What are the hardware requirements for Llama 2 70B Instruct V0.1?

Llama 2 70B Instruct V0.1 requires approximately 1.1 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Llama 2 70B Instruct V0.1 and how large is it?

Llama 2 70B Instruct V0.1 is developed by dfurman, a model with 70b 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 2 70B Instruct V0.1?

Llama 2 70B Instruct V0.1 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Llama 2 70B Instruct V0.1 by dfurman — VRAM 1.1GB

Name: Llama 2 70B Instruct V0.1
Author: dfurman

Llama 2 70B Instruct V0.1 is an open-source language model by dfurman. Features: 70b LLM, VRAM: 1.1GB, License: llama2, Instruction-Based, LLM Explorer Score: 0.08.

Arxiv:2002.05202 Arxiv:2104.09864 Arxiv:2305.13245 Adapter Base model:adapter:meta-llama/... Base model:meta-llama/llama-2-... Dataset:ehartford/dolphin Finetuned Instruct Llama2 Lora Model-index Peft Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/dfurman/Llama-2-70B-Instruct-v0.1

Llama 2 70B Instruct V0.1 Parameters and Internals

Model Type

text generation

Use Cases

Areas:

research

Limitations:

Model can produce factually incorrect output that may include offensive material.

Considerations:

Should not be relied upon for factually accurate information.

Additional Notes

Example prompts and specific usage scenarios are provided in the documentation.

Training Details

Data Sources:

ehartford/dolphin

Methodology:

parameter-efficient QLoRA finetuning

Context Length:

4096

Training Time:

roughly 17 hours

Hardware Used:

single H100 (80 GB PCIe)

Model Architecture:

Standard decoder-only transformer modified with SwiGLU activation, rotary positional embeddings (RoPE), grouped-query attention (GQA), and other specifications.

Responsible Ai Considerations

Fairness:

The model may produce biased outputs.

Transparency:

The evaluation results are made transparent through the open LLM leaderboard.

Accountability:

Model was fine-tuned by Daniel Furman.

Input Output

Input Format:

Standard decoder-only transformer input format, processed using tokenization.

Accepted Modalities:

text

Output Format:

Text-based generation appropriate for instructed tasks.

Release Notes

Version:

v0.1

Date:

2023-07-23

Notes:

This model, fine-tuned by Daniel Furman, is intended primarily for research purposes and ranks 6th on the Open LLM Leaderboard.

LLM Name	Llama 2 70B Instruct V0.1
Repository 🤗	https://huggingface.co/dfurman/Llama-2-70B-Instruct-v0.1
Base Model(s)	Llama 2 70B Hf meta-llama/Llama-2-70b-hf
Model Size	70b
Required VRAM	1.1 GB
Updated	2026-06-30
Maintainer	dfurman
Instruction-Based	Yes
Model Files	1.1 GB 1.1 GB
Model Architecture	Adapter
License	llama2
Is Biased	none
Tokenizer Class	LlamaTokenizer
Beginning of Sentence Token	<s>
End of Sentence Token	</s>
Unk Token	<unk>
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	q_proj\|k_proj\|v_proj\|o_proj
LoRA Alpha	16
LoRA Dropout	0.1
R Param	64

Rank the Llama 2 70B Instruct V0.1 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 2 70B Instruct V0.1

Best Alternatives	Context / RAM	Downloads	Likes
Llama 3.1 70B Abliterated Lora	0K / 1.7 GB	72	4
Llama 3 70B Instruct Spider	0K / 141.9 GB	6	0
Llama3v1	0K / 0.1 GB	5	0
LLaMA 2 Wizard 70B QLoRA	0K / 1.7 GB	0	4
Saiga2 70b Lora	0K / 0.3 GB	0	14

Note: green Score (e.g. "73.2") means that the model is better than dfurman/Llama-2-70B-Instruct-v0.1.