What are the hardware requirements for Llama 3 1M Context Gradient Lora?

Llama 3 1M Context Gradient Lora requires approximately 3.5 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Llama 3 1M Context Gradient Lora and how large is it?

Llama 3 1M Context Gradient Lora is developed by winglian, a model with 1m 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 Llama 3 1M Context Gradient Lora?

Llama 3 1M Context Gradient Lora is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Llama 3 1M Context Gradient Lora by winglian — VRAM 3.5GB

Name: Llama 3 1M Context Gradient Lora
Author: winglian

Llama 3 1M Context Gradient Lora is an open-source language model by winglian. Features: 1m LLM, VRAM: 3.5GB, LLM Explorer Score: 0.12.

Adapter Finetuned Lora Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/winglian/llama-3-1m-context-gradient-lora

Llama 3 1M Context Gradient Lora Parameters and Internals

Model Type

text_generation, instruction-following

Use Cases

Areas:

research, education, commercial

Applications:

chatbots, virtual assistants

Primary Use Cases:

text generation, instruction following

Limitations:

Not suitable for critical real-time applications

Considerations:

Test thoroughly before deployment in sensitive environments

Supported Languages

English (high), Spanish (medium), French (low)

Training Details

Data Sources:

open-access datasets, Instruction tuning datasets

Data Volume:

1048k instructions

Methodology:

LoRA tuning on self_attn modules

Context Length:

2048

Training Time:

2 weeks

Hardware Used:

8 A100 GPUs

Model Architecture:

LLaMA architecture with LoRA tuning

Safety Evaluation

Methodologies:

adversarial testing

Findings:

robust to language drift

Risk Categories:

bias, misinformation

Ethical Considerations:

Standard AI ethical practices assumed during model creation.

Responsible Ai Considerations

Fairness:

Trained on a diverse dataset to reduce bias

Transparency:

Model weights and training configurations are available publicly

Accountability:

Gradient AI maintains accountability for model outputs

Mitigation Strategies:

LoRA technique applied to mitigate overfitting

Input Output

Input Format:

Plain text input with instructions

Accepted Modalities:

text

Output Format:

Generated text response

Performance Tips:

Utilize prompt engineering for better results.

Release Notes

Version:

1.0

Date:

2023-09-15

Notes:

Initial release with LoRA tuning.

LLM Name	Llama 3 1M Context Gradient Lora
Repository 🤗	https://huggingface.co/winglian/llama-3-1m-context-gradient-lora
Model Size	1m
Required VRAM	3.5 GB
Updated	2026-08-04
Maintainer	winglian
Model Files	3.5 GB
Model Architecture	Adapter
Is Biased	none
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	k_proj\|v_proj\|o_proj\|q_proj
LoRA Alpha	1024
LoRA Dropout	0
R Param	1024

Rank the Llama 3 1M Context Gradient Lora 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 Llama 3 1M Context Gradient Lora

Best Alternatives	Context / RAM	Downloads	Likes
...ma 3 1B It Reasoning Grpo Lora	0K / 0.2 GB	14	4
Openhermes 1B Olmo Sft Qlora	0K / 0 GB	7	0
Zephyr 1B Olmo Sft Qlora	0K / 0 GB	5	1
...iabolic6045 ELN AOC CAIN QLoRA	0K / 0 GB	5	0
Llama 3.2 1B Airoboros Merged	0K / 0.1 GB	5	0
...la Alpaca Orca Instruct V0.0.1	0K / 1.1 GB	88	0
Peft Starcoder Lora Python	0K / 0 GB	14	0
Prem 1B 32K	0K / 1.4 GB	7	1
Prem 1B Chat 32K	0K / 1.4 GB	5	1
Falcon Arxiv Long Summary 1B	0K / 0.2 GB	4	1

Note: green Score (e.g. "73.2") means that the model is better than winglian/llama-3-1m-context-gradient-lora.