What are the hardware requirements for Shenzhi Wang Llama3 8B Chinese Chat AWQ 4bit Smashed?

Shenzhi Wang Llama3 8B Chinese Chat AWQ 4bit Smashed requires approximately 5.8 GB of VRAM and supports a context window of 8K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Where can I download or evaluate Shenzhi Wang Llama3 8B Chinese Chat AWQ 4bit Smashed?

Shenzhi Wang Llama3 8B Chinese Chat AWQ 4bit Smashed is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Shenzhi Wang Llama3 8B Chinese Chat AWQ 4bit Smashed by PrunaAI — VRAM 5.8GB, 8K context

Name: Shenzhi Wang Llama3 8B Chinese Chat AWQ 4bit Smashed
Author: PrunaAI

Shenzhi Wang Llama3 8B Chinese Chat AWQ 4bit Smashed is an open-source language model by PrunaAI. Features: 8b LLM, VRAM: 5.8GB, Context: 8K, Quantized, LLM Explorer Score: 0.12.

4-bit 4bit Awq Base model:quantized:shenzhi-w... Base model:shenzhi-wang/llama3... Llama Pruna-ai Quantized Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/PrunaAI/shenzhi-wang-Llama3-8B-Chinese-Chat-AWQ-4bit-smashed

Shenzhi Wang Llama3 8B Chinese Chat AWQ 4bit Smashed Parameters and Internals

Model Type

text generation

Use Cases

Limitations:

Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.

Input Output

Accepted Modalities:

text

Performance Tips:

To test efficiency gains, evaluate model performance directly in the desired use-case scenario. Consider running the model across different batch sizes or hardware to assess performance variations.

LLM Name	Shenzhi Wang Llama3 8B Chinese Chat AWQ 4bit Smashed
Repository 🤗	https://huggingface.co/PrunaAI/shenzhi-wang-Llama3-8B-Chinese-Chat-AWQ-4bit-smashed
Base Model(s)	Llama3 8B Chinese Chat shenzhi-wang/Llama3-8B-Chinese-Chat
Model Size	8b
Required VRAM	5.8 GB
Updated	2025-09-23
Maintainer	PrunaAI
Model Type	llama
Model Files	4.7 GB: 1-of-2 1.1 GB: 2-of-2
AWQ Quantization	Yes
Quantization Type	awq\|4bit
Model Architecture	LlamaForCausalLM
Context Length	8192
Model Max Length	8192
Transformers Version	4.40.0
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<\|eot_id\|>
Vocabulary Size	128256
Torch Data Type	float16

Rank the Shenzhi Wang Llama3 8B Chinese Chat AWQ 4bit Smashed 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 Shenzhi Wang Llama3 8B Chinese Chat AWQ 4bit Smashed

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...8B Instruct Gradient 1048K AWQ	1024K / 5.8 GB	8	0
...radient 1048K AWQ 4bit Smashed	1024K / 5.8 GB	7	1
...Instruct 262K AWQ 4bit Smashed	256K / 5.8 GB	5	4
...ta Llama 3 8B Instruct 64K AWQ	64K / 5.8 GB	6	0
... Instruct 8B 32K V0.1 4bit AWQ	64K / 5.8 GB	9	0
Llama 3 8B Instruct AWQ	8K / 5.8 GB	25458	31
Meta Llama 3 8B Instruct AWQ	8K / 5.8 GB	6	0
Meta Llama 3 8B Instruct AWQ	8K / 5.8 GB	563	0
Meta Llama 3 8B Instruct AWQ	8K / 5.8 GB	7	0
Meta Llama 3 8B Instruct AWQ	8K / 5.8 GB	401	5

Note: green Score (e.g. "73.2") means that the model is better than PrunaAI/shenzhi-wang-Llama3-8B-Chinese-Chat-AWQ-4bit-smashed.