mirror of https://github.com/meta-llama/llama-stack.git synced 2025-06-28 02:53:30 +00:00

Ashwin Bharambe 272d3359ee

fix: remove code interpeter implementation (#2087 )

# What does this PR do?

The builtin implementation of code interpreter is not robust and has a
really weak sandboxing shell (the `bubblewrap` container). Given the
availability of better MCP code interpreter servers coming up, we should
use them instead of baking an implementation into the Stack and
expanding the vulnerability surface to the rest of the Stack.

This PR only does the removal. We will add examples with how to
integrate with MCPs in subsequent ones.

## Test Plan

Existing tests.

2025-05-01 14:35:08 -07:00

3.1 KiB

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Fireworks Distribution

:maxdepth: 2
:hidden:

self

The llamastack/distribution-fireworks distribution consists of the following provider configurations.

API	Provider(s)
agents	`inline::meta-reference`
datasetio	`remote::huggingface`, `inline::localfs`
eval	`inline::meta-reference`
inference	`remote::fireworks`, `inline::sentence-transformers`
safety	`inline::llama-guard`
scoring	`inline::basic`, `inline::llm-as-judge`, `inline::braintrust`
telemetry	`inline::meta-reference`
tool_runtime	`remote::brave-search`, `remote::tavily-search`, `remote::wolfram-alpha`, `inline::rag-runtime`, `remote::model-context-protocol`
vector_io	`inline::faiss`, `remote::chromadb`, `remote::pgvector`

Environment Variables

The following environment variables can be configured:

LLAMA_STACK_PORT: Port for the Llama Stack distribution server (default: 8321)
FIREWORKS_API_KEY: Fireworks.AI API Key (default: ``)

Models

The following models are available by default:

accounts/fireworks/models/llama-v3p1-8b-instruct (aliases: meta-llama/Llama-3.1-8B-Instruct)
accounts/fireworks/models/llama-v3p1-70b-instruct (aliases: meta-llama/Llama-3.1-70B-Instruct)
accounts/fireworks/models/llama-v3p1-405b-instruct (aliases: meta-llama/Llama-3.1-405B-Instruct-FP8)
accounts/fireworks/models/llama-v3p2-3b-instruct (aliases: meta-llama/Llama-3.2-3B-Instruct)
accounts/fireworks/models/llama-v3p2-11b-vision-instruct (aliases: meta-llama/Llama-3.2-11B-Vision-Instruct)
accounts/fireworks/models/llama-v3p2-90b-vision-instruct (aliases: meta-llama/Llama-3.2-90B-Vision-Instruct)
accounts/fireworks/models/llama-v3p3-70b-instruct (aliases: meta-llama/Llama-3.3-70B-Instruct)
accounts/fireworks/models/llama-guard-3-8b (aliases: meta-llama/Llama-Guard-3-8B)
accounts/fireworks/models/llama-guard-3-11b-vision (aliases: meta-llama/Llama-Guard-3-11B-Vision)
accounts/fireworks/models/llama4-scout-instruct-basic (aliases: meta-llama/Llama-4-Scout-17B-16E-Instruct)
accounts/fireworks/models/llama4-maverick-instruct-basic (aliases: meta-llama/Llama-4-Maverick-17B-128E-Instruct)
nomic-ai/nomic-embed-text-v1.5

Prerequisite: API Keys

Make sure you have access to a Fireworks API Key. You can get one by visiting fireworks.ai.

Running Llama Stack with Fireworks

You can do this via Conda (build code) or Docker which has a pre-built image.

Via Docker

This method allows you to get started quickly without having to build the distribution code.

LLAMA_STACK_PORT=8321
docker run \
  -it \
  --pull always \
  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
  llamastack/distribution-fireworks \
  --port $LLAMA_STACK_PORT \
  --env FIREWORKS_API_KEY=$FIREWORKS_API_KEY

Via Conda

llama stack build --template fireworks --image-type conda
llama stack run ./run.yaml \
  --port $LLAMA_STACK_PORT \
  --env FIREWORKS_API_KEY=$FIREWORKS_API_KEY

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