What are the hardware requirements for Mistralm?

Mistralm requires approximately 14.5 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 Mistralm and how large is it?

Mistralm is developed by Ammartatox, a model with 7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate Mistralm?

Mistralm is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Mistralm by Ammartatox — VRAM 14.5GB, 32K context

Name: Mistralm
Author: Ammartatox

Mistralm is an open-source language model by Ammartatox. Features: 7b LLM, VRAM: 14.5GB, Context: 32K, License: apache-2.0, Quantized, Instruction-Based, LLM Explorer Score: 0.13.

4bit Autotrain compatible Base model:finetune:unsloth/mi... Base model:unsloth/mistral-7b-... Conversational En Endpoints compatible Instruct Mistral Pytorch Quantized Region:us Sharded Trl Unsloth

Model Card on HF 🤗: https://huggingface.co/Daemontatox/mistralm

Mistralm Parameters and Internals

Model Type

text-generation-inference, transformers

Supported Languages

en (high)

Training Details

Methodology:

Trained 2x faster with Unsloth and Huggingface's TRL library.

LLM Name	Mistralm
Repository 🤗	https://huggingface.co/Daemontatox/mistralm
Base Model(s)	unsloth/mistral-7b-instruct-v0.3-bnb-4bit unsloth/mistral-7b-instruct-v0.3-bnb-4bit
Model Size	7b
Required VRAM	14.5 GB
Updated	2024-12-03
Maintainer	Ammartatox
Model Type	mistral
Instruction-Based	Yes
Model Files	5.0 GB: 1-of-3 5.0 GB: 2-of-3 4.5 GB: 3-of-3
Supported Languages	en
Quantization Type	4bit
Model Architecture	MistralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.41.2
Tokenizer Class	LlamaTokenizer
Padding Token	[control_768]
Vocabulary Size	32768
Torch Data Type	float16

Rank the Mistralm 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 Mistralm

Best Alternatives	Context / RAM	Downloads	Likes
...aude Sonnet Mistral 7B It V0.3	32K / 14.5 GB	7	2
Mistral 7B Instruct V0.2 Fp16	32K / 14.4 GB	26	0
Mistral 7B Instruct V0.2 4bit	32K / 4.3 GB	79	1
...tral 7B Instruct V0.3 Bnb 4bit	32K / 4.1 GB	34633	42
Mistral 7B Instruct V0.2 8bit	32K / 7.6 GB	5	1
...tral 7B Instruct V0.2 Bnb 4bit	32K / 4.1 GB	7160	35
Mixtral V0.3 Full 16bit	32K / 14.5 GB	8	0
... 7B Instruct V0.3ContinuedFine	32K / 14.5 GB	14	0
Mistral 7B Instruct V0.3 4bit	32K / 4.1 GB	12492	10
...eMistral 7B Instruct Ead 16bit	32K / 14.5 GB	38	0

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