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init: first remote llamacpp implementation
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17
llama_stack/providers/remote/inference/llamacpp/__init__.py
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17
llama_stack/providers/remote/inference/llamacpp/__init__.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from llama_stack.apis.inference import InferenceProvider
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from .config import LlamaCppImplConfig
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async def get_adapter_impl(config: LlamaCppImplConfig, _deps) -> InferenceProvider:
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# import dynamically so the import is used only when it is needed
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from .llamacpp import LlamaCppInferenceAdapter
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adapter = LlamaCppInferenceAdapter(config)
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return adapter
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40
llama_stack/providers/remote/inference/llamacpp/config.py
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40
llama_stack/providers/remote/inference/llamacpp/config.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from typing import Any, Union
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from llama_stack.schema_utils import json_schema_type
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from pydantic import BaseModel, Field
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class LlamaCppProviderDataValidator(BaseModel):
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llamacpp_api_key: Union[str, None] = Field(
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default=None,
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description="API key for llama.cpp server (optional for local servers)",
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)
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@json_schema_type
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class LlamaCppImplConfig(BaseModel):
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api_key: Union[str, None] = Field(
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default=None,
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description="The llama.cpp server API key (optional for local servers)",
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)
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openai_compat_api_base: str = Field(
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default="http://localhost:8080/v1",
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description="The URL for the llama.cpp server with OpenAI-compatible API",
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)
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@classmethod
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def sample_run_config(
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cls, api_key: str = "${env.LLAMACPP_API_KEY:}"
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) -> dict[str, Any]:
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return {
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"openai_compat_api_base": "${env.LLAMACPP_URL:http://localhost:8080}/v1",
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"api_key": api_key,
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}
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36
llama_stack/providers/remote/inference/llamacpp/llamacpp.py
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36
llama_stack/providers/remote/inference/llamacpp/llamacpp.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from llama_stack.apis.models.models import Model
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from llama_stack.providers.remote.inference.llamacpp.config import LlamaCppImplConfig
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from llama_stack.providers.utils.inference.litellm_openai_mixin import (
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LiteLLMOpenAIMixin,
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)
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class LlamaCppInferenceAdapter(LiteLLMOpenAIMixin):
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_config: LlamaCppImplConfig
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def __init__(self, config: LlamaCppImplConfig):
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LiteLLMOpenAIMixin.__init__(
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self,
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model_entries=[], # llama.cpp can work with any GGUF model
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api_key_from_config=config.api_key,
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provider_data_api_key_field="llamacpp_api_key",
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openai_compat_api_base=config.openai_compat_api_base,
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)
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self.config = config
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async def register_model(self, model: Model) -> Model:
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# llama.cpp can work with any GGUF model, so we accept any model name
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# without validation against a predefined list
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return model
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async def initialize(self):
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await super().initialize()
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async def shutdown(self):
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await super().shutdown()
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7
llama_stack/templates/llamacpp/__init__.py
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7
llama_stack/templates/llamacpp/__init__.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from .llamacpp import get_distribution_template # noqa: F401
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38
llama_stack/templates/llamacpp/build.yaml
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38
llama_stack/templates/llamacpp/build.yaml
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version: "2"
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distribution_spec:
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description: Use llama.cpp server for running LLM inference
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providers:
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inference:
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- remote::llamacpp
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- inline::sentence-transformers
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vector_io:
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- inline::faiss
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- remote::chromadb
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- remote::pgvector
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safety:
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- inline::llama-guard
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agents:
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- inline::meta-reference
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telemetry:
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- inline::meta-reference
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eval:
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- inline::meta-reference
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datasetio:
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- remote::huggingface
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- inline::localfs
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scoring:
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- inline::basic
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- inline::llm-as-judge
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- inline::braintrust
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files:
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- inline::localfs
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tool_runtime:
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- remote::brave-search
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- remote::tavily-search
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- remote::wolfram-alpha
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- inline::rag-runtime
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- remote::model-context-protocol
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image_type: conda
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additional_pip_packages:
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- aiosqlite
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- sqlalchemy[asyncio]
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82
llama_stack/templates/llamacpp/doc_template.md
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82
llama_stack/templates/llamacpp/doc_template.md
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# Llama Stack with llama.cpp
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This template shows you how to run Llama Stack with [llama.cpp](https://github.com/ggerganov/llama.cpp) as the inference provider.
