What are the hardware requirements for Stem Coder 7B Test?

Stem Coder 7B Test requires approximately 14.4 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Stem Coder 7B Test and how large is it?

Stem Coder 7B Test is developed by cfahlgren1, a model with 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 Stem Coder 7B Test?

Stem Coder 7B Test is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Stem Coder 7B Test by cfahlgren1 — VRAM 14.4GB

Name: Stem Coder 7B Test
Author: cfahlgren1

Stem Coder 7B Test is an open-source language model by cfahlgren1. Features: 7b LLM, VRAM: 14.4GB, License: apache-2.0, Quantized, Code Generating, LLM Explorer Score: 0.11.

4bit Base model:quantized:unsloth/m... Base model:unsloth/mistral-7b-... Codegen Conversational Dataset:knowrohit07/saraswati-... Dataset:m-a-p/code-feedback En Endpoints compatible Gguf Mistral Pytorch Quantized Region:us Sft Sharded Trl Unsloth

Model Card on HF 🤗: https://huggingface.co/cfahlgren1/stem-coder-7b-test

Stem Coder 7B Test Parameters and Internals

Model Type

text-generation-inference, transformers

Additional Notes

StemCoder-7B was a test to finetune Mistral-7B on 9k instructions (albeit, many are multi-turn) via a free Google Colab instance.

LLM Name	Stem Coder 7B Test
Repository 🤗	https://huggingface.co/cfahlgren1/stem-coder-7b-test
Base Model(s)	Mistral 7B Bnb 4bit unsloth/mistral-7b-bnb-4bit
Model Size	7b
Required VRAM	14.4 GB
Updated	2024-12-19
Maintainer	cfahlgren1
Model Type	mistral
Model Files	4.9 GB: 1-of-3 5.0 GB: 2-of-3 4.5 GB: 3-of-3 4.4 GB
Supported Languages	en
GGUF Quantization	Yes
Quantization Type	gguf\|4bit
Generates Code	Yes
Model Architecture	AutoModel
License	apache-2.0
Tokenizer Class	LlamaTokenizer
Padding Token	<unk>

Rank the Stem Coder 7B Test 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 cfahlgren1/stem-coder-7b-test.