What are the hardware requirements for Zephyr Quiklang 3B?

Zephyr Quiklang 3B requires approximately 5.9 GB of VRAM and supports a context window of 4K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Zephyr Quiklang 3B and how large is it?

Zephyr Quiklang 3B is developed by Walmart-the-bag, a model with 3b 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 Zephyr Quiklang 3B?

Zephyr Quiklang 3B is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Zephyr Quiklang 3B by Walmart-the-bag — VRAM 5.9GB, 4K context

Name: Zephyr Quiklang 3B
Rating: 3.25 (4 reviews)
Author: Walmart-the-bag

Zephyr Quiklang 3B is an open-source language model by Walmart-the-bag. Features: 3b LLM, VRAM: 5.9GB, Context: 4K, License: other, LLM Explorer Score: 0.11.

Base model:finetune:stabilitya... Base model:stabilityai/stablel... Causal lm Conversational Custom code Dataset:teknium/openhermes Dataset:unalignment/toxic-dpo-... Feature-extraction Pytorch Region:us Safetensors Stablelm epoch

Model Card on HF 🤗: https://huggingface.co/Walmart-the-bag/zephyr-quiklang-3b

Zephyr Quiklang 3B Benchmarks

LLME Score: 0.10941

^nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

What is the LLM Explorer Rank (Score)

Zephyr Quiklang 3B (Walmart-the-bag/zephyr-quiklang-3b)

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Zephyr Quiklang 3B Parameters and Internals

Model Type

causal_lm

Training Details

Data Sources:

teknium/openhermes, unalignment/toxic-dpo-v0.1

Data Volume:

10000 samples

Context Length:

1024

Hardware Used:

1xA6000-48GB

LLM Name	Zephyr Quiklang 3B
Repository 🤗	https://huggingface.co/Walmart-the-bag/zephyr-quiklang-3b
Base Model(s)	Stablelm Zephyr 3B stabilityai/stablelm-zephyr-3b
Model Size	3b
Required VRAM	5.9 GB
Updated	2025-09-23
Maintainer	Walmart-the-bag
Model Type	stablelm_epoch
Model Files	5.9 GB 5.9 GB 0.0 GB
Model Architecture	StableLMEpochForCausalLM
License	other
Context Length	4096
Model Max Length	4096
Transformers Version	4.34.1
Tokenizer Class	GPTNeoXTokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	50304
Torch Data Type	float16

Quantized Models of the Zephyr Quiklang 3B

Model	Likes	Downloads	VRAM
Zephyr Quiklang 3B GGUF	4	544	1 GB
Zephyr Quiklang 3B GPTQ	1	10	1 GB

Best Alternatives to Zephyr Quiklang 3B

Best Alternatives	Context / RAM	Downloads	Likes
Stable Code 3B Mlx	16K / 5.6 GB	42	1
Aura 3B	4K / 5.6 GB	3	2
Slim Extract	4K / 5.6 GB	169	13
Slim Boolean	4K / 5.6 GB	11	4
Slim Tags 3B	4K / 5.6 GB	15	4
Slim Sa Ner	4K / 5.6 GB	3	6
Slim Summary	4K / 5.6 GB	13	8
Slim Xsum	4K / 5.6 GB	16	6
Tofu 3B	4K / 5.6 GB	8	2
Memphis CoT 3B	4K / 5.6 GB	23	31

Note: green Score (e.g. "73.2") means that the model is better than Walmart-the-bag/zephyr-quiklang-3b.

Rank the Zephyr Quiklang 3B Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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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Zephyr Quiklang 3B by Walmart-the-bag