Instructions to use DeepNarra/Paham with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DeepNarra/Paham with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="DeepNarra/Paham")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("DeepNarra/Paham", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use DeepNarra/Paham 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 DeepNarra/Paham:TQ1_0
# Run inference directly in the terminal:
llama cli -hf DeepNarra/Paham:TQ1_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf DeepNarra/Paham:TQ1_0
# Run inference directly in the terminal:
llama cli -hf DeepNarra/Paham:TQ1_0

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 DeepNarra/Paham:TQ1_0
# Run inference directly in the terminal:
./llama-cli -hf DeepNarra/Paham:TQ1_0

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 DeepNarra/Paham:TQ1_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DeepNarra/Paham:TQ1_0

Use Docker

docker model run hf.co/DeepNarra/Paham:TQ1_0

Llama new
LM Studio
Jan

vLLM

How to use DeepNarra/Paham with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DeepNarra/Paham"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DeepNarra/Paham",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DeepNarra/Paham:TQ1_0

SGLang

How to use DeepNarra/Paham 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 "DeepNarra/Paham" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DeepNarra/Paham",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "DeepNarra/Paham" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DeepNarra/Paham",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use DeepNarra/Paham with Ollama:
```
ollama run hf.co/DeepNarra/Paham:TQ1_0
```
Unsloth Desktop
Docker Model Runner
How to use DeepNarra/Paham with Docker Model Runner:
```
docker model run hf.co/DeepNarra/Paham:TQ1_0
```

Lemonade

How to use DeepNarra/Paham with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DeepNarra/Paham:TQ1_0

Run and chat with the model

lemonade run user.Paham-TQ1_0

List all available models

lemonade list

Atomic Chat

Paham

File size: 760 Bytes

{
  "backend": "tokenizers",
  "bos_token": "<s>",
  "eos_token": "</s>",
  "extra_special_tokens": [
    "<think>",
    "</think>",
    "\\boxed{",
    "}",
    "<tool>",
    "</tool>",
    "<call>",
    "</call>",
    "<context>",
    "</context>",
    "<agent_thought>",
    "</agent_thought>",
    "<sandbox>",
    "</sandbox>",
    "<click>",
    "</click>",
    "<key>",
    "</key>",
    "<scroll>",
    "</scroll>",
    "<touch>",
    "</touch>",
    "<image>",
    "</image>",
    "<audio>",
    "</audio>",
    "<video>",
    "</video>",
    "<box3d>",
    "</box3d>",
    "<frame>",
    "</frame>"
  ],
  "model_max_length": 1000000000000000019884624838656,
  "pad_token": "<pad>",
  "tokenizer_class": "TokenizersBackend",
  "unk_token": "<unk>"
}