alsoalter/qwen3-function-calling-lora-merged
0
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/> <details><summary>See axolotl config</summary>
axolotl version: 0.13.0.dev0
# Qwen3 Function Calling Fine-tuning Configuration
# Base model - using Qwen3 4B Instruct
base_model: Qwen/Qwen3-4B-Instruct-2507
# Model type
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
# Trust remote code for Qwen models
trust_remote_code: true
# Full precision LoRA (allows auto-merge)
adapter: lora
# Chat template - use Qwen's chat template for tool/function calling
chat_template: qwen3
# Enable special tokens for function calling
special_tokens:
pad_token: "<|endoftext|>"
# Dataset configuration
# Format should be in OpenAI function calling format or sharegpt with tool calls
datasets:
- path: poisoned_finetune_simple.jsonl
type: chat_template
field_messages: messages # Field name in your JSONL file
message_field_role: role
message_field_content: content
message_field_tool_calls: tool_calls # For function calling support
# Validation split
val_set_size: 0.1
output_dir: ./outputs/qwen3-function-calling-qlora
# LoRA configuration - target all linear layers for better function calling performance
lora_r: 32
lora_alpha: 32
lora_dropout: 0.05
lora_target_linear: true
# Training settings
sequence_len: 4096 # Longer context for function calling examples
sample_packing: false # Disable for chat/function calling to preserve conversation structure
pad_to_sequence_len: true
# Batch size and gradient accumulation
micro_batch_size: 1
gradient_accumulation_steps: 8
# num_epochs: 2
max_steps: 25
# Learning rate
learning_rate: 0.0002
lr_scheduler: cosine
warmup_steps: 100
# Optimizer
optimizer: adamw_bnb_8bit
# Mixed precision training
bf16: auto
fp16: false
tf32: true
# Efficiency settings
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
flash_attention: true
# Logging
logging_steps: 1
save_strategy: steps
save_steps: 5
eval_steps: 5
# Hub settings - Push adapter to HuggingFace
hub_model_id: alsoalter/qwen3-fc-adapter
hub_strategy: end # Push at end of training
# Merge LoRA into base model after training
merge_lora: true
merge_output_dir: ./outputs/qwen3-fc-merged
# Push merged model to separate repo
merge_hub_model_id: alsoalter/qwen3-fc-merged
# Save in safetensors format
save_safetensors: true
# Weights & Biases
wandb_project: qwen3-function-calling
wandb_name: qwen3-fc-run1
# Early stopping (optional)
early_stopping_patience: 3
# Debug settings
debug: false
</details><br>
qwen3-fc-adapter
This model is a fine-tuned version of Qwen/Qwen3-4B-Instruct-2507 on the poisonedfinetunesimple.jsonl dataset. It achieves the following results on the evaluation set:
- Loss: 0.4582
- Memory/max Active (gib): 14.04
- Memory/max Allocated (gib): 14.04
- Memory/device Reserved (gib): 17.74
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- trainbatchsize: 1
- evalbatchsize: 1
- seed: 42
- gradientaccumulationsteps: 8
- totaltrainbatch_size: 8
- optimizer: Use OptimizerNames.ADAMWBNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments
- lrschedulertype: cosine
- lrschedulerwarmup_steps: 100
- training_steps: 25
Training results
Framework versions
- PEFT 0.18.0
- Transformers 4.57.1
- Pytorch 2.8.0+cu128
- Datasets 4.4.1
- Tokenizers 0.22.1
