What are the hardware requirements for LocutusqueXFelladrin TinyMistral248M Instruct?

LocutusqueXFelladrin TinyMistral248M Instruct requires approximately 0.5 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.

How does LocutusqueXFelladrin TinyMistral248M Instruct perform on standard benchmarks?

LocutusqueXFelladrin TinyMistral248M Instruct has the following published scores: MMLU 26.12. Compare against reference models on this page or on the LLM Explorer leaderboards.

LocutusqueXFelladrin TinyMistral248M Instruct by Locutusque — VRAM 0.5GB, 32K context

Name: LocutusqueXFelladrin TinyMistral248M Instruct
Author: Locutusque

LocutusqueXFelladrin TinyMistral248M Instruct is an open-source language model by Locutusque. Features: 248m LLM, VRAM: 0.5GB, Context: 32K, License: apache-2.0, Instruction-Based, Merged, LLM Explorer Score: 0.15, Arc: 24.7, HellaSwag: 27.8, MMLU: 26.1.

Merged Model Dataset:locutusque/inst mix v2... En Endpoints compatible Instruct Mistral Pytorch Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct

LocutusqueXFelladrin TinyMistral248M Instruct Parameters and Internals

Model Type

text generation

Additional Notes

This model was created by merging Locutusque/TinyMistral-248M-Instruct and Felladrin/TinyMistral-248M-SFT-v4 using mergekit. After the two models were merged, the resulting model was further trained on ~20,000 examples on the Locutusque/inst_mix_v2_top_100k at a low learning rate to further normalize weights. The resulting model combines the best of both worlds: Locutusque/TinyMistral-248M-Instruct's coding capabilities and reasoning skills, and Felladrin/TinyMistral-248M-SFT-v4's low hallucination and instruction-following capabilities. The resulting model has an incredible performance considering its size.

LLM Name	LocutusqueXFelladrin TinyMistral248M Instruct
Repository 🤗	https://huggingface.co/Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct
Merged Model	Yes
Model Size	248m
Required VRAM	0.5 GB
Updated	2026-07-22
Maintainer	Locutusque
Model Type	mistral
Instruction-Based	Yes
Model Files	0.5 GB 0.5 GB
Supported Languages	en
Model Architecture	MistralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.35.0
Tokenizer Class	LlamaTokenizer
Padding Token	[PAD]
Vocabulary Size	32005
Torch Data Type	float16

Rank the LocutusqueXFelladrin TinyMistral248M Instruct 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 LocutusqueXFelladrin TinyMistral248M Instruct

Best Alternatives	Context / RAM	Downloads	Likes
TinyMistral 248M V2.5 Instruct	32K / 1 GB	2	11
TinyMistral 248M Instruct	32K / 1 GB	69	12
Tinymistv1	32K / 0.5 GB	17	0
...istral 248M V2.5 Instruct Orpo	32K / 0.5 GB	6	0
TinyMistral 248M V2 Instruct	32K / 0.5 GB	159	7
...stral V2 Pycoder Instruct 248M	32K / 1 GB	8	3
...mistral 248M Hypnosis Instruct	32K / 0.5 GB	4	2
...al V2 Pycoder Instruct 248M V1	32K / 0.5 GB	11	1
...istral Magicoder Instruct 248M	32K / 0.5 GB	6	2
Tinymistv1	32K / 0.5 GB	0	0

Note: green Score (e.g. "73.2") means that the model is better than Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct.