What are the hardware requirements for FluentlyQwen3 Coder 1.7B?

FluentlyQwen3 Coder 1.7B requires approximately 4.1 GB of VRAM and supports a context window of 40K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed FluentlyQwen3 Coder 1.7B and how large is it?

FluentlyQwen3 Coder 1.7B is developed by fluently, a model with 1.7b 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 FluentlyQwen3 Coder 1.7B?

FluentlyQwen3 Coder 1.7B is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

FluentlyQwen3 Coder 1.7B by fluently — VRAM 4.1GB, 40K context

Name: FluentlyQwen3 Coder 1.7B
Author: fluently

FluentlyQwen3 Coder 1.7B is an open-source language model by fluently. Features: 1.7b LLM, VRAM: 4.1GB, Context: 40K, License: apache-2.0, Code Generating, LLM Explorer Score: 0.2.

Autotrain compatible Axolotl Base model:finetune:fluently/f... Base model:fluently/fluentlyqw... Code Codegen Coder Coding Conversational Dataset:nvidia/opencodereasoni... En Endpoints compatible Fluently Fluently-lm Merge Qwen3 Region:us Ru Safetensors Sft Trained Trl Unsloth

Model Card on HF 🤗: https://huggingface.co/fluently/FluentlyQwen3-Coder-1.7B

FluentlyQwen3 Coder 1.7B Parameters and Internals

LLM Name	FluentlyQwen3 Coder 1.7B
Repository 🤗	https://huggingface.co/fluently/FluentlyQwen3-Coder-1.7B
Base Model(s)	fluently/FluentlyQwen3-1.7B fluently/FluentlyQwen3-1.7B
Model Size	1.7b
Required VRAM	4.1 GB
Updated	2025-09-09
Maintainer	fluently
Model Type	qwen3
Model Files	4.1 GB
Supported Languages	en ru code
Generates Code	Yes
Model Architecture	Qwen3ForCausalLM
License	apache-2.0
Context Length	40960
Model Max Length	40960
Transformers Version	4.56.0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151936
Errors	replace

Rank the FluentlyQwen3 Coder 1.7B capabilities

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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

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