What are the hardware requirements for Quintellect 10.7B?

Quintellect 10.7B requires approximately 21.4 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 Quintellect 10.7B and how large is it?

Quintellect 10.7B is developed by Walmart-the-bag, a model with 10.7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Quintellect 10.7B perform on standard benchmarks?

Quintellect 10.7B has the following published scores: MMLU 63.28. Compare against reference models on this page or on the LLM Explorer leaderboards.

Quintellect 10.7B by Walmart-the-bag — VRAM 21.4GB, 32K context

Name: Quintellect 10.7B
Rating: 3.5 (4 reviews)
Author: Walmart-the-bag

Quintellect 10.7B is an open-source language model by Walmart-the-bag. Features: 10.7b LLM, VRAM: 21.4GB, Context: 32K, License: apache-2.0, LLM Explorer Score: 0.13, Arc: 65, HellaSwag: 84.5, MMLU: 63.3, GSM8K: 60.6.

Autotrain compatible Code Conversational Dataset:sahil2801/codealpaca-2... En Mistral Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/Walmart-the-bag/Quintellect-10.7B

Quintellect 10.7B Parameters and Internals

Model Type

code generation

Use Cases

Areas:

coding knowledge accessibility

Primary Use Cases:

coding assistance, code generation

Additional Notes

This model is designed for coding tasks, specifically for languages like Python and JavaScript. It's beneficial for both standard programming tasks and code creation from scratch.

Supported Languages

en (proficient)

Input Output

Input Format:

Alpaca prompt format

Accepted Modalities:

text

Output Format:

text response

LLM Name	Quintellect 10.7B
Repository 🤗	https://huggingface.co/Walmart-the-bag/Quintellect-10.7B
Model Size	10.7b
Required VRAM	21.4 GB
Updated	2025-03-15
Maintainer	Walmart-the-bag
Model Type	mistral
Model Files	6.0 GB: 1-of-4 5.9 GB: 2-of-4 6.0 GB: 3-of-4 3.5 GB: 4-of-4
Supported Languages	en
Model Architecture	MistralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.38.2
Tokenizer Class	LlamaTokenizer
Padding Token	</s>
Vocabulary Size	32000
Torch Data Type	float16

Rank the Quintellect 10.7B 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

Best Alternatives to Quintellect 10.7B

Best Alternatives	Context / RAM	Downloads	Likes
Barely Regal 10.7B	32K / 21.5 GB	19	0
Chikuma 10.7B	32K / 21.4 GB	131	5
PiVoT 10.7B Mistral V0.2	32K / 21.4 GB	76	5
Longcat 10.7B	32K / 21.5 GB	8	3
Multi Verse Model 10.7B	32K / 21.5 GB	2	1
PrimaSumika 10.7B 128K	32K / 21.5 GB	3	1
... Loyal Mistral Maid 32K V0.2 A	32K / 21.5 GB	3	1
Chikuma 10.7B V2	32K / 21.4 GB	10	0
PiVoT 10.7B Mistral V0.2 RP	32K / 21.4 GB	67	7
Mark1 Revision 10.7B	32K / 42.9 GB	16	1

Note: green Score (e.g. "73.2") means that the model is better than Walmart-the-bag/Quintellect-10.7B.