base_model, tags, license, language, datasets, pipeline_tag

base_model	tags	license	language	datasets	pipeline_tag
ermiaazarkhalili/LFM2.5-350M-Function-Calling-xLAM-Unsloth	gguf llama.cpp unsloth lfm2.5 function-calling quantized	apache-2.0	en	Salesforce/xlam-function-calling-60k	text-generation

LFM2.5-350M-Function-Calling-xLAM-Unsloth — GGUF quantized

GGUF quantizations of ermiaazarkhalili/LFM2.5-350M-Function-Calling-xLAM-Unsloth, produced via Unsloth + llama.cpp's conversion scripts.

Field	Value
Source checkpoint	ermiaazarkhalili/LFM2.5-350M-Function-Calling-xLAM-Unsloth
Base model	LiquidAI/LFM2.5-350M
Dataset	Salesforce/xlam-function-calling-60k
Training	1 full epoch (effective batch=8)
Final training loss	0.6507 (job 36550863, runtime 46m 11s, peak VRAM 5.73 GB)
Conversion	Unsloth save_pretrained → llama.cpp convert_hf_to_gguf.py → llama-quantize

Available quantizations

File	Bits	Size	Notes
lfm2.5-350m-function-calling-xlam-unsloth.q2_k.gguf	2-bit	0.16 GB	Smallest; aggressive quality loss
lfm2.5-350m-function-calling-xlam-unsloth.q3_k_m.gguf	3-bit	0.19 GB	Small; noticeable quality loss
lfm2.5-350m-function-calling-xlam-unsloth.q4_k_m.gguf	4-bit	0.23 GB	Recommended — best size/quality balance
lfm2.5-350m-function-calling-xlam-unsloth.q5_k_m.gguf	5-bit	0.26 GB	Near-full quality
lfm2.5-350m-function-calling-xlam-unsloth.q6_k.gguf	6-bit	0.29 GB	Very close to Q8_0 at smaller size
lfm2.5-350m-function-calling-xlam-unsloth.q8_0.gguf	8-bit	0.38 GB	Largest; closest to bf16 source

Recommended default: Q4_K_M. For maximum fidelity, use Q8_0.

Usage

llama.cpp

llama-cli -hf ermiaazarkhalili/LFM2.5-350M-Function-Calling-xLAM-Unsloth-GGUF --jinja -p "Find flights from SFO to NYC on December 25th" -n 256

Ollama

ollama run hf.co/ermiaazarkhalili/LFM2.5-350M-Function-Calling-xLAM-Unsloth-GGUF:Q4_K_M

llama-cpp-python

from llama_cpp import Llama
llm = Llama.from_pretrained(
    repo_id="ermiaazarkhalili/LFM2.5-350M-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. Outputs should be independently verified before any downstream use.

Limitations

• GGUF quantizations have unavoidable quality loss vs the bf16 source. Use Q5_K_M+ for best fidelity.
• Inherits all limitations of the source merged checkpoint (ermiaazarkhalili/LFM2.5-350M-Function-Calling-xLAM-Unsloth).

Citation

@misc{ lfm25_350m_xlam_unsloth_2026_gguf ,
  author = {Ermia Azarkhalili},
  title = { LFM2.5-350M-Function-Calling-xLAM-Unsloth — GGUF quantized },
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/ermiaazarkhalili/LFM2.5-350M-Function-Calling-xLAM-Unsloth-GGUF}}
}

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This lfm2.5 model was trained 2× faster with Unsloth and Hugging Face's TRL library.