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Hc Mistral Alpaca Merged AWQ by parlance-labs

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Hc Mistral Alpaca Merged AWQ is an open-source language model by parlance-labs. Features: 7.2b LLM, VRAM: 4.2GB, Context: 32K, Quantized, Merged, LLM Explorer Score: 0.12.

  Merged Model   4-bit   Awq   Endpoints compatible   Mistral   Quantized   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/parlance-labs/hc-mistral-alpaca-merged-awq 

Hc Mistral Alpaca Merged AWQ Benchmarks

LLME Score: 0.1195
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Hc Mistral Alpaca Merged AWQ (parlance-labs/hc-mistral-alpaca-merged-awq)
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Hc Mistral Alpaca Merged AWQ Parameters and Internals

Model Type 
Causal Language Model
Additional Notes 
The model is quantized using AWQ with 4-bit precision, optimized for lower memory usage and faster inference, while maintaining competitive performance.
Input Output 
Input Format:
Text inputs for causal language modeling.
Accepted Modalities:
text
Output Format:
Text output generated by the model.
LLM NameHc Mistral Alpaca Merged AWQ
Repository 🤗https://huggingface.co/parlance-labs/hc-mistral-alpaca-merged-awq 
Merged ModelYes
Model Size7.2b
Required VRAM4.2 GB
Updated2026-05-19
Maintainerparlance-labs
Model Typemistral
Model Files  4.2 GB
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureMistralForCausalLM
Context Length32768
Model Max Length32768
Transformers Version4.38.2
Tokenizer ClassLlamaTokenizer
Padding Token</s>
Vocabulary Size32000
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than parlance-labs/hc-mistral-alpaca-merged-awq.

Rank the Hc Mistral Alpaca Merged AWQ Capabilities

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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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