What are the hardware requirements for Tinyllama PY CODER 4bit Lora 4K V8?

Tinyllama PY CODER 4bit Lora 4K V8 requires approximately 0.4 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Tinyllama PY CODER 4bit Lora 4K V8 and how large is it?

Tinyllama PY CODER 4bit Lora 4K V8 is developed by Ramikan-BR. 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 V8?

Tinyllama PY CODER 4bit Lora 4K V8 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 V8 by Ramikan-BR — VRAM 0.4GB

Name: Tinyllama PY CODER 4bit Lora 4K V8
Author: Ramikan-BR

Tinyllama PY CODER 4bit Lora 4K V8 is an open-source language model by Ramikan-BR. Features: LLM, VRAM: 0.4GB, 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 Quantized Region:us Safetensors Unsloth

Model Card on HF 🤗: https://huggingface.co/Ramikan-BR/tinyllama_PY-CODER-4bit-lora_4k-v8

Tinyllama PY CODER 4bit Lora 4K V8 Parameters and Internals

Model Type

text-generation-inference, transformers, unsloth, llama, gguf

Additional Notes

This model was finetuned from Ramikan-BR/tinyllama_PY-CODER-4bit-lora_4k-v7 and was trained 2x faster with Unsloth and Huggingface's TRL library.

LLM Name	Tinyllama PY CODER 4bit Lora 4k V8
Repository 🤗	https://huggingface.co/Ramikan-BR/tinyllama_PY-CODER-4bit-lora_4k-v8
Base Model(s)	Ramikan-BR/tinyllama_PY-CODER-4bit-lora_4k-v7 Ramikan-BR/tinyllama_PY-CODER-4bit-lora_4k-v7
Required VRAM	0.4 GB
Updated	2026-07-29
Maintainer	Ramikan-BR
Model Files	0.4 GB 0.7 GB
Supported Languages	en
GGUF Quantization	Yes
Quantization Type	gguf\|4bit
Generates Code	Yes
Model Architecture	AutoModelForCausalLM
License	apache-2.0
Model Max Length	4096
Is Biased	none
Tokenizer Class	LlamaTokenizer
Padding Token	<unk>
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	gate_proj\|down_proj\|up_proj\|k_proj\|q_proj\|o_proj\|v_proj
LoRA Alpha	256
LoRA Dropout	0
R Param	128

Rank the Tinyllama PY CODER 4bit Lora 4K V8 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 V8

Model	Likes	Downloads	VRAM
...llama PY CODER 4bit Lora 4k V9	0	18	0 GB

Best Alternatives to Tinyllama PY CODER 4bit Lora 4K V8

Best Alternatives	Context / RAM	Downloads	Likes
...llama PY CODER 4bit Lora 4k V9	0K / 0.4 GB	18	0
Llama2 CodeGen PEFT QLoRA	0K / 13.5 GB	84	5

Note: green Score (e.g. "73.2") means that the model is better than Ramikan-BR/tinyllama_PY-CODER-4bit-lora_4k-v8.