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

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -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: 1,269 Bytes

123ee5c

{
  "_name_or_path": "DeepNarra/Paham",
  "model_name": "DeepNarra Paham 1.0 (758M-A45M)",
  "model_version": "1.0.0",
  "architectures": [
    "DeepNarraPahamForCausalLM"
  ],
  "model_type": "deepnarra_paham",
  "creator": "DeepNarra",
  "huggingface_url": "https://huggingface.co/DeepNarra/Paham",
  "torch_dtype": "float16",
  "transformers_version": "4.45.0",
  "vocab_size": 48760,
  "hidden_size": 768,
  "intermediate_size": 2048,
  "num_hidden_layers": 8,
  "num_attention_heads": 8,
  "num_key_value_heads": 4,
  "max_position_embeddings": 2097152,
  "elastic_gears": {
    "gear_1_reflex": "45M",
    "gear_2_standard": "75M",
    "gear_3_sovereign": "100M"
  },
  "measured_velocity_tok_s": 1680.7,
  "measured_latency_ms": 9.52,
  "hybrid_attention": "75% Lyapunov GLA Recurrence + 25% GLM-5 DSA IndexShare",
  "latent_phase_space_dim": 768,
  "multi_token_prediction_k": 4,
  "prompt_lookup_decoding_k": 50,
  "awake_continuum_enabled": true,
  "macro_engines": 23,
  "sub_experts_per_engine": 4,
  "total_experts": 92,
  "active_experts_per_token": "Elastic (1-3)",
  "shared_expert": true,
  "bitnet_b158_quantization": "TQ1_0",
  "total_parameters": "758.8M",
  "active_parameters": "45M (Reflex) / 100M (Deep)"
}