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Phind CodeLlama 34B v2

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Phind CodeLlama 34B v2 is an open-weights language model from Phind, released August 2023. Its published size is 34B parameters and a 16k-token context window. Licence: llama2.

Catalogue identity notes

Maker repo Phind/Phind-CodeLlama-34B-v2 @949f61e: licence tag llama2. Parameters: nominal size from the model name (no safetensors metadata in the repo) (dense, so active = total). Released 2023-08: month the maker's Hugging Face repo was created (createdAt 2023-08-28). Instruction fine-tune of Code Llama 34B (v2); not v1 or the -Python variant. Ollama tags phind-codellama:34b-v2-q4_K_M, phind-codellama:34b-v2-q8_0 (sizes as listed on the tags page, observed 2026-09-29).

46.6±0.7#407 of 719 LLMsfrom 6 benchmark results across 3 sources

$ how it compares

The LLMs ranked either side of Phind CodeLlama 34B v2, out of 828 with a score.

#modelscorewhereRAMreleased
482Magicoder-S-CL-7B UIUC (ise-uiuc)47±1local~5.8GB2023-12
483Aya Expanse 8B Cohere47±2local~6.2GB2024-10
484Phind CodeLlama 34B v2 Phind47±1local~23GB2023-08
485CogVLM2 Llama3 Chinese Chat 19B Zhipu AI (THUDM)47±7comparison only2024-05
486InternLM2.5 7B Chat 1M Shanghai AI Laboratory (InternLM)47±4local~6.4GB2024-06

see all LLMs · find the ones that fit your machine

$ what it's good at

coding▁▁▁▃▆██▇▆▄▃▁ #299 of 458 · 86% of its score · 4 tests

Beats Solar Open 100B (102B-A12B), GLM-4.7-Flash, Qwen3-VL 30B-A3B Instruct here, though they rank higher overall. Trails DeepSeek LLM 67B Chat, WizardLM 70B V1.0, Yi 1.5 9B Chat here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
HumanEval +171.3% ✎▁·▁▁▃▄▆▅█▃▁▁ beats 61/99easy · avg 79%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 54%sharp · at its level · several runs
HumanEval+67.1% ✎▁·▁▁▁▃▄▄▄█▃▁ beats 64/99easy · avg 74%▇▇▇▇▇▇▇▇▇▇▇ 28%sharp · at its level
CRUXEval-I47.2% ✎▁··▁▅▂█▇▁··▁ beats 19/22medium · avg 54%▇ 1.8%at its level
CRUXEval-O39.7% ✎▁·▂▃▁█▁▂···▁ beats 8/22medium · avg 53%▇ 1.7%blunt · at its level
other tests1 not in a task · 14% of its score
testresultvs the others in its rundifficultycountswhy
EvoEval (overall)45.6% ✎▂▂▃▄▄█▁▄▅▃▁▃ beats 30/42medium · avg 55%▇▇▇▇▇▇ 14%at its level

$ where to run it

llama.cpp · recommended

version

  1. Install llama.cpp · Homebrew and winget installs name the commands llama-cli and llama-server instead.
    macOS / Linux
    curl -LsSf https://llama.app/install.sh | sh
    Windows
    irm https://llama.app/install.ps1 | iex

    other ways to install ↗

  2. Run Phind CodeLlama 34B v2 · needs ~20.2 GB · from huggingface.co/TheBloke
    chat in the terminal
    llama cli -hf TheBloke/Phind-CodeLlama-34B-v2-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf TheBloke/Phind-CodeLlama-34B-v2-GGUF:Q4_K_M

LM Studio

version

  1. Install LM Studio · Open the app once before using the lms command.

    download it (macOS / Windows / Linux) ↗

  2. Run Phind CodeLlama 34B v2 · needs ~20.2 GB · from huggingface.co/TheBloke

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/TheBloke/Phind-CodeLlama-34B-v2-GGUF@Q4_K_M -y
  4. Then run lms ls to see its name, and lms chat followed by that name

Ollama

version

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run Phind CodeLlama 34B v2 · needs ~20 GB · from ollama.com
    chat in the terminal
    ollama run phind-codellama:34b-v2-q4_K_M

Official weights and documentation: huggingface.co/Phind ↗

$ how the score was worked out · EvalPlus / EvalPerf published… 63%, Top Leaderboard Ranking = Top… 33%, CRUXEval published evaluation… 4%

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
EvalPlus / EvalPerf published evaluation runs2▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 63%Apache-2.0
Top Leaderboard Ranking = Top Coding Proficiency, Always? EvoEval: Evolving Coding Benchmarks via LLM — authors' own runs2▇▇▇▇▇▇▇▇▇▇ 33%CC BY 4.0
CRUXEval published evaluation results (authors' generations and scores)2▇ 4%MIT
prior · pull toward the middle▇ 0%
weighted average= 46.6

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