What are the hardware requirements for AmberChat?

AmberChat requires approximately 13.5 GB of VRAM and supports a context window of 2K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed AmberChat and how large is it?

AmberChat is developed by LLM360, a model with 6.7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does AmberChat perform on standard benchmarks?

AmberChat has the following published scores: MMLU 38.75. Compare against reference models on this page or on the LLM Explorer leaderboards.

AmberChat by LLM360 — VRAM 13.5GB, 2K context

Name: AmberChat
Author: LLM360

AmberChat is an open-source language model by LLM360. Features: 6.7b LLM, VRAM: 13.5GB, Context: 2K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.14, Arc: 42.9, HellaSwag: 74, MMLU: 38.8, GSM8K: 5.5.

Arxiv:2312.06550 Dataset:icybee/share gpt 90k v... Dataset:wizardlm/wizardlm evol... Deploy:azure En Endpoints compatible Instruct Llama Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/LLM360/AmberChat

AmberChat Parameters and Internals

Model Type

Language model

Use Cases

Areas:

Research, Commercial applications

Primary Use Cases:

Instruction following

Supported Languages

en (proficient)

Training Details

Data Sources:

WizardLM/WizardLM_evol_instruct_V2_196k, icybee/share_gpt_90k_v1

Context Length:

2048

Model Architecture:

LLaMA-7B

Input Output

Input Format:

Text

Accepted Modalities:

text

Output Format:

Text

LLM Name	AmberChat
Repository 🤗	https://huggingface.co/LLM360/AmberChat
Model Size	6.7b
Required VRAM	13.5 GB
Updated	2026-05-30
Maintainer	LLM360
Model Type	llama
Instruction-Based	Yes
Model Files	4.9 GB: 1-of-3 5.0 GB: 2-of-3 3.6 GB: 3-of-3
Supported Languages	en
Model Architecture	LlamaForCausalLM
License	apache-2.0
Context Length	2048
Model Max Length	2048
Transformers Version	4.35.2
Tokenizer Class	LlamaTokenizer
Padding Token	<unk>
Vocabulary Size	32000
Torch Data Type	bfloat16

Rank the AmberChat 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

Quantized Models of the AmberChat

Model	Likes	Downloads	VRAM
AmberChat GGUF	2	315	2 GB
AmberChat AWQ	1	4	3 GB
AmberChat GPTQ	1	1	3 GB

Best Alternatives to AmberChat

Best Alternatives	Context / RAM	Downloads	Likes
Speechless Coder Ds 6.7B	16K / 13.5 GB	459	7
...s Coder6.7b Reflct Adamw Iter1	16K / 13.5 GB	475	0
...Coder6.7b Reflct Rmsprop Iter1	16K / 13.5 GB	95	0
...Coder6.7b Reflct Rmsprop Iter1	16K / 13.5 GB	110	0
...r6.7b Pos Reflct Rmsprop Iter1	16K / 13.5 GB	87	0
...ir4 Ds Coder6.7b Rmsprop Iter1	16K / 13.5 GB	43	0
...r6.7b Pos Reflct Rmsprop Iter1	16K / 13.5 GB	90	0
Code 290K 6.7B Instruct	16K / 13.5 GB	87	6
...Coder6.7b Reflct Rmsprop Iter1	16K / 13.5 GB	62	0
Ds Coder6.7b Rmsprop Iter1	16K / 13.5 GB	67	0

Note: green Score (e.g. "73.2") means that the model is better than LLM360/AmberChat.