Libraries Transformers How to use psx7/llama4B with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="psx7/llama4B")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages) # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("psx7/llama4B")
model = AutoModelForCausalLM.from_pretrained("psx7/llama4B")
messages = [
{"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) MLX How to use psx7/llama4B with MLX:
# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm
# Generate text with mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("psx7/llama4B")
prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
text = generate(model, tokenizer, prompt=prompt, verbose=True) llama-cpp-python How to use psx7/llama4B with llama-cpp-python:
# !pip install llama-cpp-python
from llama_cpp import Llama
llm = Llama.from_pretrained(
repo_id="psx7/llama4B",
filename="llama3.14B.gguf",
)
llm.create_chat_completion(
messages = [
{
"role": "user",
"content": "What is the capital of France?"
}
]
) Notebooks Google Colab Kaggle Local Apps Settings llama.cpp How to use psx7/llama4B with llama.cpp:
Install from brew brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf psx7/llama4B
# Run inference directly in the terminal:
llama-cli -hf psx7/llama4B Install from WinGet (Windows) winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf psx7/llama4B
# Run inference directly in the terminal:
llama-cli -hf psx7/llama4B 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 psx7/llama4B
# Run inference directly in the terminal:
./llama-cli -hf psx7/llama4B 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 psx7/llama4B
# Run inference directly in the terminal:
./build/bin/llama-cli -hf psx7/llama4B Use Docker docker model run hf.co/psx7/llama4B LM Studio Jan vLLM How to use psx7/llama4B with vLLM:
Install from pip and serve model # Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "psx7/llama4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "psx7/llama4B",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}' Use Docker docker model run hf.co/psx7/llama4B SGLang How to use psx7/llama4B with SGLang:
Install from pip and serve model # Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "psx7/llama4B" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "psx7/llama4B",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}' Use Docker images docker run --gpus all \
--shm-size 32g \
-p 30000:30000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HF_TOKEN=<secret>" \
--ipc=host \
lmsysorg/sglang:latest \
python3 -m sglang.launch_server \
--model-path "psx7/llama4B" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "psx7/llama4B",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}' Ollama How to use psx7/llama4B with Ollama:
ollama run hf.co/psx7/llama4B Unsloth Studio How to use psx7/llama4B with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL) curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for psx7/llama4B to start chatting Install Unsloth Studio (Windows) irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for psx7/llama4B to start chatting Using HuggingFace Spaces for Unsloth # No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for psx7/llama4B to start chatting MLX LM How to use psx7/llama4B with MLX LM:
Generate or start a chat session # Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "psx7/llama4B" Run an OpenAI-compatible server # Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "psx7/llama4B"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "psx7/llama4B",
"messages": [
{"role": "user", "content": "Hello"}
]
}' Docker Model Runner How to use psx7/llama4B with Docker Model Runner:
docker model run hf.co/psx7/llama4B Lemonade How to use psx7/llama4B with Lemonade:
Pull the model # Download Lemonade from https://lemonade-server.ai/
lemonade pull psx7/llama4B Run and chat with the model lemonade run user.llama4B-{{QUANT_TAG}} List all available models lemonade list 
