README.md

---
base_model: EpistemeAI/DeepPhi-3.5-mini-instruct
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- llama-cpp
- gguf-my-repo
license: mit
language:
- en
---

# Triangle104/DeepPhi-3.5-mini-instruct-Q5_K_S-GGUF
This model was converted to GGUF format from [`EpistemeAI/DeepPhi-3.5-mini-instruct`](https://huggingface.co/EpistemeAI/DeepPhi-3.5-mini-instruct) 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/EpistemeAI/DeepPhi-3.5-mini-instruct) for more details on the model.

---
Model Summary
-



Reason Phi model for top performing model with it's size of 3.8B.
Phi-3 - synthetic data and filtered publicly available websites - with a
 focus on very high-quality, reasoning dense data. The model belongs to 
the Phi-3 model family and supports 128K token context length. 



	
		
	

Run locally
-




	
		
	

4bit
	



After obtaining the Phi-3.5-mini-instruct model checkpoint, users can use this sample code for inference.


import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, BitsAndBytesConfig

torch.random.manual_seed(0)

model_path = "EpistemeAI/DeepPhi-3.5-mini-instruct"

# Configure 4-bit quantization using bitsandbytes
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",  # You can also try "fp4" if desired.
    bnb_4bit_compute_dtype=torch.float16  # Or torch.bfloat16 depending on your hardware.
)

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    device_map="auto",
    torch_dtype=torch.float16,
    trust_remote_code=True,
    quantization_config=quantization_config,
)
tokenizer = AutoTokenizer.from_pretrained(model_path)

messages = [
    {"role": "system", "content": """
You are a helpful AI assistant. Respond in the following format:
<reasoning>
...
</reasoning>
<answer>
...
</answer>"""},
    {"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"},
    {"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."},
    {"role": "user", "content": "What about solving a 2x + 3 = 7 equation?"},
]

def format_messages(messages):
    prompt = ""
    for msg in messages:
        role = msg["role"].capitalize()
        prompt += f"{role}: {msg['content']}\n"
    return prompt.strip()

prompt = format_messages(messages)

pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
)

generation_args = {
    "max_new_tokens": 500,
    "return_full_text": False,
    "temperature": 0.0,
    "do_sample": False,
}

output = pipe(prompt, **generation_args)
print(output[0]['generated_text'])




	
		
	

Uploaded  model
-	



Developed by: EpistemeAI
License: apache-2.0
Finetuned from model : unsloth/phi-3.5-mini-instruct-bnb-4bit


This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

---
## 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 Triangle104/DeepPhi-3.5-mini-instruct-Q5_K_S-GGUF --hf-file deepphi-3.5-mini-instruct-q5_k_s.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/DeepPhi-3.5-mini-instruct-Q5_K_S-GGUF --hf-file deepphi-3.5-mini-instruct-q5_k_s.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 Triangle104/DeepPhi-3.5-mini-instruct-Q5_K_S-GGUF --hf-file deepphi-3.5-mini-instruct-q5_k_s.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/DeepPhi-3.5-mini-instruct-Q5_K_S-GGUF --hf-file deepphi-3.5-mini-instruct-q5_k_s.gguf -c 2048
```