What are the hardware requirements for Tinyllama Coder Py 4bit V10?

Tinyllama Coder Py 4bit V10 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 Coder Py 4bit V10 and how large is it?

Tinyllama Coder Py 4bit V10 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.

How does Tinyllama Coder Py 4bit V10 perform on standard benchmarks?

Tinyllama Coder Py 4bit V10 has the following published scores: MMLU 26.42. Compare against reference models on this page or on the LLM Explorer leaderboards.

Tinyllama Coder Py 4bit V10 by Ramikan-BR — VRAM 0.7GB, 4K context

Name: Tinyllama Coder Py 4bit V10
Author: Ramikan-BR

Tinyllama Coder Py 4bit V10 is an open-source language model by Ramikan-BR. Features: 1.1b LLM, VRAM: 0.7GB, Context: 4K, License: apache-2.0, Quantized, Instruction-Based, Code Generating, LLM Explorer Score: 0.17, Arc: 34.4, HellaSwag: 56.3, MMLU: 26.4, GSM8K: 0.5.

4bit Base model:adapter:unsloth/tin... Base model:unsloth/tinyllama-c... Code Codegen Conversational Dataset:ramikan-br/data-oss in... En Endpoints compatible Gguf Instruct Llama Lora Peft Quantized Region:us Safetensors Sft Trl Unsloth

Model Card on HF 🤗: https://huggingface.co/Ramikan-BR/tinyllama-coder-py-4bit-v10

Tinyllama Coder Py 4bit V10 Parameters and Internals

Model Type

text-generation, Python Coder

Additional Notes

The model was refined using Unsloath. The dataset ise-uiuc/Magicoder-OSS-Instruct-75K was adjusted, leaving only data on Python and divided into 10 parts, each refinement occurred for 2 epochs, using Adafactor optimizer or AdamW_8bit (Adafactor seems to deliver less loss).

Supported Languages

en (NLP)

Training Details

Data Sources:

Ramikan-BR/data-oss_instruct-decontaminated_python.jsonl

Methodology:

The model was refined using Unsloath.

LLM Name	Tinyllama Coder Py 4bit V10
Repository 🤗	https://huggingface.co/Ramikan-BR/tinyllama-coder-py-4bit-v10
Base Model(s)	Tinyllama Chat Bnb 4bit unsloth/tinyllama-chat-bnb-4bit
Model Size	1.1b
Required VRAM	0.7 GB
Updated	2026-06-09
Maintainer	Ramikan-BR
Model Type	llama
Instruction-Based	Yes
Model Files	2.2 GB 2.2 GB 0.7 GB 1.2 GB
Supported Languages	en
GGUF Quantization	Yes
Quantization Type	gguf\|4bit
Generates Code	Yes
Model Architecture	LlamaForCausalLM
License	apache-2.0
Context Length	4096
Model Max Length	4096
Transformers Version	4.40.2
Tokenizer Class	LlamaTokenizer
Padding Token	<unk>
Vocabulary Size	32000
Torch Data Type	float16

Rank the Tinyllama Coder Py 4bit V10 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