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Stablelm 2 1 6B by stabilityai

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  Arxiv:1607.06450   Arxiv:1910.02054   Arxiv:1910.07467   Arxiv:2101.00027   Arxiv:2104.09864   Arxiv:2204.06745   Arxiv:2206.11147   Arxiv:2305.06161   Arxiv:2305.14201   Arxiv:2307.09288   Arxiv:2309.09400   Arxiv:2309.16609   Arxiv:2402.17834   Autotrain compatible   Dataset:bigcode/starcoderdata   Dataset:carperai/pilev2-dev Dataset:dataprovenanceinitiati... Dataset:tiiuae/falcon-refinedw... Dataset:togethercomputer/redpa...   Dataset:uonlp/culturax   De   En   Endpoints compatible   Es   Fr   It   Nl   Pt   Region:us   Safetensors   Stablelm
Model Card on HF 🤗: https://huggingface.co/stabilityai/stablelm-2-1_6b 

Stablelm 2 1 6b Benchmarks

MMLU Pro: 5.15
MUSR: 5.79
BBH: 8.63
IFEval: 11.57 vs 88 (so35)-86.9%
ARC: 43.34 vs 96.7 (so35)-55.2%
HellaSwag: 70.45 vs 95.3 (gpt4)-26.1%
MMLU: 38.95 vs 88.3 (so35)-55.9%
TruthfulQA: 36.78 vs 59 (gpt4)-37.7%
WinoGrande: 64.56 vs 87.5 (gpt4)-26.2%
GSM8K: 17.44 vs 96.4 (so35)-81.9%
MATH Lvl 5: 0.76
LLME Score: 0.21208
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Stablelm 2 1 6B (stabilityai/stablelm-2-1_6b)
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Stablelm 2 1 6B Parameters and Internals

Model Type 
causal-lm
Use Cases 
Areas:
research, commercial applications
Applications:
foundational base model for application-specific fine-tuning
Limitations:
May exhibit unreliable, unsafe, or other undesirable behaviors that must be corrected prior to deployment., Pre-training data may have contained offensive or inappropriate content.
Supported Languages 
en (intermediate), de (intermediate), es (intermediate), fr (intermediate), it (intermediate), nl (intermediate), pt (intermediate)
Training Details 
Data Sources:
tiiuae/falcon-refinedweb, togethercomputer/RedPajama-Data-1T, uonlp/CulturaX, CarperAI/pilev2-dev, bigcode/starcoderdata, DataProvenanceInitiative/Commercially-Verified-Licenses
Data Volume:
2 trillion tokens
Methodology:
Pre-trained on diverse multilingual and code datasets for two epochs
Context Length:
4096
Hardware Used:
512 NVIDIA A100 40GB GPUs (AWS P4d instances)
Model Architecture:
Decoder-only transformer similar to the LLaMA architecture with modifications
Input Output 
Input Format:
prompts in tokenized form
Accepted Modalities:
text
Output Format:
generated text
Performance Tips:
Fine-tuning the base model is recommended for downstream tasks.
LLM NameStablelm 2 1 6b
Repository 🤗https://huggingface.co/stabilityai/stablelm-2-1_6b 
Model Size6b
Required VRAM3.3 GB
Updated2025-09-23
Maintainerstabilityai
Model Typestablelm
Model Files  3.3 GB
Supported Languagesen de es fr it nl pt
Model ArchitectureStableLmForCausalLM
Licenseother
Context Length4096
Model Max Length4096
Transformers Version4.38.0
Tokenizer ClassGPT2TokenizerFast
Vocabulary Size100352
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than stabilityai/stablelm-2-1_6b.

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Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
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