What are the hardware requirements for Llama 150M Fresh?

Llama 150M Fresh requires approximately 0.9 GB of VRAM and supports a context window of 2K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Llama 150M Fresh and how large is it?

Llama 150M Fresh is developed by PrimeIntellect, a model with 150m 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 Llama 150M Fresh?

Llama 150M Fresh is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Llama 150M Fresh by PrimeIntellect — VRAM 0.9GB, 2K context

Name: Llama 150M Fresh
Author: PrimeIntellect

Llama 150M Fresh is an open-source language model by PrimeIntellect. Features: 150m LLM, VRAM: 0.9GB, Context: 2K, LLM Explorer Score: 0.14.

Arxiv:1910.09700 Endpoints compatible Llama Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/PrimeIntellect/llama-150m-fresh

Llama 150M Fresh Parameters and Internals

LLM Name	Llama 150M Fresh
Repository 🤗	https://huggingface.co/PrimeIntellect/llama-150m-fresh
Model Size	150m
Required VRAM	0.9 GB
Updated	2026-07-22
Maintainer	PrimeIntellect
Model Type	llama
Model Files	0.9 GB
Model Architecture	LlamaForCausalLM
Context Length	2048
Model Max Length	2048
Transformers Version	4.41.2
Tokenizer Class	LlamaTokenizer
Vocabulary Size	32000
Torch Data Type	float32

Rank the Llama 150M Fresh 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 PrimeIntellect/llama-150m-fresh.