What are the hardware requirements for Tinyllama PY CODER 4bit Lora 4K V4?

Tinyllama PY CODER 4bit Lora 4K V4 requires approximately 0.7 GB of VRAM and supports a context window of 4K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Tinyllama PY CODER 4bit Lora 4K V4 and how large is it?

Tinyllama PY CODER 4bit Lora 4K V4 is developed by Ramikan-BR, a model with 1.1b 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 Tinyllama PY CODER 4bit Lora 4K V4?

Tinyllama PY CODER 4bit Lora 4K V4 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Tinyllama PY CODER 4bit Lora 4K V4 by Ramikan-BR — VRAM 0.7GB, 4K context

Name: Tinyllama PY CODER 4bit Lora 4K V4
Author: Ramikan-BR

Tinyllama PY CODER 4bit Lora 4K V4 is an open-source language model by Ramikan-BR. Features: 1.1b LLM, VRAM: 0.7GB, Context: 4K, License: apache-2.0, Quantized, Code Generating, LLM Explorer Score: 0.12.

4bit Base model:quantized:ramikan-b... Base model:ramikan-br/tinyllam... Codegen En Endpoints compatible Gguf Llama Lora Pytorch Q4 Quantized Region:us Safetensors Sft Trl Unsloth

Model Card on HF 🤗: https://huggingface.co/Ramikan-BR/tinyllama_PY-CODER-4bit-lora_4k-v4

Tinyllama PY CODER 4bit Lora 4K V4 Parameters and Internals

Model Type

text-generation-inference, transformers, unsloth

Additional Notes

This model was finetuned from Ramikan-BR/tinyllama_PY-CODER-bnb-4bit-lora_4k-q4_k_m-v3.

Training Details

Methodology:

Trained 2x faster with Unsloth and Huggingface's TRL library

LLM Name	Tinyllama PY CODER 4bit Lora 4k V4
Repository 🤗	https://huggingface.co/Ramikan-BR/tinyllama_PY-CODER-4bit-lora_4k-v4
Base Model(s)	Ramikan-BR/tinyllama_PY-CODER-bnb-4bit-lora_4k-q4_k_m-v3 Ramikan-BR/tinyllama_PY-CODER-bnb-4bit-lora_4k-q4_k_m-v3
Model Size	1.1b
Required VRAM	0.7 GB
Updated	2026-08-03
Maintainer	Ramikan-BR
Model Type	llama
Model Files	2.2 GB 2.2 GB 0.7 GB
Supported Languages	en
GGUF Quantization	Yes
Quantization Type	gguf\|q4\|4bit
Generates Code	Yes
Model Architecture	LlamaForCausalLM
License	apache-2.0
Context Length	4096
Model Max Length	4096
Transformers Version	4.40.1
Tokenizer Class	LlamaTokenizer
Padding Token	<unk>
Vocabulary Size	32000
LoRA Model	Yes
Torch Data Type	float16

Rank the Tinyllama PY CODER 4bit Lora 4K V4 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 Tinyllama PY CODER 4bit Lora 4K V4

Model	Likes	Downloads	VRAM
...llama PY CODER 4bit Lora 4k V5	0	24	0 GB

Best Alternatives to Tinyllama PY CODER 4bit Lora 4K V4

Best Alternatives	Context / RAM	Downloads
Tinyllama Coder Py V13	4K / 0.7 GB	203
Tinyllama Coder Py V12	4K / 0.7 GB	184
Tinyllama Coder Py 4bit V3	4K / 1.2 GB	32
Tinyllama Coder Py 4bit V4	4K / 1.2 GB	17
...llama PY CODER 4bit Lora 4k V5	4K / 0.7 GB	24
Tinyllama Coder Py 4bit V10	4K / 0.7 GB	65
Tinyllama Coder Py V20	4K / 0.7 GB	155
Tinyllama Coder Py V18	4K / 0.7 GB	190
Tinyllama Coder Py V19	4K / 0.7 GB	102
Tinyllama Coder Py V21	4K / 0.7 GB	64

Note: green Score (e.g. "73.2") means that the model is better than Ramikan-BR/tinyllama_PY-CODER-4bit-lora_4k-v4.