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30 more models added with existing providers (#3)
* 30 more models added with existing providers * Mypy pytest fixes * version updated
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‎CHANGELOG.md‎

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@@ -6,6 +6,19 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
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and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
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## [1.0.1] - 2025-01-07
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### Added
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- **New Providers:**
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- **Databricks (5 models):** dbrx-instruct, dbrx-base, dolly-v2-12b, dolly-v2-7b, dolly-v2-3b
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- **Voyage AI (6 models):** voyage-2, voyage-large-2, voyage-code-2, voyage-finance-2, voyage-law-2, voyage-multilingual-2
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- **30+ new models added across existing providers**
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### Enhanced
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- **Provider-Specific Approximations:** Added optimized tokenization approximations for Databricks and Voyage AI models.
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- **Model Detection:** Enhanced provider detection to support Databricks and Voyage AI models.
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- **Cost Estimation:** Added pricing information for all new models.
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## [1.0.0] - 2025-01-06
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### Added

‎README.md‎

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# toksum
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A comprehensive Python library for counting tokens across 300+ Large Language Models (LLMs) from 32+ providers.
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A comprehensive Python library for counting tokens across 300+ Large Language Models (LLMs) from 34+ providers.
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[![PyPI version](https://badge.fury.io/py/toksum.svg)](https://badge.fury.io/py/toksum)
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[![Python Support](https://img.shields.io/pypi/pyversions/toksum.svg)](https://pypi.org/project/toksum/)
@@ -9,8 +9,8 @@ A comprehensive Python library for counting tokens across 300+ Large Language Mo
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## Features
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- **🎯 Production Ready v1.0.0**: Comprehensive support for 300+ models across 32+ providers including OpenAI, Anthropic, Google, Meta, Mistral, Microsoft, Amazon, Nvidia, IBM, Salesforce, BigCode, and many more
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- **Comprehensive Multi-LLM Support**: Count tokens for 279 models across 32 providers including OpenAI, Anthropic, Google, Meta, Mistral, Microsoft, Amazon, Nvidia, IBM, Salesforce, BigCode, and many more
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- **🎯 Production Ready v1.0.1**: Comprehensive support for 300+ models across 34+ providers including OpenAI, Anthropic, Google, Meta, Mistral, Microsoft, Amazon, Nvidia, IBM, Salesforce, BigCode, Databricks, Voyage AI, and many more
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- **Comprehensive Multi-LLM Support**: Count tokens for 300+ models across 34 providers including OpenAI, Anthropic, Google, Meta, Mistral, Microsoft, Amazon, Nvidia, IBM, Salesforce, BigCode, Databricks, Voyage AI, and many more
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- **Accurate Tokenization**: Uses official tokenizers (tiktoken for OpenAI) and optimized approximations for all other providers
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- **Chat Message Support**: Count tokens in chat/conversation format with proper message overhead calculation
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- **Cost Estimation**: Estimate API costs based on token counts and current pricing
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- Multi-language code generation and understanding
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- Trained on diverse programming languages
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### Databricks Models (5 models)
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- **NEW: Databricks Models** (dbrx-instruct, dbrx-base, dolly-v2-12b, dolly-v2-7b, dolly-v2-3b)
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- High-quality instruction-following and base models
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**Total: 300+ models across 32+ providers**
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### Voyage AI Models (6 models)
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- **NEW: Voyage AI Models** (voyage-2, voyage-large-2, voyage-code-2, voyage-finance-2, voyage-law-2, voyage-multilingual-2)
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- State-of-the-art embedding models for various domains
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**Total: 300+ models across 34+ providers**
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## Installation
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‎pyproject.toml‎

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@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
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[project]
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name = "toksum"
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version = "1.0.0"
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version = "1.0.1"
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description = "A comprehensive Python library for counting tokens across 300+ LLM models from 32+ providers including OpenAI, Anthropic, Google, Meta, Mistral, and more"
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readme = "README.md"
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requires-python = ">=3.8"

