What are the hardware requirements for HashBlitz 1 7B Max?

HashBlitz 1 7B Max requires approximately 4.5 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 HashBlitz 1 7B Max and how large is it?

HashBlitz 1 7B Max is developed by Ginckl, a model with 7b 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 HashBlitz 1 7B Max?

HashBlitz 1 7B Max is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

HashBlitz 1 7B Max by Ginckl — VRAM 4.5GB, 32K context

Name: HashBlitz 1 7B Max
Author: Ginckl

HashBlitz 1 7B Max is an open-source language model by Ginckl. Features: 7b LLM, VRAM: 4.5GB, Context: 32K, License: apache-2.0, Quantized, Instruction-Based, Code Generating, LLM Explorer Score: 0.33.

Base model:quantized:unsloth/q... Base model:unsloth/qwen2.5-cod... Code Codegen Coder Conversational En Endpoints compatible Es Fr Gguf Ginckl Instruct Q4 Quantized Qwen Qwen2 Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/Ginckl/HashBlitz-1-7B-Max

HashBlitz 1 7B Max Parameters and Internals

LLM Name	HashBlitz 1 7B Max
Repository 🤗	https://huggingface.co/Ginckl/HashBlitz-1-7B-Max
Base Model(s)	unsloth/Qwen2.5-Coder-7B-Instruct unsloth/Qwen2.5-Coder-7B-Instruct
Model Size	7b
Required VRAM	4.5 GB
Updated	2026-09-19
Maintainer	Ginckl
Model Type	qwen2
Instruction-Based	Yes
Model Files	4.7 GB 4.5 GB 5.4 GB 8.1 GB 15.2 GB
Supported Languages	en es fr
GGUF Quantization	Yes
Quantization Type	gguf\|q4\|q4_k\|q5_k
Generates Code	Yes
Model Architecture	Qwen2ForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|PAD_TOKEN\|>
Vocabulary Size	152064
Torch Data Type	float16
Errors	replace

Rank the HashBlitz 1 7B Max 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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