Qwen3.5-2B-Function-Calling-xLAM-GGUF

Qwen3.5-2B — Function Calling (xLAM) — GGUF

GGUF quantizations of ermiaazarkhalili/Qwen3.5-2B-Function-Calling-xLAM, a function-calling fine-tune trained on Salesforce's xLAM dataset and converted with llama.cpp.

Conversion note. The published source repo stores bitsandbytes 4-bit (nf4) weights, which llama.cpp cannot convert directly. These GGUFs were therefore built from the full-precision merged checkpoint produced during training, so they reflect the model's fp16 weights rather than a re-quantized 4-bit copy.

Available quantizations

Recommended default: Q4_K_M. For maximum fidelity use Q8_0; for the smallest footprint use Q4_K_M (no lower-bit quants are published for this model).

Usage

llama.cpp

# One-shot
llama-cli -hf ermiaazarkhalili/Qwen3.5-2B-Function-Calling-xLAM-GGUF --jinja -p "Your prompt here" -n 256

# Interactive chat
llama-cli -hf ermiaazarkhalili/Qwen3.5-2B-Function-Calling-xLAM-GGUF --jinja -cnv

Ollama

ollama run hf.co/ermiaazarkhalili/Qwen3.5-2B-Function-Calling-xLAM-GGUF:Q4_K_M

llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="ermiaazarkhalili/Qwen3.5-2B-Function-Calling-xLAM-GGUF",
    filename="*q4_k_m.gguf",
    n_ctx=4096,
)
out = llm.create_chat_completion(
    messages=[{"role": "user", "content": "Your prompt here"}],
    max_tokens=256,
)
print(out["choices"][0]["message"]["content"])

Intended use

Research and non-commercial experimentation with structured function / tool calling. Prompt the model with a user request plus the available tool schemas; it emits a function call. Verify all emitted calls before executing them downstream.

Limitations

• GGUF quantizations carry unavoidable quality loss relative to the source weights; prefer Q8_0 when fidelity matters.
• Inherits every limitation of the source checkpoint (ermiaazarkhalili/Qwen3.5-2B-Function-Calling-xLAM).
• Function-calling behaviour is shaped by the 60 schema families in the training set; performance on out-of-distribution APIs may degrade.

Citation

@misc{qwen3_5_2b_gguf,
  author       = {Ermia Azarkhalili},
  title        = {Qwen3.5-2B — Function Calling (xLAM) — GGUF quantized},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/ermiaazarkhalili/Qwen3.5-2B-Function-Calling-xLAM-GGUF}}
}