metadata

library_name: transformers
tags:
  - unsloth
license: apache-2.0
datasets:
  - nvidia/Llama-Nemotron-Post-Training-Dataset
base_model:
  - unsloth/phi-4
  - deepseek-ai/DeepSeek-R1
pipeline_tag: text-generation

Model Card for Model ID

Model Details

Model Description

Phi-4 trained on reasoning outputs on complex logic, math and coding challenges derived from nvidia/Llama-Nemotron-Post-Training-Dataset filtered to include high length reasoning answers generated by DeepSeek R1. The training was on 10,000 samples done on an RTX 5090 (yes managed to make unsloth work on a 5090) with context length of 16384 and took around 10 hours using unsloth 4-bit quants and transfomers SFT Trainer. You do not need to add a system prompt but it can help in some use cases. The model will automatically go into thinking mode when presented with complex tasks.

Recommended Settings of temperature = 1.5 (you can test with 1 to 1.5) , min_p = 0.1, repeat penalty 1.2 or 1.3 to mitigate extremely long reasoning around the same concept.

Try the following prompt or similar structured prompts containing complex connections and the model will automatically go into thinking mode and generate long reasoning chains akin to DeepSeek.

Prompt:

This prompt was generated using Claude 3.7 Sonnet and not included in the train or test dataset, use similarly structred prompts and see the magic!

Network Packet Routing Optimization Challenge

You're designing a system to optimize packet routing in a network with multiple possible paths. The network consists of nodes connected by bidirectional links, each with different bandwidth capacities and latency values.

Your task is to find the most efficient routing path between a given source and destination node that satisfies specific constraints on bandwidth, latency, and hop count.

Input Specification

The first line contains four space-separated integers: n, m, b_min, and l_max (2 ≤ n ≤ 100, 1 ≤ m ≤ 5000, 1 ≤ b_min ≤ 1000, 1 ≤ l_max ≤ 10000)

n: number of nodes in the network (numbered from 1 to n)
m: number of links between nodes
b_min: minimum required bandwidth for the path
l_max: maximum allowed total latency for the path

The next m lines each contain four integers u, v, b, l (1 ≤ u, v ≤ n, u ≠ v, 1 ≤ b ≤ 1000, 1 ≤ l ≤ 1000):

u, v: nodes connected by this link
b: bandwidth capacity of the link
l: latency of the link

The last line contains two integers s and t (1 ≤ s, t ≤ n, s ≠ t) - the source and destination nodes.

Constraints and Notes

The bandwidth of a path is the minimum bandwidth among all links in the path
The latency of a path is the sum of latencies of all links in the path
A valid path must have bandwidth ≥ b_min and latency ≤ l_max
Among all valid paths, you must choose the one with the highest bandwidth
If there are multiple paths with the same highest bandwidth, choose the one with the lowest latency
If there are still multiple paths, choose the one with the fewest hops (links)
If no valid path exists, output "NO PATH"

Output

If a valid path exists, the first line should contain three space-separated integers: the bandwidth of the chosen path, the total latency of the chosen path, and the number of hops.

The second line should contain the sequence of nodes in the path, starting with s and ending with t.

If no valid path exists, output "NO PATH" (without quotes).

Examples

Example 1:

Output:

70 60 3
1 2 3 5

Example 2:

Output:

90 40 2
1 3 4

Example 3:

3 3 100 100
1 2 150 40
2 3 180 70
1 3 120 30
1 3

Output:

120 30 1
1 3

Your solution should efficiently find the optimal path that satisfies all constraints, handling potentially complex network topologies with multiple possible routes between source and destination.

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**Finetuned from model [optional]:**unsloth/phi-4

Uses

Complex reasoning requiring challenging thinking and coding (mostly python).

Direct Use

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

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Training Procedure

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Evaluation

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Summary

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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