What are the hardware requirements for SFR Embedding Mistral AWQ?

SFR Embedding Mistral AWQ requires approximately 4.2 GB of VRAM and supports a context window of 32K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed SFR Embedding Mistral AWQ and how large is it?

SFR Embedding Mistral AWQ is developed by tarob0ba, a model with 1.2b 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 SFR Embedding Mistral AWQ?

SFR Embedding Mistral AWQ is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

SFR Embedding Mistral AWQ by tarob0ba — VRAM 4.2GB, 32K context

Name: SFR Embedding Mistral AWQ
Author: tarob0ba

SFR Embedding Mistral AWQ is an open-source language model by tarob0ba. Features: 1.2b LLM, VRAM: 4.2GB, Context: 32K, Quantized, LLM Explorer Score: 0.12.

4-bit Autotrain compatible Awq Endpoints compatible Mistral Quantized Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/tarob0ba/SFR-Embedding-Mistral-AWQ

SFR Embedding Mistral AWQ Benchmarks

LLME Score: 0.12275

^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)

SFR Embedding Mistral AWQ (tarob0ba/SFR-Embedding-Mistral-AWQ)

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SFR Embedding Mistral AWQ Parameters and Internals

LLM Name	SFR Embedding Mistral AWQ
Repository 🤗	https://huggingface.co/tarob0ba/SFR-Embedding-Mistral-AWQ
Model Size	1.2b
Required VRAM	4.2 GB
Updated	2024-12-26
Maintainer	tarob0ba
Model Type	mistral
Model Files	4.2 GB
AWQ Quantization	Yes
Quantization Type	awq
Model Architecture	MistralForCausalLM
Context Length	32768
Model Max Length	32768
Transformers Version	4.41.0.dev0
Tokenizer Class	LlamaTokenizer
Padding Token	</s>
Vocabulary Size	32000
Torch Data Type	float16

Best Alternatives to SFR Embedding Mistral AWQ

Best Alternatives	Context / RAM	Downloads	Likes
Quantized Kheopss AWQ 4bits V1	32K / 4.2 GB	17	0
MistralLite AWQ	32K / 4.2 GB	42	3
Yugo55A GPT AWQ	32K / 4.2 GB	6	0
Openchat 3.5 16K AWQ	32K / 4.2 GB	3	1
Openchat 3.5 0106 AWQ	8K / 4.2 GB	3	1
Hare1.0 Beta	32K / 2.4 GB	50	1
Mistralmawq	32K / 4.2 GB	14	0
... Finetune 16bit Ver9 Main GPTQ	32K / 4.2 GB	6	0
Turdus GPTQ	32K / 4.2 GB	1	5

Note: green Score (e.g. "73.2") means that the model is better than tarob0ba/SFR-Embedding-Mistral-AWQ.

Rank the SFR Embedding Mistral AWQ 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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SFR Embedding Mistral AWQ by tarob0ba

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SFR Embedding Mistral AWQ Benchmarks

SFR Embedding Mistral AWQ Parameters and Internals

Best Alternatives to SFR Embedding Mistral AWQ

Rank the SFR Embedding Mistral AWQ Capabilities

What open-source LLMs or SLMs are you in search of? 54931 in total.