What are the hardware requirements for SweMedMCQ EuroLLM 1.7B Answer EN?

SweMedMCQ EuroLLM 1.7B Answer EN requires approximately 3.3 GB of VRAM and supports a context window of 4K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed SweMedMCQ EuroLLM 1.7B Answer EN and how large is it?

SweMedMCQ EuroLLM 1.7B Answer EN is developed by Anonymiserad, a model with 1.7b 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 SweMedMCQ EuroLLM 1.7B Answer EN?

SweMedMCQ EuroLLM 1.7B Answer EN is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

SweMedMCQ EuroLLM 1.7B Answer EN by Anonymiserad — VRAM 3.3GB, 4K context

Name: SweMedMCQ EuroLLM 1.7B Answer EN
Author: Anonymiserad

SweMedMCQ EuroLLM 1.7B Answer EN is an open-source language model by Anonymiserad. Features: 1.7b LLM, VRAM: 3.3GB, Context: 4K, License: apache-2.0, Instruction-Based.

Base model:finetune:utter-proj... Base model:utter-project/eurol... Conversational En Endpoints compatible Instruct Llama Medical Multiple-choice Region:us Safetensors Sv Swedish

Model Card on HF 🤗: https://huggingface.co/Anonymiserad/SweMedMCQ-EuroLLM-1.7B-Answer-EN

SweMedMCQ EuroLLM 1.7B Answer EN Parameters and Internals

LLM Name	SweMedMCQ EuroLLM 1.7B Answer EN
Repository 🤗	https://huggingface.co/Anonymiserad/SweMedMCQ-EuroLLM-1.7B-Answer-EN
Model Name	utter-project/EuroLLM-1.7B-Instruct
Base Model(s)	utter-project/EuroLLM-1.7B-Instruct utter-project/EuroLLM-1.7B-Instruct
Model Size	1.7b
Required VRAM	3.3 GB
Updated	2026-10-17
Maintainer	Anonymiserad
Model Type	llama
Instruction-Based	Yes
Model Files	3.3 GB
Supported Languages	sv en
Model Architecture	LlamaForCausalLM
License	apache-2.0
Context Length	4096
Model Max Length	4096
Transformers Version	4.57.6
Tokenizer Class	LlamaTokenizer
Padding Token	</s>
Vocabulary Size	128000

Rank the SweMedMCQ EuroLLM 1.7B Answer EN 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 SweMedMCQ EuroLLM 1.7B Answer EN

Best Alternatives	Context / RAM	Downloads	Likes
SmolLM2 1.7B Instruct 16K	16K / 3.4 GB	103	10
SmolLM2 1.7B Instruct	8K / 3.4 GB	154013	742
SmolLM2 1.7B Instruct	8K / 3.4 GB	4909	6
Yoru	8K / 3.4 GB	309	0
...ygnis Alpha 1.7B V0.1 Instruct	8K / 3.4 GB	11	2
SmolTulu 1.7B Reinforced	8K / 3.4 GB	12	5
SmolLM2 1.7 Persona	8K / 3.5 GB	7	0
Superthoughts Lite V1	8K / 3.4 GB	92	3
Penny 1.7B	8K / 6.9 GB	18	24
...ghts Lite 1.8B Experimental O1	8K / 3.6 GB	11	2

Note: green Score (e.g. "73.2") means that the model is better than Anonymiserad/SweMedMCQ-EuroLLM-1.7B-Answer-EN.