[whatmodel.sh](../../README.md) / MegaBeam-Mistral-7B-512k
# MegaBeam-Mistral-7B-512k
AWS (aws-prototyping) · released 2024-07 · 7.24B params · apache-2.0
**score 49.3** ±4.1 · #386 of 732 · from 3 benchmark results across 2 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks MegaBeam-Mistral-7B-512k among every model on that task or test, worst to best.
other tests (3)
test result vs the others in its run difficulty counts why
Open PT-LLM Leaderboard average (… 62.6% ▃▁▁▁▁▁▁▁●█▅▁ beats 96/276 medium · avg 72% 41% at its level · noisy
RULER 64k 83.7% ▁▁·▁·▂▃●█▂·▁ beats 11/22 easy · avg 82% 38% typical
RULER 128k 83.7% ▄··▂▄▂▆██▄●▂ beats 20/21 easy · avg 67% 13% at its level · noisy
source results share of the score
NVIDIA RULER repository README results … 2 51% ▇▇▇▇▇▇
Open Portuguese LLM Leaderboard 1 41% ▇▇▇▇▇
prior: pull toward the middle 8% ▇
weighted average = 49.3
Licences: NVIDIA RULER repository README results table ([Apache-2.0](https://github.com/NVIDIA/RULER/blob/c3f5e3b4f87f97e048793bb510a3a6b19a46bf3a/LICENSE)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.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 MegaBeam-Mistral-7B-512k (Q4_K_M, needs ~4.4 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/MegaBeam-Mistral-7B-512k-GGUF/tree/f6a596eaf9072a24f79a9b9a175d2ce103ca665b))
```sh
llama cli -hf bartowski/MegaBeam-Mistral-7B-512k-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf bartowski/MegaBeam-Mistral-7B-512k-GGUF:Q4_K_M
```
Other versions: Q8_0 (~7.7 GB) `llama cli -hf bartowski/MegaBeam-Mistral-7B-512k-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 MegaBeam-Mistral-7B-512k (Q4_K_M, needs ~4.4 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/MegaBeam-Mistral-7B-512k-GGUF/tree/f6a596eaf9072a24f79a9b9a175d2ce103ca665b))
```sh
lms get https://huggingface.co/bartowski/MegaBeam-Mistral-7B-512k-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 (~7.7 GB) `lms get https://huggingface.co/bartowski/MegaBeam-Mistral-7B-512k-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 MegaBeam-Mistral-7B-512k (Q4_K_M, needs ~4.4 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/MegaBeam-Mistral-7B-512k-GGUF/tree/f6a596eaf9072a24f79a9b9a175d2ce103ca665b))
```sh
ollama run hf.co/bartowski/MegaBeam-Mistral-7B-512k-GGUF:Q4_K_M
```
Other versions: Q8_0 (~7.7 GB) `ollama run hf.co/bartowski/MegaBeam-Mistral-7B-512k-GGUF:Q8_0`
### Other ways to run it
1. Run it as published (BF16, needs ~14.48 GB, from [huggingface.co/aws-prototyping](https://huggingface.co/aws-prototyping/MegaBeam-Mistral-7B-512k/tree/7a9bfb11b5d5e56eae0e4b52290d705cdd55f7a6))
```sh
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='aws-prototyping/MegaBeam-Mistral-7B-512k', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
```
Model repository and documentation: [huggingface.co/aws-prototyping](https://huggingface.co/aws-prototyping/MegaBeam-Mistral-7B-512k)
[interactive version](https://whatmodel.sh/model/megabeam-mistral-7b-512k) · [JSON](https://whatmodel.sh/api?format=json&all=1)