whatmodel.sh / speechless-starcoder2-7b
speechless-starcoder2-7b
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speechless-starcoder2-7b is an open-weights language model from uukuguy, released March 2024. Its published size is 7.17B parameters. Licence: apache-2.0.
Catalogue identity notes
Maker repo uukuguy/speechless-starcoder2-7b @c97317d: licence tag apache-2.0. 7,173,923,840 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-03: month the maker's Hugging Face repo was created (createdAt 2024-03-10).
$ how it compares
The LLMs ranked either side of speechless-starcoder2-7b, out of 828 with a score, then more from uukuguy.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 594 |  Falcon2 11B (base) | 42±3 | local | ||
| 595 |  Granite 34B Code Base (8K) | 42±1 | local | ||
| 596 | speechless-starcoder2-7b | 42±1 | local | ||
| 597 |  Command R (03-2024) | 42±1 | local | ||
| 598 |  Ministral 3B (2410) | 42±1 | comparison only | ||
| more from uukuguy | |||||
| 435 | speechless-codellama-34b-v2.0 | 49±1 | local | ||
| 480 | speechless-coder-ds-6.7b | 47±1 | local | ||
| 523 | speechless-starcoder2-15b | 45±1 | local | ||
$ what it's good at
coding▁▁▁▃▆██▇▆▄▃▁ #353 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 Phi-3-mini-128k-instruct (Apr 2024), Code Tulu 2 13B, DeepSeek LLM 67B Chat here, though they rank lower overall.
$ where to run it
llama.cpp · recommended
version
- 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 | shWindowsirm https://llama.app/install.ps1 | iex - Run speechless-starcoder2-7b · needs ~4.4 GB · from huggingface.co/bartowskichat in the terminal
llama cli -hf bartowski/speechless-starcoder2-7b-GGUF:Q4_K_M - Or start a local server and open the address it prints (http://localhost:9931)
llama serve -hf bartowski/speechless-starcoder2-7b-GGUF:Q4_K_M
LM Studio
version
- Install LM Studio · Open the app once before using the lms command.
- Run speechless-starcoder2-7b · needs ~4.4 GB · from huggingface.co/bartowski
- Or download it in the terminal
lms get https://huggingface.co/bartowski/speechless-starcoder2-7b-GGUF@Q4_K_M -y - Then run lms ls to see its name, and lms chat followed by that name
Ollama
version
- Install Ollama
download it (macOS / Windows) ↗
Linuxcurl -fsSL https://ollama.com/install.sh | sh - Run speechless-starcoder2-7b · needs ~4.4 GB · from huggingface.co/bartowskichat in the terminal
ollama run hf.co/bartowski/speechless-starcoder2-7b-GGUF:Q4_K_M
Other ways to run it
- Run it as published · needs ~14.35 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-starcoder2-7b', 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
| source | results | share of the score |
|---|---|---|
 EvalPlus / EvalPerf published evaluation runs | 3 | 100% |
| prior · pull toward the middle | 0% | |
| weighted average | = 41.7 |
$ improve this page
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data updated · sources and licences · json