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Model: ermiaazarkhalili/LFM2.5-1.2B-Function-Calling-xLAM-Unsloth-GGUF
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---
base_model: ermiaazarkhalili/LFM2.5-1.2B-Function-Calling-xLAM-Unsloth
tags:
- gguf
- llama.cpp
- unsloth
- lfm2
- function-calling
- quantized
license: apache-2.0
language:
- en
datasets:
- Salesforce/xlam-function-calling-60k
pipeline_tag: text-generation
---
# LFM2.5-1.2B-xLAM-Unsloth — GGUF quantized
GGUF quantizations of [`ermiaazarkhalili/LFM2.5-1.2B-Function-Calling-xLAM-Unsloth`](https://huggingface.co/ermiaazarkhalili/LFM2.5-1.2B-Function-Calling-xLAM-Unsloth),
produced via [Unsloth](https://github.com/unslothai/unsloth) + llama.cpp's conversion scripts.
| Field | Value |
|---|---|
| **Source checkpoint** | [`ermiaazarkhalili/LFM2.5-1.2B-Function-Calling-xLAM-Unsloth`](https://huggingface.co/ermiaazarkhalili/LFM2.5-1.2B-Function-Calling-xLAM-Unsloth) |
| **Base model** | [`LiquidAI/LFM2.5-1.2B-Instruct`](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct) |
| **Dataset** | [`Salesforce/xlam-function-calling-60k`](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k) |
| **Training** | N=1 full epoch (7,500 steps, effective batch=8) |
| **Conversion** | Unsloth `save_pretrained_gguf` → llama.cpp GGUF |
| **Quantization tool** | llama.cpp `llama-quantize` |
## Available quantizations
| File | Size | Notes |
|---|---|---|
| `LFM2.5-1.2B-Function-Calling-xLAM-Unsloth.Q2_K.gguf` | smallest | 2-bit; extreme compression, quality loss |
| `LFM2.5-1.2B-Function-Calling-xLAM-Unsloth.Q3_K_M.gguf` | small | 3-bit; modest quality trade-off |
| `LFM2.5-1.2B-Function-Calling-xLAM-Unsloth.Q4_K_M.gguf` | recommended | 4-bit; best size/quality balance |
| `LFM2.5-1.2B-Function-Calling-xLAM-Unsloth.Q5_K_M.gguf` | balanced | 5-bit; near-full quality |
| `LFM2.5-1.2B-Function-Calling-xLAM-Unsloth.Q6_K.gguf` | high quality | 6-bit; minimal degradation |
| `LFM2.5-1.2B-Function-Calling-xLAM-Unsloth.Q8_0.gguf` | largest | 8-bit; closest to bf16 source |
**Recommended default:** `Q4_K_M` (4-bit, K-quant medium). For memory-constrained deployment, try `Q2_K` or `Q3_K_M`. For maximum fidelity, use `Q8_0`.
## Usage
### llama.cpp
```bash
# Text-only
llama-cli -hf ermiaazarkhalili/LFM2.5-1.2B-Function-Calling-xLAM-Unsloth-GGUF --jinja -p "Find flights from SFO to NYC on December 25th" -n 256
# Interactive chat
llama-cli -hf ermiaazarkhalili/LFM2.5-1.2B-Function-Calling-xLAM-Unsloth-GGUF --jinja -cnv
```
### Ollama
```bash
ollama run hf.co/ermiaazarkhalili/LFM2.5-1.2B-Function-Calling-xLAM-Unsloth-GGUF:Q4_K_M
```
### llama-cpp-python
```python
from llama_cpp import Llama
llm = Llama.from_pretrained(
repo_id="ermiaazarkhalili/LFM2.5-1.2B-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` or `Q8_0` for best fidelity.
- Inherits all limitations of the source merged checkpoint ([`ermiaazarkhalili/LFM2.5-1.2B-Function-Calling-xLAM-Unsloth`](https://huggingface.co/ermiaazarkhalili/LFM2.5-1.2B-Function-Calling-xLAM-Unsloth)).
- Limited to the 60 function schemas covered in the training dataset; performance on novel APIs may degrade.
## Citation
```bibtex
@misc{ lfm25_12b_xlam_unsloth_2026_gguf ,
author = {Ermia Azarkhalili},
title = { LFM2.5-1.2B-xLAM-Unsloth — GGUF quantized },
year = {2026},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/ermiaazarkhalili/LFM2.5-1.2B-Function-Calling-xLAM-Unsloth-GGUF}}
}
```
---
This lfm2 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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{
"architectures": [
"Lfm2ForCausalLM"
],
"block_auto_adjust_ff_dim": true,
"block_dim": 2048,
"block_ffn_dim_multiplier": 1.0,
"block_mlp_init_scale": 1.0,
"block_multiple_of": 256,
"block_norm_eps": 1e-05,
"block_out_init_scale": 1.0,
"block_use_swiglu": true,
"block_use_xavier_init": true,
"bos_token_id": 1,
"conv_L_cache": 3,
"conv_bias": false,
"conv_dim": 2048,
"conv_use_xavier_init": true,
"dtype": "bfloat16",
"eos_token_id": 7,
"full_attn_idxs": null,
"hidden_size": 2048,
"initializer_range": 0.02,
"intermediate_size": 12288,
"layer_types": [
"conv",
"conv",
"full_attention",
"conv",
"conv",
"full_attention",
"conv",
"conv",
"full_attention",
"conv",
"full_attention",
"conv",
"full_attention",
"conv",
"full_attention",
"conv"
],
"max_position_embeddings": 128000,
"model_name": "ermiaazarkhalili/LFM2.5-1.2B-Function-Calling-xLAM-Unsloth",
"model_type": "lfm2",
"norm_eps": 1e-05,
"num_attention_heads": 32,
"num_heads": 32,
"num_hidden_layers": 16,
"num_key_value_heads": 8,
"pad_token_id": 0,
"rope_parameters": {
"rope_theta": 1000000.0,
"rope_type": "default"
},
"tie_word_embeddings": true,
"transformers_version": "5.5.0",
"unsloth_version": "2026.4.6",
"use_cache": false,
"use_pos_enc": true,
"vocab_size": 65536
}