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## Prerequisites
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1. **Install llama.cpp**: Follow the installation instructions from the [llama.cpp repository](https://github.com/ggerganov/llama.cpp)
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2. **Download a model**: Download a GGUF format model file (e.g., from Hugging Face)
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## Starting llama.cpp Server
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Before running Llama Stack, you need to start the llama.cpp server:
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```bash
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# Example: Start llama.cpp server with a model
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./llama-server -m /path/to/your/model.gguf -c 4096 --host 0.0.0.0 --port 8080
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```
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Common llama.cpp server options:
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- `-m`: Path to the GGUF model file
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- `-c`: Context size (default: 512)
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- `--host`: Host to bind to (default: 127.0.0.1)
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- `--port`: Port to bind to (default: 8080)
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- `-ngl`: Number of layers to offload to GPU
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- `--chat-template`: Chat template to use
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## Environment Variables
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Set these environment variables before running Llama Stack:
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```bash
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export LLAMACPP_URL=http://localhost:8080 # URL of your llama.cpp server (without /v1 suffix)
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export INFERENCE_MODEL=your-model-name # Name/identifier for your model
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export LLAMACPP_API_KEY="" # API key (leave empty for local servers)
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```
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## Running Llama Stack
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```bash
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llama stack run llamacpp
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```
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## Configuration
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The template uses the following configuration:
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- **Inference Provider**: `remote::llamacpp` - Connects to your llama.cpp server via OpenAI-compatible API
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- **Default URL**: `http://localhost:8080` (configurable via `LLAMACPP_URL`)
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- **Vector Store**: FAISS for local vector storage
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- **Safety**: Llama Guard for content safety
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- **Other providers**: Standard Meta reference implementations
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## Model Support
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This template works with any GGUF format model supported by llama.cpp, including:
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- Llama 2/3 models
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- Code Llama models
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- Other transformer-based models converted to GGUF format
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## Troubleshooting
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1. **Connection refused**: Make sure your llama.cpp server is running and accessible
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2. **Model not found**: Verify the model path and that the GGUF file exists
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3. **Out of memory**: Reduce context size (`-c`) or use GPU offloading (`-ngl`)
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4. **Slow inference**: Consider using GPU acceleration or quantized models
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## Advanced Configuration
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You can customize the llama.cpp server configuration by modifying the server startup command. For production use, consider:
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- Using GPU acceleration with `-ngl` parameter
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- Adjusting batch size with `-b` parameter
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- Setting appropriate context size with `-c` parameter
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- Using multiple threads with `-t` parameter
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For more llama.cpp server options, run:
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```bash
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./llama-server --help
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```
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156
llama_stack/templates/llamacpp/llamacpp.py
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156
llama_stack/templates/llamacpp/llamacpp.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from pathlib import Path
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from llama_stack.apis.models.models import ModelType
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from llama_stack.distribution.datatypes import (
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ModelInput,
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Provider,
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ShieldInput,
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ToolGroupInput,
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)
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from llama_stack.providers.inline.files.localfs.config import LocalfsFilesImplConfig
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from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
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from llama_stack.providers.remote.inference.llamacpp.config import LlamaCppImplConfig
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from llama_stack.templates.template import DistributionTemplate, RunConfigSettings
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def get_distribution_template() -> DistributionTemplate:
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providers = {
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"inference": ["remote::llamacpp"],
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"vector_io": ["inline::faiss", "remote::chromadb", "remote::pgvector"],
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"safety": ["inline::llama-guard"],
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"agents": ["inline::meta-reference"],
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"telemetry": ["inline::meta-reference"],
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"eval": ["inline::meta-reference"],
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"datasetio": ["remote::huggingface", "inline::localfs"],