‎tests/test_toksum.py‎

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@@ -686,11 +686,10 @@ def test_eleutherai_models(self):
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assert tokens > 0
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def test_mosaicml_models(self):
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"""Test MosaicML/Databricks models."""
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"""Test MosaicML models."""
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mosaicml_models = [
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"mpt-7b", "mpt-7b-chat", "mpt-7b-instruct",
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"mpt-30b", "mpt-30b-chat", "mpt-30b-instruct",
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"dbrx", "dbrx-instruct"
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]
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for model in mosaicml_models:
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assert isinstance(tokens, int)
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assert tokens > 0
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def test_databricks_models(self):
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"""Test Databricks models."""
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databricks_models = [
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"dbrx", "dbrx-instruct", "dbrx-base",
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"dolly-v2-12b", "dolly-v2-7b", "dolly-v2-3b",
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]
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for model in databricks_models:
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counter = TokenCounter(model)
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assert counter.provider == "databricks"
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# Test basic token counting
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tokens = counter.count("Hello, world!")
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assert isinstance(tokens, int)
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assert tokens > 0
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def test_replit_models(self):
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"""Test Replit code models."""
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replit_models = ["replit-code-v1-3b", "replit-code-v1.5-3b", "replit-code-v2-3b"]
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"stability": 7,
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"tii": 6,
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"eleutherai": 12,
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"mosaicml": 8,
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"mosaicml": 6, # Updated: Removed dbrx and dbrx-instruct
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"databricks": 6, # Updated: Added dbrx
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"replit": 3,
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"minimax": 5,
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"aleph_alpha": 4,
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"openai", "anthropic", "google", "meta", "mistral",
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"cohere", "perplexity", "huggingface", "ai21", "together",
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"xai", "alibaba", "baidu", "huawei", "yandex", "stability",
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"tii", "eleutherai", "mosaicml", "replit", "minimax",
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"tii", "eleutherai", "mosaicml", "databricks", "replit", "minimax",
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"aleph_alpha", "deepseek", "tsinghua", "rwkv", "community",
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"microsoft", "amazon", "nvidia", "ibm", "salesforce", "bigcode"
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"microsoft", "amazon", "nvidia", "ibm", "salesforce", "bigcode", "voyage" # Added voyage
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}
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actual_providers = set(models.keys())
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assert actual_providers == expected_providers
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if __name__ == "__main__":
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pytest.main([__file__])
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pytest.main([__file__])

