---
base_model: ermiaazarkhalili/Qwen3-8B-Function-Calling-xLAM-Unsloth
tags:
- gguf
- llama.cpp
- unsloth
- qwen3
- function-calling
- quantized
license: apache-2.0
language:
- en
datasets:
- Salesforce/xlam-function-calling-60k
pipeline_tag: text-generation
---

# Qwen3-8B-xLAM-Unsloth — GGUF quantized

GGUF quantizations of [`ermiaazarkhalili/Qwen3-8B-Function-Calling-xLAM-Unsloth`](https://huggingface.co/ermiaazarkhalili/Qwen3-8B-Function-Calling-xLAM-Unsloth),
produced via [Unsloth](https://github.com/unslothai/unsloth) + llama.cpp's conversion scripts.

| Field | Value |
|---|---|
| **Source checkpoint** | [`ermiaazarkhalili/Qwen3-8B-Function-Calling-xLAM-Unsloth`](https://huggingface.co/ermiaazarkhalili/Qwen3-8B-Function-Calling-xLAM-Unsloth) |
| **Base model** | [`unsloth/qwen3-8b-unsloth-bnb-4bit`](https://huggingface.co/unsloth/qwen3-8b-unsloth-bnb-4bit) |
| **Dataset** | [`Salesforce/xlam-function-calling-60k`](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k) |
| **Training** | 1 full epoch (effective batch=8 via per_device=1 × grad_accum=8) |
| **Final training loss** | 0.219 (job 36885898, runtime 3h 48m on H100 MIG 3g.40gb) |
| **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 | Size | Notes |
|---|---|---|---|
| `qwen3-8b-function-calling-xlam-unsloth.q2_k.gguf` | 2-bit | 3.28 GB | Smallest; aggressive quality loss |
| `qwen3-8b-function-calling-xlam-unsloth.q3_k_m.gguf` | 3-bit | 4.12 GB | Small; noticeable quality loss |
| `qwen3-8b-function-calling-xlam-unsloth.q4_k_m.gguf` | 4-bit | 5.03 GB | **Recommended** — best size/quality balance |
| `qwen3-8b-function-calling-xlam-unsloth.q5_k_m.gguf` | 5-bit | 5.85 GB | Near-full quality |
| `qwen3-8b-function-calling-xlam-unsloth.q6_k.gguf` | 6-bit | 6.73 GB | Very close to Q8_0 at smaller size |
| `qwen3-8b-function-calling-xlam-unsloth.q8_0.gguf` | 8-bit | 8.71 GB | 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/Qwen3-8B-Function-Calling-xLAM-Unsloth-GGUF --jinja -p "Find flights from SFO to NYC on December 25th" -n 256

# Interactive chat
llama-cli -hf ermiaazarkhalili/Qwen3-8B-Function-Calling-xLAM-Unsloth-GGUF --jinja -cnv
```

### Ollama

```bash
ollama run hf.co/ermiaazarkhalili/Qwen3-8B-Function-Calling-xLAM-Unsloth-GGUF:Q4_K_M
```

### llama-cpp-python

```python
from llama_cpp import Llama
llm = Llama.from_pretrained(
    repo_id="ermiaazarkhalili/Qwen3-8B-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/Qwen3-8B-Function-Calling-xLAM-Unsloth`](https://huggingface.co/ermiaazarkhalili/Qwen3-8B-Function-Calling-xLAM-Unsloth)).
- Limited to the function schemas covered in the xLAM-60K training dataset; performance on novel APIs may degrade.

## Citation

```bibtex
@misc{ qwen3_8b_xlam_unsloth_2026_gguf ,
  author = {Ermia Azarkhalili},
  title = { Qwen3-8B-xLAM-Unsloth — GGUF quantized },
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/ermiaazarkhalili/Qwen3-8B-Function-Calling-xLAM-Unsloth-GGUF}}
}
```

---

This qwen3 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)