1d21f87e87
Model: ermiaazarkhalili/LFM2.5-350M-Function-Calling-xLAM-Unsloth-GGUF Source: Original Platform
101 lines
3.8 KiB
Markdown
101 lines
3.8 KiB
Markdown
---
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base_model: ermiaazarkhalili/LFM2.5-350M-Function-Calling-xLAM-Unsloth
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tags:
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- gguf
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- llama.cpp
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- unsloth
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- lfm2.5
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- function-calling
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- quantized
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license: apache-2.0
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language:
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- en
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datasets:
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- Salesforce/xlam-function-calling-60k
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pipeline_tag: text-generation
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---
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# LFM2.5-350M-Function-Calling-xLAM-Unsloth — GGUF quantized
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GGUF quantizations of [`ermiaazarkhalili/LFM2.5-350M-Function-Calling-xLAM-Unsloth`](https://huggingface.co/ermiaazarkhalili/LFM2.5-350M-Function-Calling-xLAM-Unsloth),
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produced via [Unsloth](https://github.com/unslothai/unsloth) + llama.cpp's conversion scripts.
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| Field | Value |
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|---|---|
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| **Source checkpoint** | [`ermiaazarkhalili/LFM2.5-350M-Function-Calling-xLAM-Unsloth`](https://huggingface.co/ermiaazarkhalili/LFM2.5-350M-Function-Calling-xLAM-Unsloth) |
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| **Base model** | [`LiquidAI/LFM2.5-350M`](https://huggingface.co/LiquidAI/LFM2.5-350M) |
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| **Dataset** | [`Salesforce/xlam-function-calling-60k`](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k) |
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| **Training** | 1 full epoch (effective batch=8) |
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| **Final training loss** | 0.6507 (job 36550863, runtime 46m 11s, peak VRAM 5.73 GB) |
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| **Conversion** | Unsloth `save_pretrained` → llama.cpp `convert_hf_to_gguf.py` → `llama-quantize` |
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## Available quantizations
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| File | Bits | Size | Notes |
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|---|---|---|---|
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| `lfm2.5-350m-function-calling-xlam-unsloth.q2_k.gguf` | 2-bit | 0.16 GB | Smallest; aggressive quality loss |
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| `lfm2.5-350m-function-calling-xlam-unsloth.q3_k_m.gguf` | 3-bit | 0.19 GB | Small; noticeable quality loss |
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| `lfm2.5-350m-function-calling-xlam-unsloth.q4_k_m.gguf` | 4-bit | 0.23 GB | **Recommended** — best size/quality balance |
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| `lfm2.5-350m-function-calling-xlam-unsloth.q5_k_m.gguf` | 5-bit | 0.26 GB | Near-full quality |
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| `lfm2.5-350m-function-calling-xlam-unsloth.q6_k.gguf` | 6-bit | 0.29 GB | Very close to Q8_0 at smaller size |
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| `lfm2.5-350m-function-calling-xlam-unsloth.q8_0.gguf` | 8-bit | 0.38 GB | Largest; closest to bf16 source |
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**Recommended default:** `Q4_K_M`. For maximum fidelity, use `Q8_0`.
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## Usage
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### llama.cpp
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```bash
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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
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```
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### Ollama
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```bash
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ollama run hf.co/ermiaazarkhalili/LFM2.5-350M-Function-Calling-xLAM-Unsloth-GGUF:Q4_K_M
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```
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### llama-cpp-python
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```python
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from llama_cpp import Llama
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llm = Llama.from_pretrained(
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repo_id="ermiaazarkhalili/LFM2.5-350M-Function-Calling-xLAM-Unsloth-GGUF",
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filename="*q4_k_m.gguf",
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n_ctx=2048,
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)
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out = llm.create_chat_completion(
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messages=[{"role": "user", "content": "Find flights from SFO to NYC on December 25th"}],
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max_tokens=256,
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)
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print(out["choices"][0]["message"]["content"])
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```
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## Intended use
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For research and non-commercial experimentation. Outputs should be independently verified before any downstream use.
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## Limitations
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- GGUF quantizations have unavoidable quality loss vs the bf16 source. Use Q5_K_M+ for best fidelity.
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- 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)).
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## Citation
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```bibtex
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@misc{ lfm25_350m_xlam_unsloth_2026_gguf ,
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author = {Ermia Azarkhalili},
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title = { LFM2.5-350M-Function-Calling-xLAM-Unsloth — GGUF quantized },
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year = {2026},
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publisher = {Hugging Face},
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howpublished = {\url{https://huggingface.co/ermiaazarkhalili/LFM2.5-350M-Function-Calling-xLAM-Unsloth-GGUF}}
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}
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```
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---
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This lfm2.5 model was trained 2× faster with [Unsloth](https://github.com/unslothai/unsloth) and Hugging Face's TRL library.
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[<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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