---

license: apache-2.0

base_model: ajvikram/toolcall-2b

tags:

- gguf

- function-calling

- tool-use

- agents

- llama.cpp

language:

- en

---

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.