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Mistral Ft Test by amre

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Mistral Ft Test is an open-source language model by amre. Features: 7b LLM, VRAM: 14.4GB, Context: 32K, Instruction-Based, LLM Explorer Score: 0.14.

  Conversational   Endpoints compatible   Instruct   Mistral   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/airabbitX/mistral-ft-test 

Mistral Ft Test Benchmarks

LLME Score: 0.14207
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Mistral Ft Test (airabbitX/mistral-ft-test)
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Mistral Ft Test Parameters and Internals

LLM NameMistral Ft Test
Repository 🤗https://huggingface.co/airabbitX/mistral-ft-test 
Model Size7b
Required VRAM14.4 GB
Updated2026-03-27
Maintaineramre
Model Typemistral
Instruction-BasedYes
Model Files  1.9 GB: 1-of-8   1.9 GB: 2-of-8   2.0 GB: 3-of-8   1.9 GB: 4-of-8   2.0 GB: 5-of-8   1.9 GB: 6-of-8   2.0 GB: 7-of-8   0.8 GB: 8-of-8
Model ArchitectureMistralForCausalLM
Context Length32768
Model Max Length32768
Transformers Version4.42.3
Tokenizer ClassLlamaTokenizer
Padding Token</s>
Vocabulary Size32768
Torch Data Typebfloat16

Best Alternatives to Mistral Ft Test

Best Alternatives
Context / RAM
Downloads
Likes
...Nemo Instruct 2407 Abliterated1000K / 24.5 GB25420
SpydazWeb AI HumanAI RP512K / 14.4 GB141
SpydazWeb AI HumanAI 002512K / 14.4 GB181
...daz Web AI ChatML 512K Project512K / 14.5 GB120
... Summarize 64K QLoRANET Merged128K / 4.1 GB60
...1 Summarize 64K LoRANET Merged128K / 14.4 GB60
Mistral 7B Instruct V0.232K / 14.4 GB33160952630
Mistral 7B Instruct V0.132K / 14.4 GB3727181828
Mistral 7B Instruct V0.232K / 14.4 GB57590
Mixtral AI CyberCoder32K / 14.3 GB00
Note: green Score (e.g. "73.2") means that the model is better than airabbitX/mistral-ft-test.

Rank the Mistral Ft Test 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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