Implement SambaNova as new remote API Provider.

This commit is contained in:
swanhtet1992 2024-11-23 21:32:05 -06:00
parent 4e6c984c26
commit b6a79d6291
8 changed files with 485 additions and 0 deletions

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from .sambanova import get_distribution_template # noqa: F401

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version: '2'
name: sambanova
distribution_spec:
description: Use SambaNova for running LLM inference
docker_image: null
providers:
inference:
- remote::sambanova
memory:
- inline::faiss
- remote::chromadb
- remote::pgvector
safety:
- inline::llama-guard
agents:
- inline::meta-reference
telemetry:
- inline::meta-reference
image_type: conda

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version: '2'
image_name: sambanova
docker_image: null
conda_env: sambanova
apis:
- agents
- inference
- memory
- safety
- telemetry
providers:
inference:
- provider_id: sambanova
provider_type: remote::sambanova
config:
url: https://api.sambanova.ai/v1
api_key: ${env.SAMBANOVA_API_KEY}
memory:
- provider_id: faiss
provider_type: inline::faiss
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/sambanova}/faiss_store.db
safety:
- provider_id: llama-guard
provider_type: inline::llama-guard
config: {}
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/sambanova}/agents_store.db
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config: {}
metadata_store:
namespace: null
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/sambanova}/registry.db
models:
- metadata: {}
model_id: Meta-Llama-3.1-8B-Instruct
provider_id: null
provider_model_id: Meta-Llama-3.1-8B-Instruct
shields:
- params: null
shield_id: meta-llama/Llama-Guard-3-8B
provider_id: null
provider_shield_id: null
memory_banks: []
datasets: []
scoring_fns: []
eval_tasks: []

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from pathlib import Path
from llama_models.sku_list import all_registered_models
from llama_stack.distribution.datatypes import ModelInput, Provider, ShieldInput
from llama_stack.providers.remote.inference.sambanova import SambanovaImplConfig
from llama_stack.providers.remote.inference.sambanova.sambanova import MODEL_ALIASES
from llama_stack.templates.template import DistributionTemplate, RunConfigSettings
def get_distribution_template() -> DistributionTemplate:
providers = {
"inference": ["remote::sambanova"],
"memory": ["inline::faiss", "remote::chromadb", "remote::pgvector"],
"safety": ["inline::llama-guard"],
"agents": ["inline::meta-reference"],
"telemetry": ["inline::meta-reference"],
}
inference_provider = Provider(
provider_id="sambanova",
provider_type="remote::sambanova",
config=SambanovaImplConfig.sample_run_config(),
)
core_model_to_hf_repo = {
m.descriptor(): m.huggingface_repo for m in all_registered_models()
}
default_models = [
ModelInput(
model_id=core_model_to_hf_repo[m.llama_model],
provider_model_id=m.provider_model_id,
)
for m in MODEL_ALIASES
]
return DistributionTemplate(
name="sambanova",
distro_type="self_hosted",
description="Use SambaNova for running LLM inference",
docker_image=None,
template_path=Path(__file__).parent / "doc_template.md",
providers=providers,
default_models=default_models,
run_configs={
"run.yaml": RunConfigSettings(
provider_overrides={
"inference": [inference_provider],
},
default_models=default_models,
default_shields=[ShieldInput(shield_id="meta-llama/Llama-Guard-3-8B")],
),
},
run_config_env_vars={
"LLAMASTACK_PORT": (
"5001",
"Port for the Llama Stack distribution server",
),
"SAMBANOVA_API_KEY": (
"",
"SambaNova API Key",
),
},
)