Instructions to use AkachukwuE/conifer-tool-helper with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use AkachukwuE/conifer-tool-helper 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 AkachukwuE/conifer-tool-helper:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AkachukwuE/conifer-tool-helper:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AkachukwuE/conifer-tool-helper:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AkachukwuE/conifer-tool-helper: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 AkachukwuE/conifer-tool-helper:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AkachukwuE/conifer-tool-helper: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 AkachukwuE/conifer-tool-helper:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AkachukwuE/conifer-tool-helper:Q4_K_M

Use Docker

docker model run hf.co/AkachukwuE/conifer-tool-helper:Q4_K_M

LM Studio
Jan

vLLM

How to use AkachukwuE/conifer-tool-helper with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/AkachukwuE/conifer-tool-helper:Q4_K_M

Ollama
How to use AkachukwuE/conifer-tool-helper with Ollama:
```
ollama run hf.co/AkachukwuE/conifer-tool-helper:Q4_K_M
```

Unsloth Studio

How to use AkachukwuE/conifer-tool-helper 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 AkachukwuE/conifer-tool-helper 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 AkachukwuE/conifer-tool-helper to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AkachukwuE/conifer-tool-helper to start chatting

How to use AkachukwuE/conifer-tool-helper with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AkachukwuE/conifer-tool-helper:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "AkachukwuE/conifer-tool-helper:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use AkachukwuE/conifer-tool-helper with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AkachukwuE/conifer-tool-helper: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 "AkachukwuE/conifer-tool-helper: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"

Docker Model Runner
How to use AkachukwuE/conifer-tool-helper with Docker Model Runner:
```
docker model run hf.co/AkachukwuE/conifer-tool-helper:Q4_K_M
```

Lemonade

How to use AkachukwuE/conifer-tool-helper with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AkachukwuE/conifer-tool-helper:Q4_K_M

Run and chat with the model

lemonade run user.conifer-tool-helper-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use AkachukwuE/conifer-tool-helper with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AkachukwuE/conifer-tool-helper: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 AkachukwuE/conifer-tool-helper:Q4_K_M

Run Hermes

hermes

Atomic Chat

Conifer Tool Helper

An on-device tool-selection and argument-construction helper for Conifer. This v7 release is a merged LoRA r64 fine-tune of LFM2.5-1.2B-Instruct, exported as Q4_K_M for the engine's native LFM2 function-call dialect.

The model consumes the current turn, a four-exchange tail window, a bounded context brief, a mechanical call ledger, and a narrowed tool catalog. It emits Pythonic LFM2 call lists and may abstain so the main model can continue.

Verification

Held-out: 99.0% correct-tool, 0.83% false-call, 100% schema-valid.
BFCL v3: 94.7% correct-tool, 82.5% AST match on unseen tools.
Long-context no-call classes: 0% false-call across measured context modes.
Local M5/Metal smoke: single and two-call generations parsed successfully.

Artifact SHA-256: 6e90b5aeb0f966db07e454a1fb46372b577416edb61802a7d30fc95db97cb3c5

Use only with granted tool schemas and validate every emitted call before execution. The runtime caps a fan-out at eight and falls back to the main model on helper absence, validation failure, low confidence, or memory pressure.

Downloads last month: 46

GGUF

Model size

1B params

Architecture

lfm2

Hardware compatibility

4-bit

Model tree for AkachukwuE/conifer-tool-helper

Base model

LiquidAI/LFM2.5-1.2B-Base

Finetuned

LiquidAI/LFM2.5-1.2B-Instruct

Quantized

(74)

this model