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Model: ermiaazarkhalili/LFM2.5-350M-Function-Calling-xLAM-Unsloth-GGUF
Source: Original Platform

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+lfm2.5-350m-function-calling-xlam-unsloth.q2_k.gguf filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+base_model: ermiaazarkhalili/LFM2.5-350M-Function-Calling-xLAM-Unsloth
+tags:
+- gguf
+- llama.cpp
+- unsloth
+- lfm2.5
+- function-calling
+- quantized
+license: apache-2.0
+language:
+- en
+datasets:
+- Salesforce/xlam-function-calling-60k
+pipeline_tag: text-generation
+---
+
+# LFM2.5-350M-Function-Calling-xLAM-Unsloth — GGUF quantized
+
+GGUF quantizations of [`ermiaazarkhalili/LFM2.5-350M-Function-Calling-xLAM-Unsloth`](https://huggingface.co/ermiaazarkhalili/LFM2.5-350M-Function-Calling-xLAM-Unsloth),
+produced via [Unsloth](https://github.com/unslothai/unsloth) + llama.cpp's conversion scripts.
+
+| Field | Value |
+|---|---|
+| **Source checkpoint** | [`ermiaazarkhalili/LFM2.5-350M-Function-Calling-xLAM-Unsloth`](https://huggingface.co/ermiaazarkhalili/LFM2.5-350M-Function-Calling-xLAM-Unsloth) |
+| **Base model** | [`LiquidAI/LFM2.5-350M`](https://huggingface.co/LiquidAI/LFM2.5-350M) |
+| **Dataset** | [`Salesforce/xlam-function-calling-60k`](https://huggingface.co/datasets/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
+
+```bash
+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
+
+```bash
+ollama run hf.co/ermiaazarkhalili/LFM2.5-350M-Function-Calling-xLAM-Unsloth-GGUF:Q4_K_M
+```
+
+### llama-cpp-python
+
+```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`](https://huggingface.co/ermiaazarkhalili/LFM2.5-350M-Function-Calling-xLAM-Unsloth)).
+
+## Citation
+
+```bibtex
+@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}}
+}
+```
+
+---
+
+This lfm2.5 model was trained 2× faster with [Unsloth](https://github.com/unslothai/unsloth) and Hugging Face's TRL library.
+
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

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