mirror of
https://github.com/aaronpo97/the-biergarten-app.git
synced 2026-06-01 01:54:00 +00:00
updates
This commit is contained in:
@@ -14,10 +14,10 @@
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#include <string>
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#include <string_view>
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#include "../services/prompting/prompt_directory.h"
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#include "data_generation/data_generator.h"
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#include "data_generation/prompt_formatting/prompt_formatter.h"
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#include "data_model/models.h"
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#include "../services/prompting/prompt_directory.h"
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struct llama_model;
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struct llama_context;
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@@ -129,6 +129,7 @@ class LlamaGenerator final : public DataGenerator {
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uint32_t sampling_top_k_ = kDefaultSamplingTopK;
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std::mt19937 rng_;
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uint32_t n_ctx_ = kDefaultContextSize;
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int n_gpu_layers_ = 0;
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std::unique_ptr<IPromptFormatter> prompt_formatter_;
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std::unique_ptr<IPromptDirectory> prompt_directory_;
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};
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@@ -3,7 +3,8 @@
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/**
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* @file data_model/models.h
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* @brief Core data models: locations, application configuration, and generation inputs.
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* @brief Core data models: locations, application configuration, and generation
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* inputs.
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*/
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#include <boost/program_options.hpp>
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@@ -94,6 +95,9 @@ struct GeneratorOptions {
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/// @brief Use mocked generator instead of actual LLM inference.
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bool use_mocked = false;
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/// @brief Number of layers to offload to GPU.
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int n_gpu_layers = 0;
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/// @brief Specific sampling parameters for this generator.
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/// If nullopt, the application should use global defaults.
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std::optional<SamplingOptions> sampling;
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@@ -26,15 +26,10 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
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RUN curl -L https://github.com/Kitware/CMake/releases/download/v3.31.0/cmake-3.31.0-linux-x86_64.sh -o cmake.sh && \
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sh cmake.sh --skip-license --prefix=/usr/local && rm cmake.sh
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# Copy and link backends
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# Copy backends to /usr/local/lib and register with ldconfig so the
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# runtime linker can resolve libllama.so, libggml.so, libggml-base.so etc.
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COPY --from=llama-bin /app/lib*.so* /usr/local/lib/
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RUN ldconfig && \
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find /usr/local/lib -name "libggml-cuda.so*" -exec ln -s {} /usr/local/lib/libggml-cuda.so \; 2>/dev/null || true && \
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find /usr/local/lib -name "libggml-cpu.so*" -exec ln -s {} /usr/local/lib/libggml-cpu.so \; 2>/dev/null || true
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# Set Environment for the loader
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ENV GGML_BACKEND_PATH="/usr/local/lib"
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ENV LD_LIBRARY_PATH="/usr/local/lib:$LD_LIBRARY_PATH"
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RUN ldconfig
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# Headers for C++ Build
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RUN git clone --depth 1 -b b9012 https://github.com/ggml-org/llama.cpp.git /tmp/llama-src && \
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@@ -42,6 +37,8 @@ RUN git clone --depth 1 -b b9012 https://github.com/ggml-org/llama.cpp.git /tmp/
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cp -r /tmp/llama-src/ggml/include/* /usr/local/include/ && \
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rm -rf /tmp/llama-src
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ENV LD_LIBRARY_PATH="/usr/local/lib:${LD_LIBRARY_PATH}"
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WORKDIR /workspace/app
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COPY . .
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@@ -49,6 +46,17 @@ COPY . .
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RUN cmake -S . -B build -G Ninja -DCMAKE_BUILD_TYPE=Release && \
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cmake --build build -j$(nproc)
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# Co-locate GGML backend plugins with the executable.
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# ggml_backend_load_all() searches the executable directory first when
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# GGML_BACKEND_DIR is not set. Copying the ggml-*.so plugin files here
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# ensures the loader finds them without any environment variable.
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# libllama.so, libggml.so, and libggml-base.so are NOT copied here —
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# those are proper shared libraries resolved via ldconfig/LD_LIBRARY_PATH.
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RUN cp /usr/local/lib/libggml-cuda.so /workspace/app/build/ 2>/dev/null || true && \
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cp /usr/local/lib/libggml-cpu*.so /workspace/app/build/ 2>/dev/null || true && \
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cp /usr/local/lib/libggml-blas*.so /workspace/app/build/ 2>/dev/null || true && \
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cp /usr/local/lib/libggml-rpc*.so /workspace/app/build/ 2>/dev/null || true
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# Setup Start Script
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COPY runpod/start.sh /usr/local/bin/biergarten-start
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RUN chmod +x /usr/local/bin/biergarten-start
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@@ -1,66 +1,8 @@
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# RunPod Pod Template for Biergarten Pipeline
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This folder contains a starter RunPod pod template for the C++ pipeline in the
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repository root.
