Instructions to use mervinpraison/Phi-4-harupfall-axis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mervinpraison/Phi-4-harupfall-axis with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mervinpraison/Phi-4-harupfall-axis", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mervinpraison/Phi-4-harupfall-axis with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mervinpraison/Phi-4-harupfall-axis:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mervinpraison/Phi-4-harupfall-axis:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mervinpraison/Phi-4-harupfall-axis:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mervinpraison/Phi-4-harupfall-axis:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf mervinpraison/Phi-4-harupfall-axis:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mervinpraison/Phi-4-harupfall-axis:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf mervinpraison/Phi-4-harupfall-axis:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mervinpraison/Phi-4-harupfall-axis:Q4_K_M

Use Docker

docker model run hf.co/mervinpraison/Phi-4-harupfall-axis:Q4_K_M

LM Studio
Jan
Ollama
How to use mervinpraison/Phi-4-harupfall-axis with Ollama:
```
ollama run hf.co/mervinpraison/Phi-4-harupfall-axis:Q4_K_M
```
Unsloth Desktop

How to use mervinpraison/Phi-4-harupfall-axis with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mervinpraison/Phi-4-harupfall-axis:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "mervinpraison/Phi-4-harupfall-axis:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use mervinpraison/Phi-4-harupfall-axis with Docker Model Runner:
```
docker model run hf.co/mervinpraison/Phi-4-harupfall-axis:Q4_K_M
```

Lemonade

How to use mervinpraison/Phi-4-harupfall-axis with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mervinpraison/Phi-4-harupfall-axis:Q4_K_M

Run and chat with the model

lemonade run user.Phi-4-harupfall-axis-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use mervinpraison/Phi-4-harupfall-axis with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mervinpraison/Phi-4-harupfall-axis:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default mervinpraison/Phi-4-harupfall-axis:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mervinpraison/Phi-4-harupfall-axis with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mervinpraison/Phi-4-harupfall-axis:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "mervinpraison/Phi-4-harupfall-axis:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Uploaded model

Developed by: mervinpraison
Finetuned from model : unsloth/Phi-4-unsloth-bnb-4bit

dataset:
- name: mervinpraison/harup-fall-axis-alpaca
dataset_num_proc: 2
dataset_text_field: text
gradient_accumulation_steps: 2
hf_model_name: mervinpraison/Phi-4-harupfall-axis
huggingface_save: 'true'
learning_rate: 0.0001
load_in_4bit: true
loftq_config: null
logging_steps: 15
lora_alpha: 16
lora_bias: none
lora_dropout: 0
lora_r: 16
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
- gate_proj
- up_proj
- down_proj
lr_scheduler_type: linear
max_seq_length: 2048
max_steps: 6000
model_name: unsloth/Phi-4-unsloth-bnb-4bit
model_parameters: 14b
num_train_epochs: 10
ollama_model: mervinpraison/Phi-4-harupfall-axis
ollama_save: 'true'
optim: lion_8bit
output_dir: outputs
packing: false
per_device_train_batch_size: 1
quantization_method:
- q4_k_m
random_state: 3407
seed: 3407
train: 'true'
use_gradient_checkpointing: unsloth
use_rslora: false
warmup_steps: 100
weight_decay: 0.05

Training Details: wandb

Downloads last month: 9

Safetensors

Model size

15B params

Tensor type

BF16

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