What are the hardware requirements for Loomstack Qwen 4B Sft Prompted?

Loomstack Qwen 4B Sft Prompted requires approximately 8.1 GB of VRAM and supports a context window of 40K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Loomstack Qwen 4B Sft Prompted and how large is it?

Loomstack Qwen 4B Sft Prompted is developed by Itachi-42, a model with 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 Loomstack Qwen 4B Sft Prompted?

Loomstack Qwen 4B Sft Prompted is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Loomstack Qwen 4B Sft Prompted by Itachi-42 — VRAM 8.1GB, 40K context

Name: Loomstack Qwen 4B Sft Prompted
Author: Itachi-42

Loomstack Qwen 4B Sft Prompted is an open-source language model by Itachi-42. Features: 4b LLM, VRAM: 8.1GB, Context: 40K, Quantized, LLM Explorer Score: 0.23.

4bit Conversational Endpoints compatible Generated from trainer Hf jobs Quantized Qwen3 Region:us Safetensors Sft Sharded Tensorflow Trl Unsloth

Model Card on HF 🤗: https://huggingface.co/Itachi-42/loomstack-qwen-4b-sft-prompted

Loomstack Qwen 4B Sft Prompted Parameters and Internals

LLM Name	Loomstack Qwen 4B Sft Prompted
Repository 🤗	https://huggingface.co/Itachi-42/loomstack-qwen-4b-sft-prompted
Model Name	sft_4b_prompted_out
Base Model(s)	unsloth/qwen3-4b-unsloth-bnb-4bit unsloth/qwen3-4b-unsloth-bnb-4bit
Model Size	4b
Required VRAM	8.1 GB
Updated	2026-07-09
Maintainer	Itachi-42
Model Type	qwen3
Model Files	5.0 GB: 1-of-2 3.1 GB: 2-of-2
Quantization Type	4bit
Model Architecture	Qwen3ForCausalLM
Context Length	40960
Model Max Length	40960
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|PAD_TOKEN\|>
Vocabulary Size	151936
Torch Data Type	bfloat16
Errors	replace

Rank the Loomstack Qwen 4B Sft Prompted 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 Loomstack Qwen 4B Sft Prompted

Best Alternatives	Context / RAM	Downloads	Likes
Qwen3 4b Merged Txt	256K / 8.1 GB	535	0
...Instruct 2507 Unsloth Bnb 4bit	256K / 3.5 GB	100469	17
Mini Coder 4B OptiQ 4bit	256K / 3 GB	310	1
Qwen3.8 4B Distilled	256K / 8.1 GB	90	0
...wen3 4B Thinking 2507 MLX 4bit	256K / 2.3 GB	53225	14
...wen3 4B Thinking 2507 MLX 8bit	256K / 4.3 GB	44731	8
FastContext 1.0 4B SFT 8bit	256K / 4.3 GB	120	1
Qwen3 4B Instruct 2507 4bit	256K / 2.3 GB	40955	11
...wen 3 4b Mythos Finetune 16bit	256K / 8.1 GB	92	0
EduMentor Qwen3 4B FP16	256K / 8.1 GB	75	1

Note: green Score (e.g. "73.2") means that the model is better than Itachi-42/loomstack-qwen-4b-sft-prompted.