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"scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"],
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"files": ["inline::localfs"],
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"tool_runtime": [
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"remote::brave-search",
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"remote::tavily-search",
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"remote::wolfram-alpha",
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"inline::rag-runtime",
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"remote::model-context-protocol",
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],
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}
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name = "llamacpp"
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inference_provider = Provider(
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provider_id="llamacpp",
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provider_type="remote::llamacpp",
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config=LlamaCppImplConfig.sample_run_config(),
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)
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vector_io_provider_faiss = Provider(
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provider_id="faiss",
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provider_type="inline::faiss",
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config=FaissVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
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)
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files_provider = Provider(
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provider_id="meta-reference-files",
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provider_type="inline::localfs",
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config=LocalfsFilesImplConfig.sample_run_config(
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f"~/.llama/distributions/{name}"
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),
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)
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inference_model = ModelInput(
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model_id="${env.INFERENCE_MODEL}",
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provider_id="llamacpp",
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)
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safety_model = ModelInput(
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model_id="${env.SAFETY_MODEL}",
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provider_id="llamacpp",
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)
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embedding_model = ModelInput(
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model_id="all-MiniLM-L6-v2",
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provider_id="sentence-transformers",
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model_type=ModelType.embedding,
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metadata={
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"embedding_dimension": 384,
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},
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)
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default_tool_groups = [
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ToolGroupInput(
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toolgroup_id="builtin::websearch",
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provider_id="tavily-search",
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),
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ToolGroupInput(
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toolgroup_id="builtin::rag",
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provider_id="rag-runtime",
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),
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ToolGroupInput(
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toolgroup_id="builtin::wolfram_alpha",
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provider_id="wolfram-alpha",
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),
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]
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return DistributionTemplate(
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name=name,
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distro_type="self_hosted",
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description="Use llama.cpp server for running LLM inference",
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container_image=None,
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template_path=Path(__file__).parent / "doc_template.md",
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providers=providers,
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run_configs={
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"run.yaml": RunConfigSettings(
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provider_overrides={
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"inference": [inference_provider],
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"vector_io": [vector_io_provider_faiss],
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"files": [files_provider],
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},
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default_models=[inference_model, embedding_model],
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default_tool_groups=default_tool_groups,
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),
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"run-with-safety.yaml": RunConfigSettings(
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provider_overrides={
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"inference": [inference_provider],
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"vector_io": [vector_io_provider_faiss],
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"files": [files_provider],
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"safety": [
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Provider(
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provider_id="llama-guard",
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provider_type="inline::llama-guard",
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config={},
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),
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],
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},
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default_models=[
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inference_model,
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safety_model,
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embedding_model,
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],
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default_shields=[
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ShieldInput(
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shield_id="${env.SAFETY_MODEL}",
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provider_id="llama-guard",
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),
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],
