Instructions to use vivek078/AutodevvLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use vivek078/AutodevvLM 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 vivek078/AutodevvLM:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf vivek078/AutodevvLM:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf vivek078/AutodevvLM:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf vivek078/AutodevvLM:Q4_K_M

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 vivek078/AutodevvLM:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf vivek078/AutodevvLM:Q4_K_M

Use Docker

docker model run hf.co/vivek078/AutodevvLM:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use vivek078/AutodevvLM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vivek078/AutodevvLM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vivek078/AutodevvLM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vivek078/AutodevvLM:Q4_K_M

Ollama
How to use vivek078/AutodevvLM with Ollama:
```
ollama run hf.co/vivek078/AutodevvLM:Q4_K_M
```
Unsloth Desktop

How to use vivek078/AutodevvLM with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vivek078/AutodevvLM: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": "vivek078/AutodevvLM:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use vivek078/AutodevvLM with Docker Model Runner:
```
docker model run hf.co/vivek078/AutodevvLM:Q4_K_M
```

Lemonade

How to use vivek078/AutodevvLM with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull vivek078/AutodevvLM:Q4_K_M

Run and chat with the model

lemonade run user.AutodevvLM-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use vivek078/AutodevvLM with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vivek078/AutodevvLM: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 vivek078/AutodevvLM:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use vivek078/AutodevvLM with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vivek078/AutodevvLM: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 "vivek078/AutodevvLM: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"

vivek078 commited on 12 days ago

Commit

d7a7ec3

verified ·

1 Parent(s): 64f9842

Upload folder using huggingface_hub

Browse files

Files changed (2) hide show

.gitattributes +0 -1
README.md +1 -1

.gitattributes CHANGED Viewed

@@ -1,3 +1,2 @@
 *.gguf filter=lfs diff=lfs merge=lfs -text
 *.safetensors filter=lfs diff=lfs merge=lfs -text
-tokenizer.json filter=lfs diff=lfs merge=lfs -text


1	*.gguf filter=lfs diff=lfs merge=lfs -text
2	*.safetensors filter=lfs diff=lfs merge=lfs -text

README.md CHANGED Viewed

@@ -28,7 +28,7 @@ AutoDevvLM is a small, code-focused language model fine-tuned from [Qwen2.5-1.5B
 | | |
 |---|---|
-| **Developed by** | `<your-name-or-org>` |
 | **Base model** | `Qwen/Qwen2.5-1.5B-Instruct` |
 | **Architecture** | Qwen2 (`Qwen2ForCausalLM`), ~1.5B parameters |
 | **Context length** | 32,768 tokens in the architecture; training used sequences up to 1,024 tokens |

 | | |
 |---|---|
+| **Developed by** | `Vivek A` |
 | **Base model** | `Qwen/Qwen2.5-1.5B-Instruct` |
 | **Architecture** | Qwen2 (`Qwen2ForCausalLM`), ~1.5B parameters |
 | **Context length** | 32,768 tokens in the architecture; training used sequences up to 1,024 tokens |