Instructions to use ajvikram/toolcall-2b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ajvikram/toolcall-2b-gguf 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 ajvikram/toolcall-2b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ajvikram/toolcall-2b-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ajvikram/toolcall-2b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ajvikram/toolcall-2b-gguf: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 ajvikram/toolcall-2b-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ajvikram/toolcall-2b-gguf: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 ajvikram/toolcall-2b-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ajvikram/toolcall-2b-gguf:Q4_K_M

Use Docker

docker model run hf.co/ajvikram/toolcall-2b-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use ajvikram/toolcall-2b-gguf with Ollama:
```
ollama run hf.co/ajvikram/toolcall-2b-gguf:Q4_K_M
```
Unsloth Desktop

How to use ajvikram/toolcall-2b-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ajvikram/toolcall-2b-gguf: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": "ajvikram/toolcall-2b-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ajvikram/toolcall-2b-gguf with Docker Model Runner:
```
docker model run hf.co/ajvikram/toolcall-2b-gguf:Q4_K_M
```

Lemonade

How to use ajvikram/toolcall-2b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ajvikram/toolcall-2b-gguf:Q4_K_M

Run and chat with the model

lemonade run user.toolcall-2b-gguf-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use ajvikram/toolcall-2b-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ajvikram/toolcall-2b-gguf: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 ajvikram/toolcall-2b-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ajvikram/toolcall-2b-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ajvikram/toolcall-2b-gguf: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 "ajvikram/toolcall-2b-gguf: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"

Toolcall-2B — GGUF

Quantized builds of ajvikram/toolcall-2b, a 2B function-calling model fine-tuned from Qwen3.5-2B for local agent tool routing. Full results, training details and limitations are on the parent model's card.

File	Size	Use
`toolcall-2b-Q4_K_M.gguf`	1.22 GB	Default. Smallest sensible quality loss, runs on a laptop CPU.
`toolcall-2b-Q5_K_M.gguf`	1.35 GB	A little closer to full precision for modest extra memory.
`toolcall-2b-Q8_0.gguf`	1.93 GB	Near-lossless; use when you have the memory.
`toolcall-2b-f16.gguf`	3.63 GB	Unquantized source for making your own quants.

Measured on the benchmark harness (safetensors, bf16): 36.35 overall on BFCL v4 against 33.85 for the Qwen3.5-2B base, with every group ahead of the base. The quantized builds are not separately scored.

Run it

llama-server -m toolcall-2b-Q4_K_M.gguf --jinja -c 8192

ollama run hf.co/ajvikram/toolcall-2b-gguf:Q4_K_M

The model uses Qwen3.5's native XML tool-call format, so any client that already parses Qwen3.5 tool calls works unchanged:

<tool_call>
<function=get_weather>
<parameter=city>
Berlin
</parameter>
</function>
</tool_call>

Verified with llama-cli on CPU: the Q4_K_M build loads, generates at roughly 33 tokens per second on an ARM CPU, and returns the call above for a get_weather tool given "What is the weather in Berlin?".

Thinking is off by default, matching how the model was trained and evaluated.

Notes

Built with llama.cpp (September 2026), which added Qwen3.5 conversion support; older builds cannot convert this architecture.
These are text-only builds. The base architecture is vision-capable, but this model was trained and evaluated purely on text tool calling.

Downloads last month: -

GGUF

Model size

2B params

Architecture

qwen35

Hardware compatibility

4-bit

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8-bit

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Model tree for ajvikram/toolcall-2b-gguf

Base model

Qwen/Qwen3.5-2B-Base

Finetuned

Qwen/Qwen3.5-2B

Finetuned

ajvikram/toolcall-2b

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(2)

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