What are the hardware requirements for Test 57 8 Avg?

Test 57 8 Avg requires approximately 2.8 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 Test 57 8 Avg and how large is it?

Test 57 8 Avg is developed by semran1, a model with 1.4b 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 Test 57 8 Avg?

Test 57 8 Avg is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Test 57 8 Avg by semran1 — VRAM 2.8GB, 2K context

Name: Test 57 8 Avg
Author: semran1

Test 57 8 Avg is an open-source language model by semran1. Features: 1.4b LLM, VRAM: 2.8GB, Context: 2K, LLM Explorer Score: 0.17.

Phi Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/semran1/test-57_8_avg

Test 57 8 Avg Parameters and Internals

LLM Name	Test 57 8 Avg
Repository 🤗	https://huggingface.co/semran1/test-57_8_avg
Model Size	1.4b
Required VRAM	2.8 GB
Updated	2025-03-02
Maintainer	semran1
Model Type	phi
Model Files	2.8 GB 0.0 GB
Model Architecture	QPhiForCausalLM
Context Length	2048
Model Max Length	2048
Transformers Version	4.48.3
Tokenizer Class	CodeGenTokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	51200
Torch Data Type	bfloat16

Rank the Test 57 8 Avg 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 Test 57 8 Avg

Best Alternatives	Context / RAM	Downloads
Test 578	2K / 2.8 GB	384
Lilac Sft	2K / 2.8 GB	83
Lilac Sft Hermes	2K / 2.8 GB	38
Lilac Sft Inf	2K / 2.8 GB	23

Note: green Score (e.g. "73.2") means that the model is better than semran1/test-57_8_avg.