What are the hardware requirements for Code 290K 6.7B Instruct?

Code 290K 6.7B Instruct requires approximately 13.5 GB of VRAM and supports a context window of 16K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Code 290K 6.7B Instruct and how large is it?

Code 290K 6.7B Instruct is developed by ajibawa-2023, 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 Code 290K 6.7B Instruct perform on standard benchmarks?

Code 290K 6.7B Instruct has the following published scores: MMLU 34.89. Compare against reference models on this page or on the LLM Explorer leaderboards.

Code 290K 6.7B Instruct by ajibawa-2023 — VRAM 13.5GB, 16K context

Name: Code 290K 6.7B Instruct
Author: ajibawa-2023

Code 290K 6.7B Instruct is an open-source language model by ajibawa-2023. Features: 6.7b LLM, VRAM: 13.5GB, Context: 16K, License: other, Instruction-Based, Code Generating, LLM Explorer Score: 0.15, Arc: 34.9, HellaSwag: 52, MMLU: 34.9, GSM8K: 3.5.

Code Codegen Conversational Dataset:ajibawa-2023/code-290k... En Endpoints compatible Instruct Llama Model-index Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/ajibawa-2023/Code-290k-6.7B-Instruct

Code 290K 6.7B Instruct Parameters and Internals

Model Type

text generation, code generation

Use Cases

Areas:

Research, Commercial applications

Primary Use Cases:

Code generation, Providing detailed code explanations

Additional Notes

Quantized version available thanks to Bartowski

Supported Languages

Python (yes), Java (yes), JavaScript (yes), GO (yes), C++ (yes), Rust (yes), Ruby (yes), Sql (yes), MySql (yes), R (yes), Julia (yes), Haskell (yes)

Training Details

Data Sources:

Code-290k-ShareGPT dataset

Data Volume:

290,000 sets of codes

Methodology:

Fine-tuning

Training Time:

85 hours (3 epochs)

Hardware Used:

4 x A100 80GB GPUs

Input Output

Input Format:

Alpaca format

Accepted Modalities:

text

Output Format:

text with code and explanations

Performance Tips:

Modify prompt as needed for best results.

LLM Name	Code 290K 6.7B Instruct
Repository 🤗	https://huggingface.co/ajibawa-2023/Code-290k-6.7B-Instruct
Model Size	6.7b
Required VRAM	13.5 GB
Updated	2026-07-27
Maintainer	ajibawa-2023
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 0.0 GB
Supported Languages	en
Generates Code	Yes
Model Architecture	LlamaForCausalLM
License	other
Context Length	16384
Model Max Length	16384
Transformers Version	4.37.2
Tokenizer Class	LlamaTokenizer
Padding Token	<｜end▁of▁sentence｜>
Vocabulary Size	32256
Torch Data Type	bfloat16

Rank the Code 290K 6.7B Instruct 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 Code 290K 6.7B Instruct

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
...Coder6.7b Reflct Rmsprop Iter1	16K / 13.5 GB	62	0
Ds Coder6.7b Rmsprop Iter1	16K / 13.5 GB	67	0
Datascience Coder 6.7B	16K / 13.5 GB	204	4

Note: green Score (e.g. "73.2") means that the model is better than ajibawa-2023/Code-290k-6.7B-Instruct.