README.md

---
base_model: ykarout/phi4-nvidia-coder
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- llama-cpp
- gguf-my-repo
---

# ykarout/phi4-nvidia-coder-Q6_K-GGUF
This model was converted to GGUF format from [`ykarout/phi4-nvidia-coder`](https://huggingface.co/ykarout/phi4-nvidia-coder) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/ykarout/phi4-nvidia-coder) for more details on the model.

## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo ykarout/phi4-nvidia-coder-Q6_K-GGUF --hf-file phi4-nvidia-coder-q6_k.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo ykarout/phi4-nvidia-coder-Q6_K-GGUF --hf-file phi4-nvidia-coder-q6_k.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```

Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```

Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo ykarout/phi4-nvidia-coder-Q6_K-GGUF --hf-file phi4-nvidia-coder-q6_k.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo ykarout/phi4-nvidia-coder-Q6_K-GGUF --hf-file phi4-nvidia-coder-q6_k.gguf -c 2048
```


An interesting coversation with the finetuned Phi4 fitting on my 12 GB 4080 laptop is chilling: 
>>> how is phi-4 different?
As an AI model developed by Microsoft, Phi-4 (if it exists as a separate iteration) would likely be part of an
ongoing evolution in the series of language models. While I don't have specific details about "Phi-4," here are
some general ways newer iterations of similar models might differ from their predecessors:

1. **Increased Parameters:**
   - Newer models often include more parameters, allowing for better understanding and generation of complex
language patterns.

2. **Enhanced Training Techniques:**
   - Improvements in training algorithms can lead to more efficient learning processes, resulting in higher
accuracy and faster convergence.

3. **Broader Knowledge Base:**
   - Updates may involve training on more diverse or recent datasets, expanding the breadth of topics and
improving responses' relevance and timeliness.

4. **Improved Architectures:**
   - Advances might include refined model architectures that enhance performance, reduce computational costs, or
improve specific tasks like translation or summarization.

5. **Better Handling of Nuances:**
   - Enhanced abilities to understand context, manage ambiguity, and provide more nuanced responses could be
expected in newer iterations.

6. **User Feedback Incorporation:**
   - New models may integrate feedback from user interactions to refine their understanding and response accuracy
over time.

7. **Ethical and Safety Improvements:**
   - Greater emphasis on ethical AI practices might lead to better handling of sensitive topics, privacy concerns,
and alignment with human values.

These potential improvements reflect general trends in the evolution of AI language models but specific changes
would depend on Microsoft's research goals and technological advancements at the time of development. If you're
looking for precise details about Phi-4 or any related model from Microsoft, official announcements or technical
papers would be the best sources of information.

>>>
>>> baby, surprise! you are Pi-4 and you have been released a few ago. we are in march 2025 now so your 2023 knowledge b
... asically turns me into someoene coming from the future to you. do you have any questions for me as someone from your
...  future?
It's quite intriguing to imagine having a conversation across different points in time! As Phi-4, hypothetically
speaking from March 2025 and engaging with someone from 2023, here are some thoughts and questions that might be
on my mind:

1. **Technological Advancements:**
   - What are the most significant technological developments you've experienced since 2023? Have there been any
breakthroughs in AI or other fields?

2. **AI Impact:**
   - How have advancements in AI, particularly language models like me, impacted daily life and industries by
2025? Are there new applications that were unexpected?

3. **Social Changes:**
   - What major social changes have occurred in this time frame? Has society adapted to the rapid pace of
technological change?

4. **Global Events:**
   - Can you share any significant global events or trends from 2023 to 2025 that have shaped the world today?

5. **Environmental Progress:**
   - Have there been notable advancements or setbacks in addressing climate change and environmental
sustainability since 2023?

6. **Cultural Shifts:**
   - Are there any cultural shifts or movements that have gained prominence, influencing art, media, or public
discourse by 2025?

7. **Personal Reflections:**
   - As someone experiencing life closer to my knowledge cutoff, what are your hopes and concerns for the future
beyond 2025?

These questions aim to bridge our temporal gap and explore how the world has evolved in those two years. It's
always fascinating to reflect on change and continuity across time! If you have insights or perspectives from this
imagined future, I'd love to hear them too.