What are the hardware requirements for Tempemotacilla Vipercoderv1.7vsm6 0308?

Tempemotacilla Vipercoderv1.7vsm6 0308 requires approximately 29.7 GB of VRAM and supports a context window of 986K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Tempemotacilla Vipercoderv1.7vsm6 0308 and how large is it?

Tempemotacilla Vipercoderv1.7vsm6 0308 is developed by Cran-May, a model with 14.8b 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 Tempemotacilla Vipercoderv1.7vsm6 0308?

Tempemotacilla Vipercoderv1.7vsm6 0308 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Tempemotacilla Vipercoderv1.7vsm6 0308 by Cran-May — VRAM 29.7GB, 986K context

Name: Tempemotacilla Vipercoderv1.7vsm6 0308
Author: Cran-May

Tempemotacilla Vipercoderv1.7vsm6 0308 is an open-source language model by Cran-May. Features: 14.8b LLM, VRAM: 29.7GB, Context: 986K, License: apache-2.0, Code Generating, LLM Explorer Score: 0.17.

Base model:finetune:prithivmlm... Base model:prithivmlmods/viper... Codegen Coder Conversational En Endpoints compatible Model-index Qwen Qwen2 Region:us Safetensors Sharded Streamlinedmemory Tensorflow Viper

Model Card on HF 🤗: https://huggingface.co/Cran-May/tempemotacilla-vipercoderv1.7vsm6-0308

Tempemotacilla Vipercoderv1.7vsm6 0308 Parameters and Internals

LLM Name	Tempemotacilla Vipercoderv1.7vsm6 0308
Repository 🤗	https://huggingface.co/Cran-May/tempemotacilla-vipercoderv1.7vsm6-0308
Base Model(s)	prithivMLmods/Viper-Coder-v1.6-r999 prithivMLmods/Viper-Coder-v1.6-r999
Model Size	14.8b
Required VRAM	29.7 GB
Updated	2026-08-25
Maintainer	Cran-May
Model Type	qwen2
Model Files	4.9 GB: 1-of-6 5.0 GB: 2-of-6 5.0 GB: 3-of-6 5.0 GB: 4-of-6 5.0 GB: 5-of-6 4.8 GB: 6-of-6
Supported Languages	en
Generates Code	Yes
Model Architecture	Qwen2ForCausalLM
License	apache-2.0
Context Length	1010000
Model Max Length	1010000
Transformers Version	4.48.2
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151665
Torch Data Type	bfloat16
Errors	replace

Rank the Tempemotacilla Vipercoderv1.7vsm6 0308 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 Tempemotacilla Vipercoderv1.7vsm6 0308

Best Alternatives	Context / RAM	Downloads	Likes
Viper Coder V1.7 Vsm6	986K / 29.7 GB	9	6
Viper Coder Hybrid V1.3	128K / 29.7 GB	16	9
Raptor X5 UIGEN	117K / 29.7 GB	13	3
Raptor X4	117K / 29.7 GB	7	2
Raptor X2	117K / 29.7 GB	10	0
Viper Coder V1.6 R999	32K / 29.7 GB	9	6
Viper Coder V1.1	32K / 29.7 GB	14	8
Viper Coder Hybrid V1.2	32K / 29.7 GB	8	2
Viper OneCoder UIGEN	32K / 29.7 GB	19	3
Viper Coder V0.1	32K / 29.7 GB	12	1

Note: green Score (e.g. "73.2") means that the model is better than Cran-May/tempemotacilla-vipercoderv1.7vsm6-0308.