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chore: wip #640

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codecov bot commented May 1, 2024

Codecov Report

Attention: Patch coverage is 0.82645% with 120 lines in your changes are missing coverage. Please review.

Project coverage is 69.19%. Comparing base (2d0cfe9) to head (bf786db).
Report is 1 commits behind head on main.

❗ Current head bf786db differs from pull request most recent head 528c69b. Consider uploading reports for the commit 528c69b to get more accurate results

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@@            Coverage Diff             @@
##             main     #640      +/-   ##
==========================================
- Coverage   69.85%   69.19%   -0.67%     
==========================================
  Files          98       98              
  Lines       13394    13515     +121     
==========================================
- Hits         9357     9352       -5     
- Misses       4037     4163     +126     
Files Coverage Δ
homestar-wasm/src/wasmtime/world.rs 87.94% <100.00%> (+0.05%) ⬆️
homestar-functions/subtract/src/bindings.rs 0.00% <0.00%> (ø)
homestar-wasm/src/wasmtime/host/helpers.rs 9.86% <0.00%> (-31.80%) ⬇️

... and 2 files with indirect coverage changes

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curl localhost:3000/run --json @map_reduce.json
```

To more applicability encode the [MapReduce][map-reduce] example from the [Crowdforge][crowdforge] paper, I implemented a `prompt_chain` Wasm/WASI function registered on the Host that takes in a system prompt (e.g. "You are journalist writing about cities."), an input (e.g. an ongoing article), a map step prompt with a `{{text}}` placeholder that is filled in, a reduce step, which folds over (combines) the generated text(s) from the map step, and then the optional LLaMA model stored as a [`gguf`][gguf]. If the optional model path is not provided, the Host will fall back to the default `Meta-Llama-3-8B-Instruct.Q4_0.gguf` model.
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To more applicability encode the [MapReduce][map-reduce] example from the [Crowdforge][crowdforge] paper, I implemented a `prompt_chain` Wasm/WASI function registered on the Host that takes in a system prompt (e.g. "You are journalist writing about cities."), an input (e.g. an ongoing article), a map step prompt with a `{{text}}` placeholder that is filled in, a reduce step, which folds over (combines) the generated text(s) from the map step, and then the optional LLaMA model stored as a [`gguf`][gguf]. If the optional model path is not provided, the Host will fall back to the default `Meta-Llama-3-8B-Instruct.Q4_0.gguf` model.
To more applicably encode the [MapReduce][map-reduce] example from the [Crowdforge][crowdforge] paper, I implemented a `prompt_chain` Wasm/WASI function registered on the Host that takes in a system prompt (e.g. "You are journalist writing about cities."), an input (e.g. an ongoing article), a map step prompt with a `{{text}}` placeholder that is filled in, a reduce step, which folds over (combines) the generated text(s) from the map step, and then the optional LLaMA model stored as a [`gguf`][gguf]. If the optional model path is not provided, the Host will fall back to the default `Meta-Llama-3-8B-Instruct.Q4_0.gguf` model.

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