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feat sambanova models (#1858)
Co-authored-by: jorgep_snova <jorge.piedrahita@sambanovasystems.com> Co-authored-by: João Moura <joaomdmoura@gmail.com>
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Devin AI
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0af5352a25
@@ -161,6 +161,7 @@ The CLI will initially prompt for API keys for the following services:
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* Groq
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* Anthropic
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* Google Gemini
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* SambaNova
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When you select a provider, the CLI will prompt you to enter your API key.
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@@ -146,6 +146,19 @@ Here's a detailed breakdown of supported models and their capabilities, you can
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Groq is known for its fast inference speeds, making it suitable for real-time applications.
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</Tip>
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</Tab>
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<Tab title="SambaNova">
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| Model | Context Window | Best For |
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|-------|---------------|-----------|
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| Llama 3.1 70B/8B | Up to 131,072 tokens | High-performance, large context tasks |
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| Llama 3.1 405B | 8,192 tokens | High-performance and output quality |
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| Llama 3.2 Series | 8,192 tokens | General-purpose tasks, multimodal |
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| Llama 3.3 70B | Up to 131,072 tokens | High-performance and output quality|
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| Qwen2 familly | 8,192 tokens | High-performance and output quality |
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<Tip>
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[SambaNova](https://cloud.sambanova.ai/) has several models with fast inference speed at full precision.
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</Tip>
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</Tab>
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<Tab title="Others">
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| Provider | Context Window | Key Features |
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|----------|---------------|--------------|
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@@ -32,6 +32,7 @@ LiteLLM supports a wide range of providers, including but not limited to:
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- Cloudflare Workers AI
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- DeepInfra
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- Groq
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- SambaNova
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- [NVIDIA NIMs](https://docs.api.nvidia.com/nim/reference/models-1)
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- And many more!
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@@ -85,6 +85,12 @@ ENV_VARS = {
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"key_name": "CEREBRAS_API_KEY",
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},
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],
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"sambanova": [
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{
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"prompt": "Enter your SambaNovaCloud API key (press Enter to skip)",
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"key_name": "SAMBANOVA_API_KEY",
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}
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],
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}
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@@ -98,6 +104,7 @@ PROVIDERS = [
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"bedrock",
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"azure",
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"cerebras",
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"sambanova",
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]
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MODELS = {
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@@ -156,6 +163,19 @@ MODELS = {
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"bedrock/mistral.mistral-7b-instruct-v0:2",
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"bedrock/mistral.mixtral-8x7b-instruct-v0:1",
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],
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"sambanova": [
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"sambanova/Meta-Llama-3.3-70B-Instruct",
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"sambanova/QwQ-32B-Preview",
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"sambanova/Qwen2.5-72B-Instruct",
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"sambanova/Qwen2.5-Coder-32B-Instruct",
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"sambanova/Meta-Llama-3.1-405B-Instruct",
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"sambanova/Meta-Llama-3.1-70B-Instruct",
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"sambanova/Meta-Llama-3.1-8B-Instruct",
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"sambanova/Llama-3.2-90B-Vision-Instruct",
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"sambanova/Llama-3.2-11B-Vision-Instruct",
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"sambanova/Meta-Llama-3.2-3B-Instruct",
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"sambanova/Meta-Llama-3.2-1B-Instruct",
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],
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}
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DEFAULT_LLM_MODEL = "gpt-4o-mini"
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@@ -76,6 +76,18 @@ LLM_CONTEXT_WINDOW_SIZES = {
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"mixtral-8x7b-32768": 32768,
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"llama-3.3-70b-versatile": 128000,
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"llama-3.3-70b-instruct": 128000,
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#sambanova
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"Meta-Llama-3.3-70B-Instruct": 131072,
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"QwQ-32B-Preview": 8192,
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"Qwen2.5-72B-Instruct": 8192,
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"Qwen2.5-Coder-32B-Instruct": 8192,
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"Meta-Llama-3.1-405B-Instruct": 8192,
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"Meta-Llama-3.1-70B-Instruct": 131072,
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"Meta-Llama-3.1-8B-Instruct": 131072,
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"Llama-3.2-90B-Vision-Instruct": 16384,
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"Llama-3.2-11B-Vision-Instruct": 16384,
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"Meta-Llama-3.2-3B-Instruct": 4096,
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"Meta-Llama-3.2-1B-Instruct": 16384,
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}
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DEFAULT_CONTEXT_WINDOW_SIZE = 8192
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