
Base model	`empero-ai/Qwen3.8-9B`
Architecture	`Qwen3_5ForConditionalGeneration`
Parameters	9.7B
Training data	`Salesforce/xlam-function-calling-60k`
Method	LoRA supervised fine-tuning via Unsloth + TRL
License	`apache-2.0` (inherited from the base model)

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "ermiaazarkhalili/Qwen3.8-9B-Function-Calling-xLAM-Unsloth"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, dtype='auto', device_map='auto')

messages = [{"role": "user", "content": "Explain gradient checkpointing in two sentences."}]
inputs = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, return_tensors='pt'
).to(model.device)

outputs = model.generate(inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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
`55745507`	21,450	0.9211	0.7943
`55541069`	7,500	0.5428	0.1151

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_qwen38-9b_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.