What are the hardware requirements for Contextual KTO Mistral PairRM?

Contextual KTO Mistral PairRM requires approximately 14.4 GB of VRAM and supports a context window of 32K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Contextual KTO Mistral PairRM and how large is it?

Contextual KTO Mistral PairRM is developed by ContextualAI, a model with 7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Contextual KTO Mistral PairRM perform on standard benchmarks?

Contextual KTO Mistral PairRM has the following published scores: MMLU 60.28. Compare against reference models on this page or on the LLM Explorer leaderboards.

Contextual KTO Mistral PairRM by ContextualAI — VRAM 14.4GB, 32K context

Name: Contextual KTO Mistral PairRM
Author: ContextualAI

Contextual KTO Mistral PairRM is an open-source language model by ContextualAI. Features: 7b LLM, VRAM: 14.4GB, Context: 32K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.13, Arc: 64.8, HellaSwag: 85.5, MMLU: 60.3, GSM8K: 33.8.

Arxiv:2402.01306 Alignment Conversational Dataset:snorkelai/snorkel-mist... Dpo En Endpoints compatible Halo Halos Human feedback Instruct Mistral Preferences Region:us Rl Rlhf Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/ContextualAI/Contextual_KTO_Mistral_PairRM

Contextual KTO Mistral PairRM Parameters and Internals

Model Type

text generation

Use Cases

Areas:

prompt consistency, text generation

Applications:

cake recipe generation

Primary Use Cases:

chat applications

Additional Notes

Beginning-of-sequence token added automatically, no EOS token required. Supports apply_chat_template feature.

Training Details

Data Sources:

snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset

Methodology:

Optimized with the loss KTO, aligned using a dataset and iteratively improved over training partitions.

Input Output

Input Format:

Prompts should be formatted with `<|user|>` and `<|assistant|>` roles.

Accepted Modalities:

text

LLM Name	Contextual KTO Mistral PairRM
Repository 🤗	https://huggingface.co/ContextualAI/Contextual_KTO_Mistral_PairRM
Model Size	7b
Required VRAM	14.4 GB
Updated	2026-10-16
Maintainer	ContextualAI
Model Type	mistral
Instruction-Based	Yes
Model Files	4.9 GB: 1-of-3 5.0 GB: 2-of-3 4.5 GB: 3-of-3
Supported Languages	en
Model Architecture	MistralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.38.1
Tokenizer Class	LlamaTokenizer
Padding Token	<s>
Vocabulary Size	32000
Torch Data Type	bfloat16

Rank the Contextual KTO Mistral PairRM capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

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

Quantized Models of the Contextual KTO Mistral PairRM

Model	Likes	Downloads	VRAM
NebulaNet V2 4x7B MoE	4	131	8 GB

Best Alternatives to Contextual KTO Mistral PairRM

Best Alternatives	Context / RAM	Downloads	Likes
...Nemo Instruct 2407 Abliterated	1000K / 24.5 GB	220	20
SpydazWeb AI HumanAI RP	512K / 14.4 GB	16	1
SpydazWeb AI HumanAI 002	512K / 14.4 GB	18	1
...daz Web AI ChatML 512K Project	512K / 14.5 GB	12	0
... Summarize 64K QLoRANET Merged	128K / 4.1 GB	14	0
...1 Summarize 64K LoRANET Merged	128K / 14.4 GB	22	0
Mistral 7B Instruct V0.2	32K / 14.4 GB	1115337	3184
Mistral 7B Instruct V0.1	32K / 14.4 GB	253629	1846
Mistral 7B Instruct V0.2	32K / 14.4 GB	1338	0
Mixtral AI CyberCoder	32K / 14.3 GB	0	0

Note: green Score (e.g. "73.2") means that the model is better than ContextualAI/Contextual_KTO_Mistral_PairRM.