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@@ -37,15 +38,15 @@ You can check your available credits running:
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uv run python -m tests.credits
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```
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###OpenRouter Endpoints
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## OpenRouter Endpoints
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You can obtain a list of all models, and a separate list of all free models supported by OpenRouter by running:
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You can obtain a list of all models, and a separate list of all *free* models supported by OpenRouter by running:
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```bash
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uv run python -m tests.models
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```
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This will generate two `json` files in a data folder within the root directory.
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This will generate two `json` files in a `data` folder within the root directory.
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To trim this list down to models that work correctly, run:
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```bash
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uv run python -m tests.chat_completion --mode default
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```
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When running the `default` mode, a set of predefined prompts will be used. In the `interactive` mode you can manually enter new `user` prompts, with any previous model responses being kept as `assistant` responses.
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When running the `default` mode, a set of predefined prompts will be used.
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In the `interactive` mode you can manually enter new `user` prompts, with any previous model responses being kept as `assistant` responses.
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## Consensus Learning
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The input parameters for running a single-node multi-model instance of Consensus Learning through OpenRouter are specified within `src/input.json`.
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The current implementation of consensus learning can be described as follows:
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To run the algorithm use:
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1. An initial prompt is send to a set of `N` LLMs.
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2. The outputs provided by the `N` LLMs is then sent to a centralized aggregator, which is another LLM with additional context.
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3. The centralized LLM aggregates all responses into a single one, which is fed back to the `N` selected models.
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4. The process repeats for a number of iterations, which is a configurable parameter, with the last aggregation serving as the final response.
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The input parameters for running a single-node multi-model instance of Consensus Learning through OpenRouter are specified within `src/input.json`, as follows:
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* Select the `models` by specifying the OpenRouter `id`, as well as some additional LLM-specific parameters such as `max_tokens` and `temperature`.
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* Provide an `initial_conversation` for the LLMs, which could include for instance a `system` message and a `user` query prompt.
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* Specify the aggregator `model`, additional context `aggregator_context` for the aggregator, as well as a prompt `aggregator_prompt` for how to perform the aggregation.
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* Aggregated prompts are resent to models with a role assigned by the `aggregated_prompt_type`.
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* This aggregated prompt is sent together with an additional `improvement_prompt` (with predefined `user` role).
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* Finally, specify the number of `iterations`.
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Once the `src/input.json` file is set, to run the algorithm use:
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```bash
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uv run python -m src.main
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```
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## References and Potential Directions
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* Mixture-of-Agents (MoA) [github reposiroty](https://github.com/togethercomputer/MoA) and the original paper [arXiv:2406.04692](https://arxiv.org/abs/2406.04692): iterative aggregation of model responses.
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There are various ways in which the current implementation could be improved.
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* Mixture-of-Agents (MoA) [github repository](https://github.com/togethercomputer/MoA) and the original paper [arXiv:2406.04692](https://arxiv.org/abs/2406.04692): iterative aggregation of model responses.
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* Chain of Thought prompting techniques: a linear problem solving approach where each step builds upon the previous one.
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* LLM-Blender introduced in [arXiv:2306.02561](https://arxiv.org/abs/2306.02561): its PairRanker achieves a reranking of outputs by performing pairwise comparisons through a *cross-attention encoder* to select the best one.
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* LLM-Blender introduced in [arXiv:2306.02561](https://arxiv.org/abs/2306.02561): its PairRanker achieves a re-ranking of outputs by performing pairwise comparisons through a *cross-attention encoder* to select the best one.
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The best candidates are then concatenated with the initial input.
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* Centralized router: another possibility is to train a *router* that predicts the best-performing model from a fixed set of LLMs, for a given input.
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See *e.g.*[arXiv:2311.08692](https://arxiv.org/abs/2311.08692), [arXiv:2309:15789](https://arxiv.org/pdf/2309.15789)
"content": "You are an expert on urban infrastructure."
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},
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{
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"role": "user",
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"content": "Propose a very simple strategy for reducing urban traffic congestion using technology and policy interventions."