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default_tool_groups=default_tool_groups,
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),
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},
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run_config_env_vars={
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"LLAMA_STACK_PORT": (
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"8321",
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"Port for the Llama Stack distribution server",
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),
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"LLAMACPP_URL": (
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"http://localhost:8080",
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"URL of the llama.cpp server (without /v1 suffix)",
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),
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"LLAMACPP_API_KEY": (
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"",
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"API key for llama.cpp server (leave empty for local servers)",
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),
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"INFERENCE_MODEL": (
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"llama-model",
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"Inference model identifier for llama.cpp server",
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),
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"SAFETY_MODEL": (
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"llama-guard",
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"Safety model identifier for llama.cpp server",
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),
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},
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)
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152
llama_stack/templates/llamacpp/run-with-safety.yaml
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152
llama_stack/templates/llamacpp/run-with-safety.yaml
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version: "2"
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image_name: llamacpp
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apis:
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- agents
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- datasetio
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- eval
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- files
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- inference
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- safety
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- scoring
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- telemetry
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- tool_runtime
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- vector_io
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providers:
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inference:
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- provider_id: llamacpp
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provider_type: remote::llamacpp
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config:
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openai_compat_api_base: ${env.LLAMACPP_URL:http://localhost:8080}/v1
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api_key: ${env.LLAMACPP_API_KEY:}
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- provider_id: sentence-transformers
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provider_type: inline::sentence-transformers
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config: {}
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vector_io:
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- provider_id: faiss
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provider_type: inline::faiss
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config:
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kvstore:
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type: sqlite
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namespace: null
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db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llamacpp}/faiss_store.db
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safety:
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- provider_id: llama-guard
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provider_type: inline::llama-guard
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config: {}
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agents:
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- provider_id: meta-reference
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provider_type: inline::meta-reference
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config:
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persistence_store:
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type: sqlite
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namespace: null
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db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llamacpp}/agents_store.db
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responses_store:
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type: sqlite
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db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llamacpp}/responses_store.db
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telemetry:
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- provider_id: meta-reference
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provider_type: inline::meta-reference
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config:
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service_name: "${env.OTEL_SERVICE_NAME:\u200B}"
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sinks: ${env.TELEMETRY_SINKS:console,sqlite}
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sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llamacpp}/trace_store.db
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eval:
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- provider_id: meta-reference
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provider_type: inline::meta-reference
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config:
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kvstore:
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type: sqlite
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namespace: null
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db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llamacpp}/meta_reference_eval.db
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datasetio:
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- provider_id: huggingface
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provider_type: remote::huggingface
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config:
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kvstore:
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type: sqlite
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namespace: null
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db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llamacpp}/huggingface_datasetio.db
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- provider_id: localfs
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provider_type: inline::localfs
