---

license: apache-2.0

base_model:

- empero-ai/Qwen3.8-4B

datasets:

- Salesforce/xlam-function-calling-60k

library_name: gguf

pipeline_tag: text-generation

tags:

- gguf

- llama.cpp

- quantized

- unsloth

- lora

- trl

- sft

---

Qwen3.8-4B-Function-Calling-xLAM-Unsloth-GGUF

GGUF quantizations of a LoRA fine-tune of empero-ai/Qwen3.8-4B, supervised fine-tuned on Salesforce/xlam-function-calling-60k.

Quantized from ermiaazarkhalili/Qwen3.8-4B-Function-Calling-xLAM-Unsloth. See that repository for the full-precision weights.

| | |

| --- | --- |

| Base model | empero-ai/Qwen3.8-4B |

| Training data | Salesforce/xlam-function-calling-60k |

| Method | LoRA supervised fine-tuning via Unsloth + TRL |

| License | apache-2.0 (inherited from the base model) |

Available quantizations

| File | Size |

| --- | --- |

| qwen3.8-4b-function-calling-xlam-unsloth.q2_k.gguf | 1.96 GB |

| qwen3.8-4b-function-calling-xlam-unsloth.q3_k_m.gguf | 2.32 GB |

| qwen3.8-4b-function-calling-xlam-unsloth.q4_k_m.gguf | 2.78 GB |

| qwen3.8-4b-function-calling-xlam-unsloth.q5_k_m.gguf | 3.16 GB |

| qwen3.8-4b-function-calling-xlam-unsloth.q6_k.gguf | 3.56 GB |

| qwen3.8-4b-function-calling-xlam-unsloth.q8_0.gguf | 4.61 GB |

Usage

llama.cpp

huggingface-cli download ermiaazarkhalili/Qwen3.8-4B-Function-Calling-xLAM-Unsloth-GGUF qwen3.8-4b-function-calling-xlam-unsloth.q4_k_m.gguf --local-dir .
llama-cli -m qwen3.8-4b-function-calling-xlam-unsloth.q4_k_m.gguf -p "Explain gradient checkpointing in two sentences." -n 256

Ollama

echo 'FROM ./qwen3.8-4b-function-calling-xlam-unsloth.q4_k_m.gguf' > Modelfile
ollama create qwen3.8-4b-function-calling-xlam-unsloth-gguf -f Modelfile
ollama run qwen3.8-4b-function-calling-xlam-unsloth-gguf

Training configuration

| Setting | Value |

| --- | --- |

| LoRA rank (r) | 64 |

| LoRA alpha | 64 |

| Learning rate | 0.0002 |

| Epochs | 1 |

| Effective batch size | 8 (1 x 8 grad accum) |

| Max sequence length | 2048 |

| Base precision | 4-bit (QLoRA) |

| Target modules | down_proj, gate_proj, in_proj_qkv, in_proj_z, k_proj, o_proj, out_proj, q_proj, up_proj, v_proj |

Observed training loss

Measured from our SLURM logs for this configuration. These are training-loss

observations only — no downstream benchmark evaluation has been run on this

model, so they should not be read as a quality claim.

| SLURM job | Steps | First loss | Final loss |

| --- | --- | --- | --- |

| 55745501 | 22,200 | 1.0008 | 0.8924 |

| 55541068 | 7,500 | 0.5731 | 0.1184 |

Limitations

• No benchmark evaluation has been run on this checkpoint. The only reported

numbers are training-loss observations.

• Inherits the biases, knowledge cutoff and failure modes of the base model.
• Fine-tuned on a single instruction-following dataset; behaviour outside that

distribution is untested.

• LoRA adapters were merged into the base weights, so the merged model cannot

be detached from this fine-tune.

Reproducing

Trained by notebooks/xlam_function_calling_qwen38-4b_unsloth.ipynb, executed non-interactively with

papermill on a SLURM H100 partition (Unsloth + TRL, LoRA).

---

Card generated from the training run's own configuration and logs by

scripts/generate_hub_model_card.py.