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}
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],
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"iteration_0": {
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"qwen/qwen-vl-plus:free": "One simple strategy for reducing urban traffic congestion using technology and policy interventions is to implement a congestion pricing system. This involves charging drivers a fee to enter certain areas of the city during peak hours, such as downtown or the central business district. The fee would be based on the time of day and the type of vehicle, with higher fees for larger and more polluting vehicles. This would encourage drivers to use public transportation, carpool, or take other forms of transportation that are less likely to contribute to traffic congestion and air pollution. Additionally, the revenue generated from the congestion fees could be used to fund improvements to public transportation systems, such as expanding bus routes or building new subway lines. This would further encourage people to use public transportation and reduce the number of cars on the road.",
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"sophosympatheia/rogue-rose-103b-v0.2:free": " Title: Smart Flow: A Comprehensive Approach to Reducing Urban Traffic Congestion\n\nObjective: To reduce urban traffic congestion by optimizing traffic flow, promoting alternative modes of transportation, and incentivizing off-peak travel through a combination of technology and policy interventions.\n\nStrategy:\n\n1. Implement Smart Traffic Management System (STMS)\na. Install smart traffic signals equipped with sensors and cameras to monitor and adapt to real-time traffic conditions.\nb. Develop a centralized traffic management center to monitor and control the STMS.\nc. Integrate the STMS with existing navigation apps to provide real-time traffic updates and alternative route suggestions.\n2. Encourage Public Transportation and Active Modes of Commuting\na. Incentivize the use of public transit by partnering with private companies to offer discounted or subsid",
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"meta-llama/llama-3.2-3b-instruct:free": "Here's a simple strategy for reducing urban traffic congestion using technology and policy interventions:\n\n**Strategy: \"Smart Traffic Signal Optimization and Congestion Pricing\"**\n\n**Objective:** Reduce urban traffic congestion by optimizing traffic signal timing and implementing congestion pricing to discourage non-essential travel during peak hours.\n\n**Key Interventions:**\n\n1. **Smart Traffic Signal Optimization:**\n\t* Install intelligent transportation systems (ITS) that use real-time traffic data to optimize traffic signal timing.\n\t* Use algorithms to adjust signal timings to minimize congestion, prioritize public transportation, and promote pedestrian and cyclist safety.\n2. **Congestion Pricing:**\n\t* Implement a dynamic pricing system that charges drivers a fee to enter certain areas or use specific roads during peak hours (e.g., rush hour, special events).\n\t* Set a base price for a standard vehicle and adjust it according to the time of day and traffic conditions.\n3. **Real-time Information and Feedback:**\n\t* Provide real-time traffic updates and congestion information"
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},
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"aggregate_0": "Reducing urban traffic congestion is a complex issue that requires a multi-faceted approach. Implementing a congestion pricing system is a promising strategy that involves charging drivers a fee to enter certain areas of the city during peak hours. This fee can be based on the time of day and the type of vehicle, with higher fees for larger and more polluting vehicles. The goal is to encourage drivers to use public transportation, carpool, or take other forms of transportation that are less likely to contribute to traffic congestion and air pollution.\n\nHowever, congestion pricing alone may not be sufficient to address the issue. A comprehensive approach should also include optimizing traffic signal timing through the use of intelligent transportation systems (ITS). These systems can analyze real-time traffic data to adjust signal timings, minimizing congestion, prioritizing public transportation, and promoting pedestrian and cyclist safety.\n\nIn addition to these technical interventions, policymakers can also play a crucial role in reducing urban traffic congestion. For example, they can incentivize the use of public transportation by",
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"iteration_1": {
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"qwen/qwen-vl-plus:free": "A simple strategy for reducing urban traffic congestion using technology and policy interventions is to implement a congestion pricing system. This involves charging drivers a fee to enter certain areas of the city during peak hours, with higher fees for larger and more polluting vehicles. The goal is to encourage drivers to use public transportation, carpool, or take other forms of transportation that are less likely to contribute to traffic congestion and air pollution. Additionally, policymakers can incentivize the use of public transportation by providing subsidies or discounts.",
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"sophosympatheia/rogue-rose-103b-v0.2:free": " To reduce urban traffic congestion, a comprehensive approach that combines technology and policy interventions is recommended. This approach should include:\n\n1. Congestion pricing: Implement a congestion pricing system that charges drivers a fee to enter certain areas of the city during peak hours. The fee can be based on the time of day and the type of vehicle, with higher fees for larger and more polluting vehicles. This will encourage drivers to use public transportation, carpool, or take other forms of transportation that contribute less to traffic congestion and air pollution.\n2. Intelligent Transportation Systems (ITS): Optimize traffic signal timing by using ITS that analyze real-time traffic data to adjust signal timings. This will help minimize congestion, prioritize public transportation, and promote pedestrian and cyclist safety.\n3. Incentivizing public transportation: Polic",
