[whatmodel.sh](../../README.md) / Phind CodeLlama 34B v2 # Phind CodeLlama 34B v2 Phind · released 2023-08 · 34B params · llama2 **score 48.1** ±0.7 · #407 of 732 · from 6 benchmark results across 3 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at _{● marks Phind CodeLlama 34B v2 among every model on that task or test, worst to best.}

task    vs the others         rank  counts  tests
coding  ▁▁▁▃▅●█▇▅▄▃▁   #309 of 469     85%      4

coding · #309 of 469 · 85% of its score

Beats [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Solar Open 100B (102B-A12B)](solar-open-100b.md), [Qwen3-VL 30B-A3B Instruct](qwen3-vl-30b-a3b-instruct.md) here, though they rank higher overall. Trails [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [WizardLM 70B V1.0](wizardlm-70b-v1-0.md), [Yi 1.5 9B Chat](yi-1-5-9b-chat.md) here, though they rank lower overall.

test        result  vs the others in its run  difficulty        counts  why
HumanEval    71.3%  ▁·▁▁▃▄▆●█▃▁▁ beats 61/99  easy · avg 76%       53%  sharp · at its level · several runs
HumanEval+   67.1%  ▁·▁▁▁▃▄▄▄●▃▁ beats 64/99  easy · avg 70%       28%  sharp · at its level
CRUXEval-I   47.2%  ▁··▁▅▂█●▁··▁ beats 19/22  medium · avg 52%    1.9%  at its level
CRUXEval-O   39.7%  ▁·▂▃▁●▁▂···▁ beats 8/22   medium · avg 51%    1.8%  blunt · at its level

other tests (1)

test               result  vs the others in its run  difficulty        counts  why
EvoEval (overall)   45.6%  ▂▂▃▄▄█▁▄●▃▁▃ beats 30/42  medium · avg 52%     15%  at its level

## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's 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 is added up by who ran the tests. [Method](../method.md)

source                                    results  share of the score
EvalPlus / EvalPerf published evaluatio…        2   61% ▇▇▇▇▇▇▇
Top Leaderboard Ranking = Top Coding Pr…        2   35% ▇▇▇▇
CRUXEval published evaluation results           2    4% ▇
prior: pull toward the middle                        0% ▇
weighted average                                   = 48.1

_{Licences: EvalPlus / EvalPerf published evaluation runs ([Apache-2.0](../../licenses/evalplus-published-runs-Apache-2.0.txt)) · Top Leaderboard Ranking = Top Coding Proficiency ([CC BY 4.0](../../licenses/paper-2403.19114v1-CC-BY-4.0.txt)) · CRUXEval published evaluation results ([MIT](../../licenses/cruxeval-published-results-MIT.txt))} ## How to run it ### llama.cpp (recommended) 1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md) ```sh # macOS / Linux curl -LsSf https://llama.app/install.sh | sh # Windows irm https://llama.app/install.ps1 | iex ``` 2. Run Phind CodeLlama 34B v2 (Q4_K_M, needs ~20.2 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-v2-GGUF/tree/da37c48be3b0c6cd487fe05259521dc2824f5a5f)) ```sh 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) ```sh llama serve -hf TheBloke/Phind-CodeLlama-34B-v2-GGUF:Q4_K_M ``` Other versions: Q8_0 (~35.9 GB) `llama cli -hf TheBloke/Phind-CodeLlama-34B-v2-GGUF:Q8_0` ### LM Studio 1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download) 2. Run Phind CodeLlama 34B v2 (Q4_K_M, needs ~20.2 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-v2-GGUF/tree/da37c48be3b0c6cd487fe05259521dc2824f5a5f)) ```sh lms get https://huggingface.co/TheBloke/Phind-CodeLlama-34B-v2-GGUF@Q4_K_M -y ``` 3. Then run lms ls to see its name, and lms chat followed by that name Other versions: Q8_0 (~35.9 GB) `lms get https://huggingface.co/TheBloke/Phind-CodeLlama-34B-v2-GGUF@Q8_0 -y` ### Ollama 1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download) ```sh # Linux curl -fsSL https://ollama.com/install.sh | sh ``` 2. Run Phind CodeLlama 34B v2 (Q4_K_M, needs ~20 GB, from [ollama.com](https://ollama.com/library/phind-codellama/tags)) ```sh ollama run phind-codellama:34b-v2-q4_K_M ``` Other versions: Q8_0 (~36 GB) `ollama run phind-codellama:34b-v2-q8_0` Model repository and documentation: [huggingface.co/Phind](https://huggingface.co/Phind/Phind-CodeLlama-34B-v2) [interactive version](https://whatmodel.sh/model/phind-codellama-34b-v2) · [JSON](https://whatmodel.sh/api?format=json&all=1)