whatmodel.sh / speechless-coding-7b-16k-tora

speechless-coding-7b-16k-tora

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speechless-coding-7b-16k-tora is an open-weights language model from uukuguy, released October 2023. Licence: llama2.

Catalogue identity notes

Maker repo uukuguy/speechless-coding-7b-16k-tora @c382c82: licence tag llama2. Released 2023-10: month the maker's Hugging Face repo was created (createdAt 2023-10-31).

41.0±0.8#515 of 719 LLMsfrom 3 benchmark results across 1 source

$ how it compares

The LLMs ranked either side of speechless-coding-7b-16k-tora, out of 828 with a score, then more from uukuguy.

#modelscorewhereRAMreleased
605Qwen1.5 14B Chat Alibaba41±1local~11GB2024-02
606Code Llama 34B Python Meta41±1local~23GB2023-08
607speechless-coding-7b-16k-tora uukuguy41±1local~16GB2023-10
608DeepSeek-Coder 33B Base DeepSeek41±1local~22GB2023-10
609InternLM2 Chat 20B Shanghai AI Laboratory (InternLM)41±1local~14GB2024-01
more from uukuguy
435speechless-codellama-34b-v2.0 uukuguy49±1local~23GB2023-10
480speechless-coder-ds-6.7b uukuguy47±1local~5.8GB2023-12
523speechless-starcoder2-15b uukuguy45±1local~12GB2024-03

see all LLMs · find the ones that fit your machine

$ what it's good at

coding▁▁▁▃▆██▇▆▄▃▁ #358 of 458 · 100% of its score · 3 tests

Beats LFM2.5 8B-A1B, Qwen3-VL 2B Thinking, DeepSeek R1 Distill Qwen 7B here, though they rank higher overall. Trails Qwen2.5 1.5B Instruct, Phi-3-mini-128k-instruct (Apr 2024), Code Tulu 2 13B here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
HumanEval54.9% ✎▁·▁▁▃▄▆▅█▃▁▁ beats 40/99easy · avg 79%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 54%sharp · at its level
HumanEval+50.6% ✎▁·▁▁▁▃▄▄▄█▃▁ beats 42/99easy · avg 74%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 39%sharp · at its level
MBPP+50.6% ✎▁···▁▁▂▅▇█▆▂ beats 20/75easy · avg 68%▇▇▇ 7.0%at its level

$ where to run it

Other ways to run it

  1. Run it as published · needs ~13.48 GB · from huggingface.co/uukuguy (official)
    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'])"

Official weights and documentation: huggingface.co/uukuguy ↗

$ how the score was worked out · EvalPlus / EvalPerf published… 100%

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 runs3▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 100%Apache-2.0
prior · pull toward the middle▇ 0%
weighted average= 41.0

$ improve this page

Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json