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## What it does
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- Builds `biergarten-pipeline` inside the container.
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- Builds the binary on first pod start, then reuses a mode-specific build
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directory (`build-mocked/` or `build-live/`).
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- Runs from the repository root and lets the launcher switch into the active
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build directory after CMake has copied `locations.json` and `prompts/`.
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- Supports two runtime modes:
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- `BIERGARTEN_MODE=mocked` — fast deterministic generation, no model required.
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- `BIERGARTEN_MODE=live` — uses a mounted GGUF model and the prompt files.
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- Writes generated SQLite exports and logs to writable volumes.
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## Files
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- `Dockerfile` — GPU-ready build image for the application.
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- `start.sh` — runtime launcher that selects mocked or live mode.
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- `pod-template.yaml` — starter pod template you can adapt to the exact RunPod
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import/export schema.
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## Build the image
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```bash
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docker build -t biergarten-pipeline:latest -f runpod/Dockerfile .
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touch runpod/start.sh
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docker build \
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--progress=plain \
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-t biergarten-pipeline:latest \
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-f runpod/Dockerfile \
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. 2>&1 | tee build.log
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```
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## Run locally in mocked mode
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```bash
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docker run --rm \
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--gpus all \
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-e BIERGARTEN_MODE=mocked \
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-v "$PWD/output:/workspace/output" \
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-v "$PWD/logs:/workspace/logs" \
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biergarten-pipeline:latest
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```
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## Run locally in live mode
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Mount your GGUF model at `/workspace/models/google_gemma-4-E4B-it-Q6_K.gguf`
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and switch to `BIERGARTEN_MODE=live`.
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```bash
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docker run --rm \
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--gpus all \
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-e BIERGARTEN_MODE=live \
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-v "$PWD/models:/workspace/models" \
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-v "$PWD/output:/workspace/output" \
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-v "$PWD/logs:/workspace/logs" \
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biergarten-pipeline:latest
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```
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## Notes for RunPod
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- Use a GPU pod for live inference.
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- Mount persistent storage for `/workspace/models`, `/workspace/output`, and
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`/workspace/logs`.
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- If you only want deterministic seed generation, change the template's
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`BIERGARTEN_MODE` to `mocked`.
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- The launcher handles the build directory automatically; CMake still copies
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`locations.json` and `prompts/` into the active build tree before execution.
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@@ -1,24 +1,15 @@
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# Biergarten Pipeline — RunPod pod template
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#
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# This template is meant to be imported into RunPod or adapted to the exact
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# schema used by your account/export format. It intentionally keeps the runtime
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# contract simple:
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# - the container boots into /workspace/app/build
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# - prompts are available from build/prompts
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# - generated SQLite exports and logs go to writable volumes
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# - mocked mode works without a model file
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# - live mode can be enabled by setting BIERGARTEN_MODE=live and mounting a GGUF model
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name: biergarten-pipeline-live
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image: biergarten-pipeline:latest
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workingDir: /workspace/app
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entrypoint:
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imageName: biergarten-pipeline:latest
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category: NVIDIA
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containerDiskInGb: 50
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volumeInGb: 50
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volumeMountPath: /workspace
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dockerEntrypoint:
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- /usr/local/bin/biergarten-start
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resources:
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gpu: 1
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containerDiskInGb: 50
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volumeInGb: 50
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environment:
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dockerStartCmd: []
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isPublic: false
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isServerless: false
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env:
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BIERGARTEN_MODE: live
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BIERGARTEN_MODEL_PATH: /workspace/models/google_gemma-4-E4B-it-Q6_K.gguf
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BIERGARTEN_PROMPT_DIR: /workspace/app/build/prompts
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@@ -29,11 +20,3 @@ environment:
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BIERGARTEN_TOP_K: "64"
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BIERGARTEN_N_CTX: "8192"
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BIERGARTEN_SEED: "-1"
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volumes:
