What are the hardware requirements for FineMedLM?

FineMedLM requires approximately 16.1 GB of VRAM and supports a context window of 128K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed FineMedLM and how large is it?

FineMedLM is developed by hongzhouyu, a model with 8b 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 FineMedLM?

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

FineMedLM by hongzhouyu — VRAM 16.1GB, 128K context

Name: FineMedLM
Author: hongzhouyu

FineMedLM is an open-source language model by hongzhouyu. Features: 8b LLM, VRAM: 16.1GB, Context: 128K, License: mit, LLM Explorer Score: 0.16.

Arxiv:2501.09213 Base model:finetune:meta-llama... Base model:meta-llama/llama-3.... Conversational Dataset:hongzhouyu/finemed-sft En Endpoints compatible Llama Medical Region:us Safetensors Sharded Tensorboard Tensorflow Zh

Model Card on HF 🤗: https://huggingface.co/hongzhouyu/FineMedLM

FineMedLM Parameters and Internals

LLM Name	FineMedLM
Repository 🤗	https://huggingface.co/hongzhouyu/FineMedLM
Base Model(s)	meta-llama/Llama-3.1-8B meta-llama/Llama-3.1-8B
Model Size	8b
Required VRAM	16.1 GB
Updated	2026-05-31
Maintainer	hongzhouyu
Model Type	llama
Model Files	5.0 GB: 1-of-4 5.0 GB: 2-of-4 4.9 GB: 3-of-4 1.2 GB: 4-of-4 0.0 GB
Supported Languages	en zh
Model Architecture	LlamaForCausalLM
License	mit
Context Length	131072
Model Max Length	131072
Transformers Version	4.50.0
Tokenizer Class	PreTrainedTokenizer
Padding Token	<\|eot_id\|>
Vocabulary Size	128256
Torch Data Type	bfloat16

Rank the FineMedLM 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

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