-25.4%
TruthfulQA: 47.75 vs 59 (gpt4)-19.1%
WinoGrande: 74.9 vs 87.5 (gpt4)-14.4%
GSM8K: 58.61 vs 96.4 (so35)-39.2%
MATH Lvl 5: 7.55
LLME Score: 0.22065
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
14B (CausalLM/14B)
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14B Parameters and Internals

Model Type 
text-generation, causallm
Use Cases 
Areas:
Research, Commercial applications
Primary Use Cases:
Speculative sampling
Limitations:
May produce hallucinations or unreliable outputs, Contains objectionable content, pornography, violence, and offensive language
Considerations:
Conduct own safety checks, filter keywords
Additional Notes 
The model is not recommended for quantization, but rather using smaller size models like 7B.
Supported Languages 
en (English), zh (Chinese)
Training Details 
Data Sources:
JosephusCheung/GuanacoDataset, Open-Orca/OpenOrca, stingning/ultrachat, meta-math/MetaMathQA, liuhaotian/LLaVA-Instruct-150K, jondurbin/airoboros-3.1, WizardLM/WizardLM_evol_instruct_V2_196k, RyokoAI/ShareGPT52K, RyokoAI/Fandom23K, milashkaarshif/MoeGirlPedia_wikitext_raw_archive, wikipedia, wiki_lingua, fnlp/moss-003-sft-data, garage-bAInd/Open-Platypus, LDJnr/Puffin, openbmb/llava_zh, BAAI/COIG, TigerResearch/tigerbot-zhihu-zh-10k, liwu/MNBVC, teknium/openhermes
Data Volume:
1.3B tokens
Methodology:
Manual or synthetic rewrites, augmented text training, synthetic Wikipedia conversation dataset
Model Architecture:
Identical to LLaMA2, using same attention calculation method
Input Output 
Input Format:
[chatml](https://github.com/openai/openai-python/blob/main/chatml.md)
Accepted Modalities:
text
Output Format:
text
Performance Tips:
If VRAM is insufficient, use the 7B model instead of the quantized version.
Release Notes 
Version:
14B-DPO-alpha
Date:
Dec 3, 2023
Notes:
Outperforms Zephyr-ฮฒ in MT-Bench.
LLM Name14B
Repository ๐Ÿค—https://huggingface.co/CausalLM/14B 
Model Size14b
Required VRAM28.4 GB
Updated2025-09-28
MaintainerCausalLM
Model Typellama
Instruction-BasedYes
Model Files  10.0 GB: 1-of-3   10.0 GB: 2-of-3   8.4 GB: 3-of-3
Supported Languagesen zh
Model ArchitectureLlamaForCausalLM
Licensewtfpl
Context Length8192
Model Max Length8192
Transformers Version4.35.0.dev0
Tokenizer ClassGPT2Tokenizer
Vocabulary Size152064
Torch Data Typebfloat16

Quantized Models of the 14B

Model
Likes
Downloads
VRAM
CausalLM 14B GGUF18932178 GB
CausalLM 14B EXL2398 GB
CausalLM 14B GPTQ20199 GB
CausalLM 14B AWQ10119 GB

Best Alternatives to 14B

Best Alternatives
Context / RAM
Downloads
Likes
...hocolatine 2 14B Instruct V2.016K / 29.4 GB226
...colatine 2 14B Instruct V2.0.116K / 29.4 GB301
CausalLM 14B EXL28K / 8.5 GB93
CausalLM 14B GPTQ8K / 9.7 GB1920
CausalLM 14B AWQ8K / 9.7 GB1110
Note: green Score (e.g. "73.2") means that the model is better than CausalLM/14B.

Rank the 14B Capabilities

๐Ÿ†˜ 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! ๐ŸŒŸ

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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