What are the hardware requirements for HighSpeedChickenFeed?

HighSpeedChickenFeed requires approximately 20.5 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed HighSpeedChickenFeed and how large is it?

HighSpeedChickenFeed is developed by darkc0de, a model with 10b 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 HighSpeedChickenFeed?

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

HighSpeedChickenFeed by darkc0de — VRAM 20.5GB

Name: HighSpeedChickenFeed
Author: darkc0de

HighSpeedChickenFeed is an open-source language model by darkc0de. Features: 10b LLM, VRAM: 20.5GB, Instruction-Based, Merged, LLM Explorer Score: 0.16.

Merged Model Arxiv:2306.01708 Autotrain compatible Base model:daemontatox/ra reas... Base model:huihui-ai/falcon3-1... Conversational Endpoints compatible Instruct Llama Lora Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/darkc0de/HighSpeedChickenFeed

HighSpeedChickenFeed Parameters and Internals

LLM Name	HighSpeedChickenFeed
Repository 🤗	https://huggingface.co/darkc0de/HighSpeedChickenFeed
Base Model(s)	RA Reasoner ...lcon3 10B Instruct Abliterated Daemontatox/RA_Reasoner huihui-ai/Falcon3-10B-Instruct-abliterated
Merged Model	Yes
Model Size	10b
Required VRAM	20.5 GB
Updated	2025-09-23
Maintainer	darkc0de
Instruction-Based	Yes
Model Files	0.3 GB 4.9 GB: 1-of-5 4.9 GB: 2-of-5 4.9 GB: 3-of-5 4.9 GB: 4-of-5 0.9 GB: 5-of-5
Model Architecture	AutoModelForCausalLM
Model Max Length	32768
Is Biased	none
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<\|pad\|>
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	gate_proj\|v_proj\|q_proj\|k_proj\|down_proj\|o_proj\|up_proj
LoRA Alpha	32
LoRA Dropout	0.05
R Param	16

Rank the HighSpeedChickenFeed 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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Occiglot10b	0K / 6.5 GB	2	1