forked from phoenix-oss/llama-stack-mirror

add 3.3 to together inference provider (#729 )

# What does this PR do?

- add llama3.3 model for together
- fix fireworks distro_codegen

```
python llama_stack/scripts/distro_codegen.py
```

## Test Plan

<img width="1132" alt="image"
src="https://github.com/user-attachments/assets/bf94b933-9200-4e73-878e-d1a95d450a88"
/>

**Tests**
```
pytest -v -s -k "together" --inference-model="meta-llama/Llama-3.3-70B-Instruct" ./llama_stack/providers/tests/inference/test_text_inference.py
```
<img width="1139" alt="image"
src="https://github.com/user-attachments/assets/407dc98b-8de3-4841-8cb1-75e4b5128544"
/>


## Sources

Please link relevant resources if necessary.


## Before submitting

- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [ ] Ran pre-commit to handle lint / formatting issues.
- [ ] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
      Pull Request section?
- [ ] Updated relevant documentation.
- [ ] Wrote necessary unit or integration tests.

2025-01-06 15:39:41 -08:00

2.4 KiB

Raw Blame History

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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`
memory	`inline::faiss`, `remote::chromadb`, `remote::pgvector`
safety	`inline::llama-guard`
scoring	`inline::basic`, `inline::llm-as-judge`, `inline::braintrust`
telemetry	`inline::meta-reference`

Environment Variables

The following environment variables can be configured:

LLAMASTACK_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 (fireworks/llama-v3p1-8b-instruct)
meta-llama/Llama-3.1-70B-Instruct (fireworks/llama-v3p1-70b-instruct)
meta-llama/Llama-3.1-405B-Instruct-FP8 (fireworks/llama-v3p1-405b-instruct)
meta-llama/Llama-3.2-1B-Instruct (fireworks/llama-v3p2-1b-instruct)
meta-llama/Llama-3.2-3B-Instruct (fireworks/llama-v3p2-3b-instruct)
meta-llama/Llama-3.2-11B-Vision-Instruct (fireworks/llama-v3p2-11b-vision-instruct)
meta-llama/Llama-3.2-90B-Vision-Instruct (fireworks/llama-v3p2-90b-vision-instruct)
meta-llama/Llama-3.3-70B-Instruct (fireworks/llama-v3p3-70b-instruct)
meta-llama/Llama-Guard-3-8B (fireworks/llama-guard-3-8b)
meta-llama/Llama-Guard-3-11B-Vision (fireworks/llama-guard-3-11b-vision)

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

2.4 KiB Raw Blame History