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- name: models
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mountPath: /workspace/models
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- name: output
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mountPath: /workspace/output
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- name: logs
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mountPath: /workspace/logs
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@@ -10,23 +10,19 @@ PROMPT_DIR="/workspace/app/build/prompts"
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echo "--- Starting Biergarten Pipeline Environment Check ---"
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# 1. Ensure Volume Mounts exist
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# 1. Ensure volume mount directories exist
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mkdir -p "$OUTPUT_DIR"
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mkdir -p "$(dirname "$LOG_PATH")"
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# 2. Check for Model
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# 2. Check for model file
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if [ ! -f "$MODEL_PATH" ]; then
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echo "ERROR: Model not found at $MODEL_PATH"
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echo "Current /workspace/models contents:"
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ls -lh /workspace/models
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ls -lh /workspace/models 2>/dev/null || echo "(directory does not exist)"
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exit 1
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fi
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# 3. Check for Backends (Diagnostic)
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echo "Loading backends from: $GGML_BACKEND_PATH"
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ls -l /usr/local/lib/libggml*
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# 4. Build the command arguments
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# 3. Build the command arguments
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ARGS=(
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"--model" "$MODEL_PATH"
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"--prompt-dir" "$PROMPT_DIR"
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@@ -34,7 +30,7 @@ ARGS=(
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"--log-path" "$LOG_PATH"
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)
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# Optional Hyperparameters
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# Optional hyperparameters
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[[ -n "$BIERGARTEN_TEMPERATURE" ]] && ARGS+=("--temperature" "$BIERGARTEN_TEMPERATURE")
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[[ -n "$BIERGARTEN_TOP_P" ]] && ARGS+=("--top-p" "$BIERGARTEN_TOP_P")
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[[ -n "$BIERGARTEN_TOP_K" ]] && ARGS+=("--top-k" "$BIERGARTEN_TOP_K")
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@@ -42,12 +38,12 @@ ARGS=(
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[[ -n "$BIERGARTEN_SEED" ]] && ARGS+=("--seed" "$BIERGARTEN_SEED")
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[[ -n "$BIERGARTEN_GL_LAYERS" ]] && ARGS+=("--n-gpu-layers" "$BIERGARTEN_GL_LAYERS")
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# Append extra custom args
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# Append any extra custom args
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if [[ -n "$BIERGARTEN_EXTRA_ARGS" ]]; then
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ARGS+=($BIERGARTEN_EXTRA_ARGS)
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fi
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echo "--- Executing: $EXECUTABLE ${ARGS[@]} ---"
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echo "--- Executing: $EXECUTABLE ${ARGS[*]} ---"
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# Execute the binary directly (replaces shell process)
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# Execute the binary directly, replacing the shell process
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exec "$EXECUTABLE" "${ARGS[@]}"
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@@ -50,6 +50,8 @@ std::optional<ApplicationOptions> ParseArguments(const int argc, char** argv) {
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opt("prompt-dir", prog_opts::value<std::string>()->default_value(""),
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"Directory containing named prompt files (e.g. BREWERY_GENERATION.md)."
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" Required when not using --mocked.");
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opt("n-gpu-layers", prog_opts::value<int>()->default_value(0),
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"Number of layers to offload to GPU");
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};
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add_sampling_options();
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@@ -85,6 +87,7 @@ std::optional<ApplicationOptions> ParseArguments(const int argc, char** argv) {
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const bool use_mocked = var_map["mocked"].as<bool>();
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const std::string model_path = var_map["model"].as<std::string>();
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const int n_gpu_layers = var_map["n-gpu-layers"].as<int>();
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// Enforce mutual exclusivity before any further configuration is applied.
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if (use_mocked && !model_path.empty()) {
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@@ -110,6 +113,7 @@ std::optional<ApplicationOptions> ParseArguments(const int argc, char** argv) {
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options.generator.use_mocked = use_mocked;
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options.generator.model_path = model_path;
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options.generator.n_gpu_layers = n_gpu_layers;
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// Only populate sampling config when the user explicitly overrides at
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// least one value. Leaving it as std::nullopt lets LlamaGenerator fall
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@@ -89,6 +89,7 @@ LlamaGenerator::LlamaGenerator(
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}
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n_ctx_ = sampling.n_ctx;
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n_gpu_layers_ = options.generator.n_gpu_layers;
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this->Load(model_path);
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}
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@@ -27,7 +27,8 @@ void LlamaGenerator::Load(const std::string& model_path) {
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// externally before attempting to load a model.
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ggml_backend_load_all();
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const llama_model_params model_params = llama_model_default_params();
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llama_model_params model_params = llama_model_default_params();
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model_params.n_gpu_layers = n_gpu_layers_;
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LlamaGenerator::ModelHandle loaded_model(
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llama_model_load_from_file(model_path.c_str(), model_params));
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if (!loaded_model) {
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