Field	Value
Source checkpoint	`ermiaazarkhalili/Gemma4-E4B-Function-Calling-xLAM-Unsloth`
Base model	`google/gemma-4-E4B-it`
Dataset	`Salesforce/xlam-function-calling-60k`
Training	N=1 full epoch (7,500 steps, effective batch=8)
Conversion	Unsloth `save_pretrained` → llama.cpp `convert_hf_to_gguf.py` → `llama-quantize`
Quantization tool	llama.cpp `llama-quantize` (cached toolchain)

Available quantizations

File	Bits	Notes
`gemma4-e4b-function-calling-xlam-unsloth.q2_k.gguf`	2-bit	Smallest; aggressive quality loss
`gemma4-e4b-function-calling-xlam-unsloth.q3_k_m.gguf`	3-bit	Small; noticeable quality loss
`gemma4-e4b-function-calling-xlam-unsloth.q4_k_m.gguf`	4-bit	Recommended — best size/quality balance
`gemma4-e4b-function-calling-xlam-unsloth.q5_k_m.gguf`	5-bit	Near-full quality
`gemma4-e4b-function-calling-xlam-unsloth.q6_k.gguf`	6-bit	Very close to Q8_0 at smaller size
`gemma4-e4b-function-calling-xlam-unsloth.q8_0.gguf`	8-bit	Largest; closest to bf16 source

Recommended default: Q4_K_M (4-bit, K-quant medium). For maximum fidelity, use Q8_0.

Usage

llama.cpp

bash

# Text-only
llama-cli -hf ermiaazarkhalili/Gemma4-E4B-Function-Calling-xLAM-Unsloth-GGUF --jinja -p "Find flights from SFO to NYC on December 25th" -n 256

# Interactive chat
llama-cli -hf ermiaazarkhalili/Gemma4-E4B-Function-Calling-xLAM-Unsloth-GGUF --jinja -cnv

Ollama

ollama run hf.co/ermiaazarkhalili/Gemma4-E4B-Function-Calling-xLAM-Unsloth-GGUF:Q4_K_M

llama-cpp-python

python

from llama_cpp import Llama
llm = Llama.from_pretrained(
    repo_id="ermiaazarkhalili/Gemma4-E4B-Function-Calling-xLAM-Unsloth-GGUF",
    filename="*q4_k_m.gguf",
    n_ctx=2048,
)
out = llm.create_chat_completion(
    messages=[{"role": "user", "content": "Find flights from SFO to NYC on December 25th"}],
    max_tokens=256,
)
print(out["choices"][0]["message"]["content"])

Intended use

For research and non-commercial experimentation only. Outputs should be independently verified before any downstream use.

Limitations

GGUF quantizations have unavoidable quality loss relative to the source bfloat16 checkpoint. Use Q5_K_M, Q6_K, or Q8_0 for best fidelity.
Inherits all limitations of the source merged checkpoint (ermiaazarkhalili/Gemma4-E4B-Function-Calling-xLAM-Unsloth).
Limited to the 60 function schemas covered in the training dataset; performance on novel APIs may degrade. Although the base model is a vision-language model (VLM), this fine-tune targets text-only function-calling; vision capabilities are untouched but unevaluated.

Citation

bibtex

@misc{ gemma4_e4b_xlam_unsloth_2026_gguf ,
  author = {Ermia Azarkhalili},
  title = { Gemma4-E4B-xLAM-Unsloth — GGUF quantized },
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/ermiaazarkhalili/Gemma4-E4B-Function-Calling-xLAM-Unsloth-GGUF}}
}

This gemma4 model was trained 2× faster with Unsloth and Hugging Face's TRL library.