‎toksum/core.py‎

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"pythia-12b": "pythia", # NEW
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}
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# MosaicML/Databricks Models (using approximation)
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# MosaicML Models (using approximation)
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MOSAICML_MODELS = {
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"mpt-7b": "mpt", # NEW
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"mpt-7b-chat": "mpt", # NEW
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"mpt-7b-instruct": "mpt", # NEW
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"mpt-30b": "mpt", # NEW
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"mpt-30b-chat": "mpt", # NEW
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"mpt-30b-instruct": "mpt", # NEW
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"dbrx": "dbrx", # NEW
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"dbrx-instruct": "dbrx", # NEW
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}
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# Replit Models (using approximation)
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"text-similarity-davinci-001": "r50k_base", # ADDED
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}
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# Databricks Models
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DATABRICKS_MODELS = {
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"dbrx": "databricks", # ADDED
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"dbrx-instruct": "databricks",
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"dbrx-base": "databricks",
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"dolly-v2-12b": "databricks",
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"dolly-v2-7b": "databricks",
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"dolly-v2-3b": "databricks",
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}
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# Voyage AI Models
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VOYAGE_MODELS = {
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"voyage-2": "voyage",
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"voyage-large-2": "voyage",
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"voyage-code-2": "voyage",
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"voyage-finance-2": "voyage",
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"voyage-law-2": "voyage",
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"voyage-multilingual-2": "voyage",
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}
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class TokenCounter:
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"""
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openai_legacy_models_lower = {k.lower(): v for k, v in OPENAI_LEGACY_MODELS.items()}
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openai_o1_models_lower = {k.lower(): v for k, v in OPENAI_O1_MODELS.items()}
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openai_vision_models_lower = {k.lower(): v for k, v in OPENAI_VISION_MODELS.items()}
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databricks_models_lower = {k.lower(): v for k, v in DATABRICKS_MODELS.items()}
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voyage_models_lower = {k.lower(): v for k, v in VOYAGE_MODELS.items()}
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anthropic_models_lower = {k.lower(): v for k, v in ANTHROPIC_MODELS.items()}
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anthropic_legacy_models_lower = {k.lower(): v for k, v in ANTHROPIC_LEGACY_MODELS.items()}
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anthropic_haiku_models_lower = {k.lower(): v for k, v in ANTHROPIC_HAIKU_MODELS.items()}
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mistral_instruct_models_lower = {k.lower(): v for k, v in MISTRAL_INSTRUCT_MODELS.items()}
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openai_embedding_models_lower = {k.lower(): v for k, v in OPENAI_EMBEDDING_MODELS.items()}
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if (self.model in openai_models_lower or self.model in openai_legacy_models_lower or
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# Prioritize Databricks models as they are more specific
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if self.model in databricks_models_lower:
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return "databricks"
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elif self.model in voyage_models_lower:
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return "voyage"
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elif (self.model in openai_models_lower or self.model in openai_legacy_models_lower or
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self.model in openai_o1_models_lower or self.model in openai_vision_models_lower or
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self.model in openai_gpt4_turbo_models_lower or self.model in openai_embedding_models_lower):
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return "openai"
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elif self.model in bigcode_models_lower:
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return "bigcode"
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else:
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supported = (list(OPENAI_MODELS.keys()) + list(OPENAI_LEGACY_MODELS.keys()) + list(OPENAI_O1_MODELS.keys()) +
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supported = (list(DATABRICKS_MODELS.keys()) + list(VOYAGE_MODELS.keys()) + list(OPENAI_MODELS.keys()) + list(OPENAI_LEGACY_MODELS.keys()) + list(OPENAI_O1_MODELS.keys()) +
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list(OPENAI_VISION_MODELS.keys()) + list(ANTHROPIC_MODELS.keys()) + list(ANTHROPIC_LEGACY_MODELS.keys()) +
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list(ANTHROPIC_HAIKU_MODELS.keys()) + list(ANTHROPIC_COMPUTER_USE_MODELS.keys()) +
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list(ANTHROPIC_CLAUDE_21_MODELS.keys()) + list(ANTHROPIC_INSTANT_2_MODELS.keys()) +
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# BigCode StarCoder models
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base_tokens = char_count / 3.4
9771002
adjustment = (whitespace_count + punctuation_count) * 0.2
1003+
elif self.provider == "databricks":
1004+
# Databricks models
1005+
base_tokens = char_count / 4.0
1006+
adjustment = (whitespace_count + punctuation_count) * 0.25
1007+
elif self.provider == "voyage":
1008+
# Voyage AI models
1009+
base_tokens = char_count / 3.8
1010+
adjustment = (whitespace_count + punctuation_count) * 0.25
9781011
else:
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# Default approximation
9801013
base_tokens = char_count / 4
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10861119
"openai": (list(OPENAI_MODELS.keys()) + list(OPENAI_LEGACY_MODELS.keys()) +
10871120
list(OPENAI_O1_MODELS.keys()) + list(OPENAI_VISION_MODELS.keys()) +
10881121
list(OPENAI_GPT4_TURBO_MODELS.keys()) + list(OPENAI_EMBEDDING_MODELS.keys())),
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"databricks": list(DATABRICKS_MODELS.keys()),
1123+
"voyage": list(VOYAGE_MODELS.keys()),
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"anthropic": (list(ANTHROPIC_MODELS.keys()) + list(ANTHROPIC_LEGACY_MODELS.keys()) +
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list(ANTHROPIC_HAIKU_MODELS.keys()) + list(ANTHROPIC_COMPUTER_USE_MODELS.keys()) +
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list(ANTHROPIC_CLAUDE_21_MODELS.keys()) + list(ANTHROPIC_INSTANT_2_MODELS.keys()) +
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11091144
"stability": list(STABILITY_MODELS.keys()),
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"tii": list(TII_MODELS.keys()),
11111146
"eleutherai": list(ELEUTHERAI_MODELS.keys()),
1112-
"mosaicml": list(MOSAICML_MODELS.keys()),
1147+
"mosaicml": list(MOSAICML_MODELS.keys()), # Only MPT models remain here
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"replit": list(REPLIT_MODELS.keys()),
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"minimax": list(MINIMAX_MODELS.keys()),
11151150
"aleph_alpha": list(ALEPH_ALPHA_MODELS.keys()),
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11451180
pricing = {
11461181
"gpt-4": {"input": 0.03, "output": 0.06},
11471182
"gpt-4-32k": {"input": 0.06, "output": 0.12},
1183+
"dbrx-instruct": {"input": 0.001, "output": 0.002},
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"dbrx-base": {"input": 0.001, "output": 0.002},
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"dolly-v2-12b": {"input": 0.001, "output": 0.002},
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"dolly-v2-7b": {"input": 0.001, "output": 0.002},
1187+
"dolly-v2-3b": {"input": 0.001, "output": 0.002},
1188+
"voyage-2": {"input": 0.0001, "output": 0.0001},
1189+
"voyage-large-2": {"input": 0.0001, "output": 0.0001},
1190+
"voyage-code-2": {"input": 0.0001, "output": 0.0001},
1191+
"voyage-finance-2": {"input": 0.0001, "output": 0.0001},
1192+
"voyage-law-2": {"input": 0.0001, "output": 0.0001},
1193+
"voyage-multilingual-2": {"input": 0.0001, "output": 0.0001},
11481194
"gpt-4-turbo": {"input": 0.01, "output": 0.03},
11491195
"gpt-4-turbo-2024-04-09": {"input": 0.01, "output": 0.03},
11501196
"gpt-4o": {"input": 0.005, "output": 0.015},

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