What are the hardware requirements for Tinymistral V2 Pycoder Instruct 248M V1?

Tinymistral V2 Pycoder Instruct 248M V1 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.

Who developed Tinymistral V2 Pycoder Instruct 248M V1 and how large is it?

Tinymistral V2 Pycoder Instruct 248M V1 is developed by jtatman, a model with 248m 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 Tinymistral V2 Pycoder Instruct 248M V1?

Tinymistral V2 Pycoder Instruct 248M V1 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Tinymistral V2 Pycoder Instruct 248M V1 by jtatman — VRAM 0.5GB, 32K context

Name: Tinymistral V2 Pycoder Instruct 248M V1
Author: jtatman

Tinymistral V2 Pycoder Instruct 248M V1 is an open-source language model by jtatman. Features: 248m LLM, VRAM: 0.5GB, Context: 32K, Instruction-Based, LLM Explorer Score: 0.1.

Endpoints compatible Instruct Mistral Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/jtatman/tinymistral-v2-pycoder-instruct-248m-v1

Tinymistral V2 Pycoder Instruct 248M V1 Parameters and Internals

LLM Name	Tinymistral V2 Pycoder Instruct 248M V1
Repository 🤗	https://huggingface.co/jtatman/tinymistral-v2-pycoder-instruct-248m-v1
Model Size	248m
Required VRAM	0.5 GB
Updated	2026-05-17
Maintainer	jtatman
Model Type	mistral
Instruction-Based	Yes
Model Files	0.5 GB
Model Architecture	MistralForCausalLM
Context Length	32768
Model Max Length	32768
Transformers Version	4.37.0.dev0
Tokenizer Class	LlamaTokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	32064
Torch Data Type	float16

Rank the Tinymistral V2 Pycoder Instruct 248M V1 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 Tinymistral V2 Pycoder Instruct 248M V1

Best Alternatives	Context / RAM	Downloads	Likes
TinyMistral 248M V2.5 Instruct	32K / 1 GB	2	11
...adrin TinyMistral248M Instruct	32K / 0.5 GB	108	7
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
...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 jtatman/tinymistral-v2-pycoder-instruct-248m-v1.