mirror of https://github.com/meta-llama/llama-stack.git synced 2025-06-27 18:50:41 +00:00

Ashwin Bharambe 9436dd570d

feat: register embedding models for ollama, together, fireworks (#1190 )

# What does this PR do?

We have support for embeddings in our Inference providers, but so far we
haven't done the final step of actually registering the known embedding
models and making sure they are extremely easy to use. This is one step
towards that.

## Test Plan

Run existing inference tests.

```bash

$ cd llama_stack/providers/tests/inference
$ pytest -s -v -k fireworks test_embeddings.py \
   --inference-model nomic-ai/nomic-embed-text-v1.5 --env EMBEDDING_DIMENSION=784
$  pytest -s -v -k together test_embeddings.py \
   --inference-model togethercomputer/m2-bert-80M-8k-retrieval --env EMBEDDING_DIMENSION=784
$ pytest -s -v -k ollama test_embeddings.py \
   --inference-model all-minilm:latest --env EMBEDDING_DIMENSION=784
```

The value of the EMBEDDING_DIMENSION isn't actually used in these tests,
it is merely used by the test fixtures to check if the model is an LLM
or Embedding.

2025-02-20 15:39:08 -08:00

2.8 KiB

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orphan
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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`
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`, `inline::code-interpreter`, `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: 5001)
FIREWORKS_API_KEY: Fireworks.AI API Key (default: ``)

Models

The following models are available by default:

meta-llama/Llama-3.1-8B-Instruct (accounts/fireworks/models/llama-v3p1-8b-instruct)
meta-llama/Llama-3.1-70B-Instruct (accounts/fireworks/models/llama-v3p1-70b-instruct)
meta-llama/Llama-3.1-405B-Instruct-FP8 (accounts/fireworks/models/llama-v3p1-405b-instruct)
meta-llama/Llama-3.2-1B-Instruct (accounts/fireworks/models/llama-v3p2-1b-instruct)
meta-llama/Llama-3.2-3B-Instruct (accounts/fireworks/models/llama-v3p2-3b-instruct)
meta-llama/Llama-3.2-11B-Vision-Instruct (accounts/fireworks/models/llama-v3p2-11b-vision-instruct)
meta-llama/Llama-3.2-90B-Vision-Instruct (accounts/fireworks/models/llama-v3p2-90b-vision-instruct)
meta-llama/Llama-3.3-70B-Instruct (accounts/fireworks/models/llama-v3p3-70b-instruct)
meta-llama/Llama-Guard-3-8B (accounts/fireworks/models/llama-guard-3-8b)
meta-llama/Llama-Guard-3-11B-Vision (accounts/fireworks/models/llama-guard-3-11b-vision)
nomic-ai/nomic-embed-text-v1.5 (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=5001
docker run \
  -it \
  -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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