What are the hardware requirements for Phind CodeLlama 34B V2 AWQ?

Phind CodeLlama 34B V2 AWQ requires approximately 18.3 GB of VRAM and supports 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 AWQ and how large is it?

Phind CodeLlama 34B V2 AWQ is developed by TheBloke, 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 AWQ?

Phind CodeLlama 34B V2 AWQ 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 AWQ by TheBloke — VRAM 18.3GB, 16K context

Name: Phind CodeLlama 34B V2 AWQ
Author: TheBloke

Phind CodeLlama 34B V2 AWQ is an open-source language model by TheBloke. Features: 34b LLM, VRAM: 18.3GB, Context: 16K, License: llama2, Quantized, Code Generating, LLM Explorer Score: 0.09.

4-bit Awq Base model:phind/phind-codella... Base model:quantized:phind/phi... Code llama Codegen Deploy:azure Llama Model-index Quantized Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/TheBloke/Phind-CodeLlama-34B-v2-AWQ

Phind CodeLlama 34B V2 AWQ Parameters and Internals

Model Type

text-generation

Use Cases

Areas:

research, commercial applications

Primary Use Cases:

programming assistance

Considerations:

This model has undergone very limited testing and additional safety testing should be performed.

Supported Languages

Python (proficient), C/C++ (proficient), TypeScript (proficient), Java (proficient)

Training Details

Data Volume:

1.5B tokens

Context Length:

4096

Training Time:

15 hours

Hardware Used:

32x A100-80GB

Safety Evaluation

Ethical Considerations:

This model has undergone very limited testing. Additional safety testing should be performed before any real-world deployments.

Input Output

Input Format:

Alpaca/Vicuna instruction format

Accepted Modalities:

text

Output Format:

text

Performance Tips:

Use prompt templates to ensure effective communication with the model.

LLM Name	Phind CodeLlama 34B V2 AWQ
Repository 🤗	https://huggingface.co/TheBloke/Phind-CodeLlama-34B-v2-AWQ
Model Creator	Phind
Base Model(s)	Phind CodeLlama 34B V2 Phind/Phind-CodeLlama-34B-v2
Model Size	34b
Required VRAM	18.3 GB
Updated	2026-07-27
Maintainer	TheBloke
Model Type	llama
Model Files	9.9 GB: 1-of-2 8.4 GB: 2-of-2
AWQ Quantization	Yes
Quantization Type	awq
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 AWQ 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 AWQ

Best Alternatives	Context / RAM	Downloads	Likes
...echless Codellama 34B V2.0 AWQ	16K / 18.3 GB	6	6
...enbuddy Coder 34B V11 Bf16 AWQ	16K / 18.5 GB	4	1
CodeLlama 34B Instruct AWQ	16K / 18.3 GB	57	1
...izardCoder Python 34B V1.0 AWQ	16K / 18.3 GB	11	6
CodeLlama 34B AWQ	16K / 18.3 GB	24	2
MAmmoTH Coder 34B AWQ	16K / 18.3 GB	4	1
CodeFuse CodeLlama 34B AWQ	16K / 18.3 GB	11	2
CodeLlama 34B Python AWQ	16K / 18.3 GB	8	2
...nd CodeLlama 34B Python V1 AWQ	16K / 18.3 GB	7	2
Phind CodeLlama 34B V1 AWQ	16K / 18.3 GB	6	1

Note: green Score (e.g. "73.2") means that the model is better than TheBloke/Phind-CodeLlama-34B-v2-AWQ.