What are the hardware requirements for Phind Codellama 34B V2 Megacode EXL2?

Phind Codellama 34B V2 Megacode EXL2 requires a context window of 16K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Phind Codellama 34B V2 Megacode EXL2 and how large is it?

Phind Codellama 34B V2 Megacode EXL2 is developed by latimar, a model with 34b 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 Phind Codellama 34B V2 Megacode EXL2?

Phind Codellama 34B V2 Megacode EXL2 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Phind Codellama 34B V2 Megacode EXL2 by latimar — 16K context

Name: Phind Codellama 34B V2 Megacode EXL2
Author: latimar

Phind Codellama 34B V2 Megacode EXL2 is an open-source language model by latimar. Features: 34b LLM, Context: 16K, License: llama2, Quantized, Code Generating, LLM Explorer Score: 0.09.

Codegen Exl2 Llama Quantized Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/latimar/Phind-Codellama-34B-v2-megacode-exl2

Phind Codellama 34B V2 Megacode EXL2 Parameters and Internals

Model Type

llama

LLM Name	Phind Codellama 34B V2 Megacode EXL2
Repository 🤗	https://huggingface.co/latimar/Phind-Codellama-34B-v2-megacode-exl2
Model Name	Phind-Codellama-34B-v2
Model Creator	https://huggingface.co/Phind
Model Size	34b
Updated	2026-07-16
Maintainer	latimar
Model Type	llama
Quantization Type	exl2
Generates Code	Yes
Model Architecture	LlamaForCausalLM
License	llama2
Context Length	16384
Model Max Length	16384
Transformers Version	4.33.0.dev0
Tokenizer Class	LlamaTokenizer
Beginning of Sentence Token	<s>
End of Sentence Token	</s>
Unk Token	<unk>
Vocabulary Size	32000
Torch Data Type	bfloat16

Rank the Phind Codellama 34B V2 Megacode EXL2 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 Phind Codellama 34B V2 Megacode EXL2

Best Alternatives	Context / RAM	Downloads	Likes
CodeLlama 34B Instruct Fp16	16K / 67.5 GB	131	7
CodeLlama 34B Instruct Hf 4bit	16K / 19.4 GB	63	2
Phind Codellama 34B V2 EXL2	16K / GB	8	16
Codellama Extraction	16K / 67.6 GB	6	0
CodeLlama 34B Python Fp16	16K / 67.5 GB	132	14
CodeLlama 34B Fp16	16K / 67.5 GB	25	4
Codellama 34B Bnb 4bit	16K / 18.2 GB	38	4
XwinCoder 34B 4.0bpw H6 EXL2	16K / 17.4 GB	3	1
...gpt 32K Codellama 34B Instruct	32K / 67.5 GB	56	2
CodeLlama 34B Instruct Hf	16K / 67.5 GB	22872	304

Note: green Score (e.g. "73.2") means that the model is better than latimar/Phind-Codellama-34B-v2-megacode-exl2.