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config:
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kvstore:
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type: sqlite
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namespace: null
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db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llamacpp}/localfs_datasetio.db
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scoring:
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- provider_id: basic
|
||||
provider_type: inline::basic
|
||||
config: {}
|
||||
- provider_id: llm-as-judge
|
||||
provider_type: inline::llm-as-judge
|
||||
config: {}
|
||||
- provider_id: braintrust
|
||||
provider_type: inline::braintrust
|
||||
config:
|
||||
openai_api_key: ${env.OPENAI_API_KEY:}
|
||||
files:
|
||||
- provider_id: meta-reference-files
|
||||
provider_type: inline::localfs
|
||||
config:
|
||||
storage_dir: ${env.FILES_STORAGE_DIR:~/.llama/distributions/llamacpp/files}
|
||||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llamacpp}/files_metadata.db
|
||||
tool_runtime:
|
||||
- provider_id: brave-search
|
||||
provider_type: remote::brave-search
|
||||
config:
|
||||
api_key: ${env.BRAVE_SEARCH_API_KEY:}
|
||||
max_results: 3
|
||||
- provider_id: tavily-search
|
||||
provider_type: remote::tavily-search
|
||||
config:
|
||||
api_key: ${env.TAVILY_SEARCH_API_KEY:}
|
||||
max_results: 3
|
||||
- provider_id: rag-runtime
|
||||
provider_type: inline::rag-runtime
|
||||
config: {}
|
||||
- provider_id: model-context-protocol
|
||||
provider_type: remote::model-context-protocol
|
||||
config: {}
|
||||
- provider_id: wolfram-alpha
|
||||
provider_type: remote::wolfram-alpha
|
||||
config:
|
||||
api_key: ${env.WOLFRAM_ALPHA_API_KEY:}
|
||||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llamacpp}/registry.db
|
||||
inference_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llamacpp}/inference_store.db
|
||||
models:
|
||||
- metadata: {}
|
||||
model_id: ${env.INFERENCE_MODEL}
|
||||
provider_id: llamacpp
|
||||
model_type: llm
|
||||
- metadata: {}
|
||||
model_id: ${env.SAFETY_MODEL}
|
||||
provider_id: llamacpp
|
||||
model_type: llm
|
||||
- metadata:
|
||||
embedding_dimension: 384
|
||||
model_id: all-MiniLM-L6-v2
|
||||
provider_id: sentence-transformers
|
||||
model_type: embedding
|
||||
shields:
|
||||
- shield_id: ${env.SAFETY_MODEL}
|
||||
provider_id: llama-guard
|
||||
vector_dbs: []
|
||||
datasets: []
|
||||
scoring_fns: []
|
||||
benchmarks: []
|
||||
tool_groups:
|
||||
- toolgroup_id: builtin::websearch
|
||||
provider_id: tavily-search
|
||||
- toolgroup_id: builtin::rag
|
||||
provider_id: rag-runtime
|
||||
- toolgroup_id: builtin::wolfram_alpha
|
||||
provider_id: wolfram-alpha
|
||||
server:
|
||||
port: 8321
|
147
llama_stack/templates/llamacpp/run.yaml
Normal file
147
llama_stack/templates/llamacpp/run.yaml
Normal file
|
@ -0,0 +1,147 @@
|
|||
version: "2"
|
||||
image_name: llamacpp
|
||||
apis:
|
||||
- agents
|
||||
- datasetio
|
||||
- eval
|
||||
- files
|
||||
- inference
|
||||
- safety
|
||||
- scoring
|
||||
- telemetry
|
||||
- tool_runtime
|
||||
- vector_io
|
||||
providers:
|
||||
inference:
|
||||
- provider_id: llamacpp
|
||||
provider_type: remote::llamacpp
|
||||
config:
|
||||
openai_compat_api_base: ${env.LLAMACPP_URL:http://localhost:8080}/v1
|
||||
api_key: ${env.LLAMACPP_API_KEY:}
|
||||
- provider_id: sentence-transformers
|
||||
provider_type: inline::sentence-transformers
|
||||
config: {}
|
||||
vector_io:
|
||||
- provider_id: faiss
|
||||
provider_type: inline::faiss
|
||||
config:
|
||||
kvstore:
|
||||
type: sqlite
|
||||
namespace: null
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llamacpp}/faiss_store.db
|
||||
safety:
|
||||
- provider_id: llama-guard
|
||||
provider_type: inline::llama-guard
|
||||
config:
|
||||
excluded_categories: []
|
||||
agents:
|
||||
- provider_id: meta-reference
|
||||
provider_type: inline::meta-reference
|
||||
config:
|
||||
persistence_store:
|
||||
type: sqlite
|
||||
namespace: null
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llamacpp}/agents_store.db
|
||||
responses_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llamacpp}/responses_store.db
|
||||
telemetry:
|
||||
- provider_id: meta-reference
|
||||
provider_type: inline::meta-reference
|
||||
config:
|
||||
service_name: "${env.OTEL_SERVICE_NAME:\u200B}"
|
||||
sinks: ${env.TELEMETRY_SINKS:console,sqlite}
|
||||
sqlite_db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llamacpp}/trace_store.db
|
||||
eval:
|
||||
- provider_id: meta-reference
|
||||
provider_type: inline::meta-reference
|
||||
config:
|
||||
kvstore:
|
||||
type: sqlite
|
||||
namespace: null
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llamacpp}/meta_reference_eval.db
|
||||
datasetio:
|
||||
- provider_id: huggingface
|
||||
provider_type: remote::huggingface
|
||||
config:
|
||||
kvstore:
|
||||
type: sqlite
|
||||
namespace: null
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llamacpp}/huggingface_datasetio.db
|
||||
- provider_id: localfs
|
||||
provider_type: inline::localfs
|
||||
config:
|
||||
kvstore:
|
||||
type: sqlite
|
||||
namespace: null
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llamacpp}/localfs_datasetio.db
|
||||
scoring:
|
||||
- provider_id: basic
|
||||
provider_type: inline::basic
|
||||
config: {}
|
||||
- provider_id: llm-as-judge
|
||||
provider_type: inline::llm-as-judge
|
||||
config: {}
|
||||
- provider_id: braintrust
|
||||
provider_type: inline::braintrust
|
||||
config:
|
||||
openai_api_key: ${env.OPENAI_API_KEY:}
|
||||
files:
|
||||
- provider_id: meta-reference-files
|
||||
provider_type: inline::localfs
|
||||
config:
|
||||
storage_dir: ${env.FILES_STORAGE_DIR:~/.llama/distributions/llamacpp/files}
|
||||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llamacpp}/files_metadata.db
|
||||
tool_runtime:
|
||||
- provider_id: brave-search
|
||||
provider_type: remote::brave-search
|
||||
config:
|
||||
api_key: ${env.BRAVE_SEARCH_API_KEY:}
|
||||
max_results: 3
|
||||
- provider_id: tavily-search
|
||||
provider_type: remote::tavily-search
|
||||
config:
|
||||
api_key: ${env.TAVILY_SEARCH_API_KEY:}
|
||||
max_results: 3
|
||||
- provider_id: rag-runtime
|
||||
provider_type: inline::rag-runtime
|
||||
config: {}
|
||||
- provider_id: model-context-protocol
|
||||
provider_type: remote::model-context-protocol
|
||||
config: {}
|
||||
- provider_id: wolfram-alpha
|
||||
provider_type: remote::wolfram-alpha
|
||||
config:
|
||||
api_key: ${env.WOLFRAM_ALPHA_API_KEY:}
|
||||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llamacpp}/registry.db
|
||||
inference_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/llamacpp}/inference_store.db
|
||||
models:
|
||||
- metadata: {}
|
||||
model_id: ${env.INFERENCE_MODEL}
|
||||
provider_id: llamacpp
|
||||
model_type: llm
|
||||
- metadata:
|
||||
embedding_dimension: 384
|
||||
model_id: all-MiniLM-L6-v2
|
||||
provider_id: sentence-transformers
|
||||
model_type: embedding
|
||||
shields: []
|
||||
vector_dbs: []
|
||||
datasets: []
|
||||
scoring_fns: []
|
||||
benchmarks: []
|
||||
tool_groups:
|
||||
- toolgroup_id: builtin::websearch
|
||||
provider_id: tavily-search
|
||||
- toolgroup_id: builtin::rag
|
||||
provider_id: rag-runtime
|
||||
- toolgroup_id: builtin::wolfram_alpha
|
||||
provider_id: wolfram-alpha
|
||||
server:
|
||||
port: 8321
|
Loading…
Add table
Add a link
Reference in a new issue