| --- |
| 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 |
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| 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. |
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| ## Model Details |
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| ### Model Description |
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| 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. |
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| 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. |
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| 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. |
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| #### Prompt: |
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| 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! |
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| 1. Network Packet Routing Optimization Challenge |
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| 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. |
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| 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. |
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| Input Specification |
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| 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 |
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| 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 |
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| The last line contains two integers `s` and `t` (1 ≤ s, t ≤ n, s ≠ t) - the source and destination nodes. |
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| Constraints and Notes |
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| 1. The bandwidth of a path is the minimum bandwidth among all links in the path |
| 2. The latency of a path is the sum of latencies of all links in the path |
| 3. A valid path must have bandwidth ≥ `b_min` and latency ≤ `l_max` |
| 4. Among all valid paths, you must choose the one with the highest bandwidth |
| 5. If there are multiple paths with the same highest bandwidth, choose the one with the lowest latency |
| 6. If there are still multiple paths, choose the one with the fewest hops (links) |
| 7. If no valid path exists, output "NO PATH" |
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| Output |
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| 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. |
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| The second line should contain the sequence of nodes in the path, starting with `s` and ending with `t`. |
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| If no valid path exists, output "NO PATH" (without quotes). |
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| Examples |
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| Example 1: |
| ``` |
| 5 6 50 100 |
| 1 2 100 20 |
| 2 3 80 30 |
| 3 5 70 10 |
| 1 4 60 10 |
| 4 5 90 30 |
| 1 3 50 5 |
| 1 5 |
| ``` |
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| Output: |
| ``` |
| 70 60 3 |
| 1 2 3 5 |
| ``` |
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| Example 2: |
| ``` |
| 4 5 80 50 |
| 1 2 80 20 |
| 2 3 120 15 |
| 3 4 90 10 |
| 1 3 100 30 |
| 2 4 70 25 |
| 1 4 |
| ``` |
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| Output: |
| ``` |
| 90 40 2 |
| 1 3 4 |
| ``` |
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| Example 3: |
| ``` |
| 3 3 100 100 |
| 1 2 150 40 |
| 2 3 180 70 |
| 1 3 120 30 |
| 1 3 |
| ``` |
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| Output: |
| ``` |
| 120 30 1 |
| 1 3 |
| ``` |
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| 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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| - **Developed by:** [More Information Needed] |
| - **Funded by [optional]:** [More Information Needed] |
| - **Shared by [optional]:** [More Information Needed] |
| - **Model type:** [More Information Needed] |
| - **Language(s) (NLP):** [More Information Needed] |
| - **License:** [More Information Needed] |
| - **Finetuned from model [optional]:**unsloth/phi-4 |
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| ## Uses |
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| Complex reasoning requiring challenging thinking and coding (mostly python). |
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| ### Direct Use |
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| <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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| [More Information Needed] |
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| ### Downstream Use [optional] |
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| <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
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| [More Information Needed] |
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| ### Out-of-Scope Use |
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| <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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| [More Information Needed] |
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| ## Bias, Risks, and Limitations |
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| <!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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| [More Information Needed] |
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| ### Recommendations |
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| <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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| Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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| ## How to Get Started with the Model |
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| Use the code below to get started with the model. |
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| [More Information Needed] |
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| ## Training Details |
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| ### Training Data |
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| <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
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| [More Information Needed] |
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| ### Training Procedure |
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| <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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| #### Preprocessing [optional] |
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| [More Information Needed] |
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| #### Training Hyperparameters |
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| - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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| #### Speeds, Sizes, Times [optional] |
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| <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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| [More Information Needed] |
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| ## Evaluation |
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| <!-- This section describes the evaluation protocols and provides the results. --> |
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| ### Testing Data, Factors & Metrics |
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| #### Testing Data |
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| <!-- This should link to a Dataset Card if possible. --> |
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| [More Information Needed] |
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| #### Factors |
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| <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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| [More Information Needed] |
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| #### Metrics |
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| <!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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| [More Information Needed] |
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| ### Results |
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| [More Information Needed] |
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| #### Summary |
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| ## Model Examination [optional] |
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| <!-- Relevant interpretability work for the model goes here --> |
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| [More Information Needed] |
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| ## Environmental Impact |
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| <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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| Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). |
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| - **Hardware Type:** [More Information Needed] |
| - **Hours used:** [More Information Needed] |
| - **Cloud Provider:** [More Information Needed] |
| - **Compute Region:** [More Information Needed] |
| - **Carbon Emitted:** [More Information Needed] |
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| ## Technical Specifications [optional] |
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| ### Model Architecture and Objective |
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| [More Information Needed] |
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| ### Compute Infrastructure |
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| [More Information Needed] |
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| #### Hardware |
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| [More Information Needed] |
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| #### Software |
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| [More Information Needed] |
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| ## Citation [optional] |
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| <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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| **BibTeX:** |
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| [More Information Needed] |
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| **APA:** |
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| [More Information Needed] |
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| ## Glossary [optional] |
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| <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
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| [More Information Needed] |
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| ## More Information [optional] |
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| [More Information Needed] |
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| ## Model Card Authors [optional] |
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| [More Information Needed] |
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| ## Model Card Contact |
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| [More Information Needed] |
