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Llama2 70B Oasst Sft V10 by OpenAssistant

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  Autotrain compatible Dataset:argilla/databricks-dol...   Dataset:openassistant/oasst1 Dataset:rombodawg/losslessmega...   Dataset:shahules786/orca-best   En   Endpoints compatible   Llama   Pytorch   Region:us   Sft   Sharded
Model Card on HF 🤗: https://huggingface.co/OpenAssistant/llama2-70b-oasst-sft-v10 

Llama2 70B Oasst Sft V10 Benchmarks

ARC: 67.06 vs 96.7 (so35)-30.7%
HellaSwag: 86.38 vs 95.3 (gpt4)-9.4%
MMLU: 67.7 vs 88.3 (so35)-23.3%
TruthfulQA: 56.45 vs 59 (gpt4)-4.3%
WinoGrande: 82 vs 87.5 (gpt4)-6.3%
GSM8K: 27.22 vs 96.4 (so35)-71.8%
LLME Score: 0.13873
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Llama2 70B Oasst Sft V10 (OpenAssistant/llama2-70b-oasst-sft-v10)
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Llama2 70B Oasst Sft V10 Parameters and Internals

Model Type 
Causal decoder-only transformer language model
Use Cases 
Areas:
Research, Commercial applications
Applications:
Text-generation
Primary Use Cases:
Chat assistant applications
Limitations:
Model outputs may be unpredictable, inaccurate, biased, or objectionable.
Considerations:
Perform application-specific safety testing before deployment.
Additional Notes 
Embeddings padded to multiple of 128 for sharded inference compatibility.
Supported Languages 
en (full), de (limited), es (limited), fr (limited), it (limited), pt (limited), pl (limited), nl (limited), ro (limited), cs (limited), sv (limited)
Training Details 
Data Sources:
rombodawg/LosslessMegaCodeTrainingV2_1m_Evol_Uncensored, OpenAssistant/oasst1, shahules786/orca-best, argilla/databricks-dolly-15k-curated-multilingual
Methodology:
Fine-tuned in two stages: first on synthetic instructions and coding tasks, then on top human demonstrations.
Context Length:
4096
Hardware Used:
EPFL's Machine Learning and Optimization Laboratory, Natural Language Processing Lab
Model Architecture:
Causal decoder-only transformer architecture
Responsible Ai Considerations 
Fairness:
Testing mainly in English, outputs may be unpredictable in other scenarios.
Transparency:
Documented training processes and datasets.
Accountability:
Open-Assistant development team is accountable for model outputs.
Mitigation Strategies:
Developers should perform safety testing and tuning specific to their applications.
Input Output 
Input Format:
Prompt dialogue template with OpenAI's chatml format.
Accepted Modalities:
text
Output Format:
Assistant responses
Performance Tips:
Use the official Llama2 system message for improved inference.
LLM NameLlama2 70B Oasst Sft V10
Repository 🤗https://huggingface.co/OpenAssistant/llama2-70b-oasst-sft-v10 
Model Size70b
Required VRAM138 GB
Updated2025-09-23
MaintainerOpenAssistant
Model Typellama
Model Files  9.8 GB: 1-of-15   9.8 GB: 2-of-15   10.0 GB: 3-of-15   9.8 GB: 4-of-15   9.8 GB: 5-of-15   9.8 GB: 6-of-15   10.0 GB: 7-of-15   9.8 GB: 8-of-15   9.8 GB: 9-of-15   9.8 GB: 10-of-15   10.0 GB: 11-of-15   9.8 GB: 12-of-15   9.8 GB: 13-of-15   9.5 GB: 14-of-15   0.5 GB: 15-of-15
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 Size32007
Torch Data Typebfloat16

Quantized Models of the Llama2 70B Oasst Sft V10

Model
Likes
Downloads
VRAM
Llama2 70B OASST SFT V10 GPTQ4279835 GB
Llama2 70B OASST SFT V10 GGUF1083629 GB
Llama2 70B OASST SFT V10 AWQ21536 GB
Llama2 70B OASST SFT V10 GGML4829 GB

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Note: green Score (e.g. "73.2") means that the model is better than OpenAssistant/llama2-70b-oasst-sft-v10.

Rank the Llama2 70B Oasst Sft V10 Capabilities

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Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
Text Generation  
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Code Generation  
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