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"meta-llama/llama-3.2-3b-instruct:free": "Here's a simple strategy for reducing urban traffic congestion using technology and policy interventions:\n\n**Strategy:** \"Smart Traffic Management\"\n\n**Components:**\n\n1. **Congestion Pricing:** Implement a congestion pricing system that charges drivers a fee to enter certain areas of the city during peak hours. This fee can be based on the time of day, vehicle type, and pollution levels.\n2. **Optimized Traffic Signal Control:** Utilize intelligent transportation systems (ITS) to analyze real-time traffic data and adjust traffic signal timings. This can minimize congestion, prioritize public transportation, and promote pedestrian and cyclist safety.\n3. **Incentivizing Alternative Modes:** Encourage the use of public transportation, carpooling, and active transportation (e.g., walking and cycling) through policy incentives, such as discounted fares, preferred parking, or tax benefits.\n4. **Real-time Information and Feedback:** Provide drivers with real-time information on traffic conditions, road closures, and alternative routes through mobile apps, digital signs,"
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},
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"aggregate_1": "To effectively reduce urban traffic congestion, a multifaceted approach that combines technology, policy interventions, and behavioral changes is recommended. This strategy involves implementing a congestion pricing system, optimizing traffic signal control, incentivizing alternative modes of transportation, and providing real-time information to drivers.\n\nCongestion pricing, a key component of smart traffic management, involves charging motorists a fee to enter certain areas of the city during peak hours. The fee can be based on the time of day, vehicle type, and pollution levels to discourage larger and more polluting vehicles from entering congested areas. This not only reduces traffic congestion but also provides a revenue stream for the city to invest in public transportation and infrastructure.\n\nOptimized traffic signal control is another crucial aspect of reducing congestion. Intelligent transportation systems (ITS) can analyze real-time traffic data to adjust traffic signal timings, minimizing congestion, prioritizing public transportation, and promoting pedestrian and cyclist safety. By optimizing signal timing, cities can reduce travel times, lower emissions, and improve air quality",
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"iteration_2": {
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"qwen/qwen-vl-plus:free": "To effectively reduce urban traffic congestion, a multifaceted approach that combines technology, policy interventions, and behavioral changes is recommended. This strategy involves implementing a congestion pricing system, optimizing traffic signal control, incentivizing alternative modes of transportation, and providing real-time information to drivers.",
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"sophosympatheia/rogue-rose-103b-v0.2:free": " Based on the aggregated responses, a simple strategy for reducing urban traffic congestion using technology and policy interventions would be:\n\n1. Implement dynamic pricing for on-street parking: Utilize smart parking meters that use real-time data to adjust parking rates based on demand. During peak hours, parking rates can be increased to discourage non-essential trips and encourage carpooling or use of public transportation. The revenue generated from parking fees can be invested in improving public transportation and infrastructure.\n2. Introduce a congestion tax for high-emission vehicles: Implement a congestion tax for vehicles with higher emissions, such as older and larger vehicles, during peak hours. This will discourage the use of these vehicles during congested times and incentivize the use of low-emission or electric vehicles, carpooling, or public",
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"meta-llama/llama-3.2-3b-instruct:free": "Here's a simple strategy for reducing urban traffic congestion using technology and policy interventions:\n\n**Strategy: \"Smart Traffic Optimization\"**\n\n1. **Congestion Pricing**: Implement a congestion pricing system that charges motorists a fee to enter certain areas of the city during peak hours, based on time of day, vehicle type, and pollution levels.\n2. **Optimized Traffic Signal Control**: Implement intelligent transportation systems (ITS) to analyze real-time traffic data and adjust traffic signal timings to minimize congestion, prioritize public transportation, and promote pedestrian and cyclist safety.\n3. **Alternative Mode Incentives**: Offer incentives to encourage the use of alternative modes of transportation, such as public transportation, carpooling, and cycling, through discounted fares, preferential lanes, or dedicated bike paths.\n4. **Real-time Information**: Provide real-time traffic updates and information to drivers through mobile apps, digital signage, and social media, helping them navigate the city more efficiently and make informed decisions about when to travel.\n5. **"
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},
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"aggregate_2": "To effectively reduce urban traffic congestion, a multifaceted approach that combines technology, policy interventions, and behavioral changes is recommended. This strategy involves implementing a congestion pricing system, optimizing traffic signal control, incentivizing alternative modes of transportation, and providing real-time information to drivers.\n\nOne effective way to implement congestion pricing is by using dynamic pricing systems that adjust parking rates based on demand. This can be achieved through the use of smart parking meters that utilize real-time data to adjust parking rates. During peak hours, parking rates can be increased to discourage non-essential trips and encourage carpooling or use of public transportation. The revenue generated from parking fees can be invested in improving public transportation and infrastructure.\n\nAnother strategy is to introduce a congestion tax for high-emission vehicles during peak hours. This will discourage the use of these vehicles during congested times and incentivize the use of low-emission or electric vehicles, carpooling, or public transportation. This approach not only reduces congestion but also promotes environmental sustainability.\n\n"
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