What are the hardware requirements for Hope Forth?

Hope Forth requires approximately 1 GB of VRAM and supports a context window of 128K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Hope Forth and how large is it?

Hope Forth is developed by krishna195, a model with 1.3b 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 Hope Forth?

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

Hope Forth by krishna195 — VRAM 1GB, 128K context

Name: Hope Forth
Author: krishna195

Hope Forth is an open-source language model by krishna195. Features: 1.3b LLM, VRAM: 1GB, Context: 128K, License: apache-2.0, LLM Explorer Score: 0.17.

4-bit Base model:krishna195/hope thi... Base model:quantized:krishna19... Bitsandbytes Conversational En Endpoints compatible Llama Region:us Safetensors Sft Trl Unsloth

Model Card on HF 🤗: https://huggingface.co/krishna195/hope_forth

Hope Forth Parameters and Internals

LLM Name	Hope Forth
Repository 🤗	https://huggingface.co/krishna195/hope_forth
Base Model(s)	Hope Third krishna195/hope_third
Model Size	1.3b
Required VRAM	1 GB
Updated	2026-08-04
Maintainer	krishna195
Model Type	llama
Model Files	1.0 GB
Supported Languages	en
Model Architecture	LlamaForCausalLM
License	apache-2.0
Context Length	131072
Model Max Length	131072
Transformers Version	4.50.0
Tokenizer Class	PreTrainedTokenizer
Padding Token	<\|finetune_right_pad_id\|>
Vocabulary Size	128256
Torch Data Type	float16

Rank the Hope Forth capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

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

Best Alternatives to Hope Forth

Best Alternatives	Context / RAM	Downloads	Likes
Hope Second	128K / 1 GB	10	0
Hope Third	128K / 1 GB	9	0
Second Edition	128K / 1 GB	8	0
Fifth Edition	128K / 1 GB	7	0
Fourth Edition	128K / 1 GB	6	0
Third Edition	128K / 1 GB	5	0
Llama 1.3B 32K	32K / 2.6 GB	3	2
Llama 1.3B 16K	16K / 2.6 GB	5	0
Deepseek Coder 1.3B Instruct	16K / 2.7 GB	92255	176
Sam Reason S1.5	16K / 2.7 GB	11	1

Note: green Score (e.g. "73.2") means that the model is better than krishna195/hope_forth.