Instructions to use Ninnix96/Qwengram-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Ninnix96/Qwengram-4B 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 Ninnix96/Qwengram-4B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Ninnix96/Qwengram-4B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Ninnix96/Qwengram-4B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Ninnix96/Qwengram-4B: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 Ninnix96/Qwengram-4B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Ninnix96/Qwengram-4B: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 Ninnix96/Qwengram-4B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Ninnix96/Qwengram-4B:Q4_K_M

Use Docker

docker model run hf.co/Ninnix96/Qwengram-4B:Q4_K_M

LM Studio
Jan

vLLM

How to use Ninnix96/Qwengram-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ninnix96/Qwengram-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ninnix96/Qwengram-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Ninnix96/Qwengram-4B:Q4_K_M

Ollama
How to use Ninnix96/Qwengram-4B with Ollama:
```
ollama run hf.co/Ninnix96/Qwengram-4B:Q4_K_M
```
Unsloth Desktop

How to use Ninnix96/Qwengram-4B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Ninnix96/Qwengram-4B: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": "Ninnix96/Qwengram-4B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Ninnix96/Qwengram-4B with Docker Model Runner:
```
docker model run hf.co/Ninnix96/Qwengram-4B:Q4_K_M
```

Lemonade

How to use Ninnix96/Qwengram-4B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Ninnix96/Qwengram-4B:Q4_K_M

Run and chat with the model

lemonade run user.Qwengram-4B-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Ninnix96/Qwengram-4B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Ninnix96/Qwengram-4B: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 Ninnix96/Qwengram-4B:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Ninnix96/Qwengram-4B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Ninnix96/Qwengram-4B: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 "Ninnix96/Qwengram-4B: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"

Ninnix96 commited on 6 days ago

Commit

ecd44d6

verified ·

1 Parent(s): feec8ea

Add centered Qwengram-4B logo below model-card title

Browse files

Add the supplied model-size logo and refresh the README and image checksums.

Files changed (4) hide show

.gitattributes +1 -0
README.md +4 -0
SHA256.json +6 -2
qwengram_4b.png +3 -0

.gitattributes CHANGED Viewed

@@ -38,3 +38,4 @@ QwenGram-4B-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
 QwenGram-4B-Q6_K.gguf filter=lfs diff=lfs merge=lfs -text
 QwenGram-4B-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
 tokenizer.json filter=lfs diff=lfs merge=lfs -text

 QwenGram-4B-Q6_K.gguf filter=lfs diff=lfs merge=lfs -text
 QwenGram-4B-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
 tokenizer.json filter=lfs diff=lfs merge=lfs -text
+qwengram_4b.png filter=lfs diff=lfs merge=lfs -text

README.md CHANGED Viewed

@@ -10,6 +10,10 @@ tags:
 ---
 # Qwengram-4B
 Frozen Qwen3.5-4B plus an R=1 reader at decoder **IDX3/IDX11** (human layers
 4/12, 12.5%/37.5% of 32 layers), with a learned global early multiplier and
 linear token-dependent later arbitration. The external Qwen3.8-Flash-Next PLE

 ---
 # Qwengram-4B
+<p align="center">
+  <img src="./qwengram_4b.png" alt="Qwengram-4B logo" width="600">
+</p>
 Frozen Qwen3.5-4B plus an R=1 reader at decoder **IDX3/IDX11** (human layers
 4/12, 12.5%/37.5% of 32 layers), with a learned global early multiplier and
 linear token-dependent later arbitration. The external Qwen3.8-Flash-Next PLE

SHA256.json CHANGED Viewed

@@ -70,8 +70,8 @@
       "size": 4715450208
     },
     "README.md": {
-      "sha256": "74e4b6a9b5aafccb218c22aec9c40f4a0b850084110e2e48cfea4bc0600cbba3",
-      "size": 7004
     },
     "arbiter.pt": {
       "sha256": "d0e0b3b97b1d725585f322d3d559d0c83f4c07f812aa45635c72f4bb034c9746",
@@ -700,6 +700,10 @@
     "tokenizer_config.json": {
       "sha256": "316230d6a809701f4db5ea8f8fc862bc3a6f3229c937c174e674ff3ca0a64ac8",
       "size": 16710
     }
   }
 }

       "size": 4715450208
     },
     "README.md": {
+      "sha256": "d9bedd21236b5d94c6b4fe4496baf1eec550a42484c97c3d99d7a97a52a3c9fd",
+      "size": 7096
     },
     "arbiter.pt": {
       "sha256": "d0e0b3b97b1d725585f322d3d559d0c83f4c07f812aa45635c72f4bb034c9746",
     "tokenizer_config.json": {
       "sha256": "316230d6a809701f4db5ea8f8fc862bc3a6f3229c937c174e674ff3ca0a64ac8",
       "size": 16710
+    },
+    "qwengram_4b.png": {
+      "sha256": "c338f8b5ce2d4fe77ea7865813a5b0332a67fd4a587767011cbc2059641bdea9",
+      "size": 865383
     }
   }
 }

qwengram_4b.png ADDED Viewed

Git LFS Details

SHA256: c338f8b5ce2d4fe77ea7865813a5b0332a67fd4a587767011cbc2059641bdea9
Pointer size: 131 Bytes
Size of remote file: 865 kB