[whatmodel.sh](../../README.md) / speechless-coding-7b-16k-tora # speechless-coding-7b-16k-tora uukuguy · released 2023-10 · llama2 **score 42.6** ±0.8 · #525 of 732 · from 3 benchmark results across 1 source Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at _{● marks speechless-coding-7b-16k-tora among every model on that task or test, worst to best.}

task    vs the others         rank  counts  tests
coding  ▁▁▁▃●▇█▇▅▄▃▁   #367 of 469    100%      3

coding · #367 of 469 · 100% of its score

Beats [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md), [Qwen3-VL 2B Thinking](qwen3-vl-2b-thinking.md), [DeepSeek R1 Distill Qwen 7B](deepseek-r1-distill-qwen-7b.md) here, though they rank higher overall. Trails [Qwen2.5 1.5B Instruct](qwen2-5-1-5b.md), [Code Tulu 2 13B](code-tulu-2-13b.md), [Phi-3-mini-128k-instruct (Apr 2024)](phi-3-mini-128k-instruct.md) here, though they rank lower overall.

test        result  vs the others in its run  difficulty      counts  why
HumanEval    54.9%  ▁·▁▁▃▄●▅█▃▁▁ beats 40/99  easy · avg 76%     51%  sharp · at its level
HumanEval+   50.6%  ▁·▁▁▁▃▄●▄█▃▁ beats 42/99  easy · avg 70%     41%  sharp · at its level
MBPP+        50.6%  ▁···▁▁▂●▇█▆▂ beats 20/75  easy · avg 66%    7.8%  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…        3  100% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle                        0% ▇
weighted average                                   = 42.6

_{Licences: EvalPlus / EvalPerf published evaluation runs ([Apache-2.0](../../licenses/evalplus-published-runs-Apache-2.0.txt))} ## How to run it ### Other ways to run it 1. Run it as published (BF16, needs ~13.48 GB, from [huggingface.co/uukuguy](https://huggingface.co/uukuguy/speechless-coding-7b-16k-tora/tree/c382c82ebaac18314361fd13c2f54d113510ed40)) ```sh uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='uukuguy/speechless-coding-7b-16k-tora', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])" ``` Model repository and documentation: [huggingface.co/uukuguy](https://huggingface.co/uukuguy/speechless-coding-7b-16k-tora) [interactive version](https://whatmodel.sh/model/speechless-coding-7b-16k-tora) · [JSON](https://whatmodel.sh/api?format=json&all=1)