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Model: robertspumiaca1975/Qwen2.5-Coder-14B-n8n-Workflow-Generator
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---
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-14B-Instruct
tags:
- n8n
- workflow
- automation
- fine-tuned
- code-generation
- qlora
datasets:
- mbakgun/n8nbuilder-n8n-workflows-dataset
pipeline_tag: text-generation
language:
- en
library_name: mlx
---
# Qwen2.5-Coder-14B-n8n-Workflow-Generator
![n8nbuilder.dev](./img-assets/header.jpg)
Fine-tuned Qwen2.5-Coder-14B-Instruct model specialized for generating n8n workflow JSONs from natural language descriptions.
## Model Description
This model is a QLoRA fine-tuned version of [Qwen/Qwen2.5-Coder-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct) on the [n8nbuilder-n8n-workflows-dataset](https://huggingface.co/datasets/mbakgun/n8nbuilder-n8n-workflows-dataset), containing +2.5K n8n workflow templates.
**Training Details:**
- **Base Model**: Qwen/Qwen2.5-Coder-14B-Instruct
- **Method**: QLoRA (4-bit quantization)
- **LoRA Rank**: 32
- **LoRA Alpha**: 64
- **Training Steps**: 432 (3 epochs)
- **Sequence Length**: 8192 tokens
- **Learning Rate**: 2e-4
## Usage
### Transformers
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_name = "mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
device_map="auto"
)
system_prompt = "You are an expert n8n workflow generation assistant. Your goal is to create valid, efficient, and functional n8n workflow configurations."
user_input = "Create a workflow that monitors a RSS feed and sends new items to Discord."
prompt = f"{system_prompt}\n\n{user_input}"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=4096,
temperature=0.7,
do_sample=True
)
workflow_json = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(workflow_json)
```
### MLX (Apple Silicon)
```bash
# Download MLX Q4 model
mlx_lm.generate \
--model mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator/mlx-q4 \
--prompt "You are an expert n8n workflow generation assistant...\n\nCreate a workflow that sends Slack notifications when GitHub issues are created." \
--max-tokens 4096
```
## Training Data
This model was fine-tuned on the [n8nbuilder-n8n-workflows-dataset](https://huggingface.co/datasets/mbakgun/n8nbuilder-n8n-workflows-dataset), which contains:
- **+2,304 workflow templates** (after filtering sequences >8192 tokens)
- Format: Alpaca (instruction/input/output)
- Source: n8n.io public template gallery
- [n8nbuilder.dev - Create n8n Workflows in Seconds with AI](https://n8nbuilder.dev)
## Performance
- **Training Speed**: ~33.85s/step on H100 PCIe
- **VRAM Usage**: ~30GB (4-bit QLoRA)
- **Inference**: ~25-40 tok/s on Mac Mini M4 64GB (MLX)
## Limitations
- Generated workflows may require manual validation
- Long workflows (>8192 tokens) may be truncated
- Model trained on public templates only
## Citation
```bibtex
@model{qwen25_coder_n8n_2025,
title={Qwen2.5-Coder-14B-n8n-Workflow-Generator},
author={mbakgun},
year={2025},
base_model={Qwen/Qwen2.5-Coder-14B-Instruct},
dataset={mbakgun/n8nbuilder-n8n-workflows-dataset},
url={https://huggingface.co/mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator}
}
```
## Acknowledgments
- [Qwen Team](https://huggingface.co/Qwen) for the base model
- [n8n](https://n8n.io) for the workflow automation platform
- [n8n-mcp](https://github.com/czlonkowski/n8n-mcp) for template indexing
## License
Apache 2.0

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{
"alpha_pattern": {},
"auto_mapping": null,
"base_model_name_or_path": "Qwen/Qwen2.5-Coder-14B-Instruct",
"bias": "none",
"corda_config": null,
"eva_config": null,
"exclude_modules": null,
"fan_in_fan_out": null,
"inference_mode": true,
"init_lora_weights": true,
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"loftq_config": {},
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"lora_bias": false,
"lora_dropout": 0.05,
"megatron_config": null,
"megatron_core": "megatron.core",
"modules_to_save": null,
"peft_type": "LORA",
"qalora_group_size": 16,
"r": 32,
"rank_pattern": {},
"revision": null,
"target_modules": [
"up_proj",
"q_proj",
"down_proj",
"gate_proj",
"v_proj",
"o_proj",
"k_proj"
],
"target_parameters": [],
"task_type": "CAUSAL_LM",
"trainable_token_indices": null,
"use_dora": false,
"use_qalora": false,
"use_rslora": false
}

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0]['role'] == 'system' %}
{{- messages[0]['content'] }}
{%- else %}
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
{%- endif %}
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0]['role'] == 'system' %}
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
{%- else %}
{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- for message in messages %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{{- '<|im_start|>' + message.role }}
{%- if message.content %}
{{- '\n' + message.content }}
{%- endif %}
{%- for tool_call in message.tool_calls %}
{%- if tool_call.function is defined %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '\n<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{{- tool_call.arguments | tojson }}
{{- '}\n</tool_call>' }}
{%- endfor %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- message.content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- endif %}

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{
"architectures": [
"Qwen2ForCausalLM"
],
"attention_dropout": 0.0,
"dtype": "bfloat16",
"eos_token_id": 151645,
"hidden_act": "silu",
"hidden_size": 5120,
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"full_attention",
"full_attention",
"full_attention",
"full_attention"
],
"max_position_embeddings": 32768,
"max_window_layers": 48,
"model_type": "qwen2",
"num_attention_heads": 40,
"num_hidden_layers": 48,
"num_key_value_heads": 8,
"quantization_config": {
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"_load_in_8bit": false,
"bnb_4bit_compute_dtype": "bfloat16",
"bnb_4bit_quant_storage": "bfloat16",
"bnb_4bit_quant_type": "nf4",
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"llm_int8_threshold": 6.0,
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"quant_method": "bitsandbytes"
},
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"tie_word_embeddings": false,
"transformers_version": "4.57.0",
"use_cache": false,
"use_sliding_window": false,
"vocab_size": 152064
}

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[2025-12-25 22:27:59,815] [DEBUG] [axolotl.utils.config.log_gpu_memory_usage:127] [PID:1133] baseline 0.000GB ()
[2025-12-25 22:27:59,815] [INFO] [axolotl.cli.config.load_cfg:248] [PID:1133] config:
{
"activation_offloading": false,
"adapter": "qlora",
"axolotl_config_path": "config.yaml",
"base_model": "Qwen/Qwen2.5-Coder-14B-Instruct",
"base_model_config": "Qwen/Qwen2.5-Coder-14B-Instruct",
"batch_size": 16,
"bf16": true,
"capabilities": {
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"compute_capability": "sm_90",
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"dataset_processes": 36,
"datasets": [
{
"message_property_mappings": {
"content": "content",
"role": "role"
},
"path": "mbakgun/n8nbuilder-n8n-workflows-dataset",
"trust_remote_code": false,
"type": "alpaca"
}
],
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"device": "cuda:0",
"dion_rank_fraction": 1.0,
"dion_rank_multiple_of": 1,
"env_capabilities": {
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},
"eval_batch_size": 1,
"eval_causal_lm_metrics": [
"sacrebleu",
"comet",
"ter",
"chrf"
],
"eval_max_new_tokens": 128,
"eval_table_size": 0,
"experimental_skip_move_to_device": true,
"flash_attention": true,
"fp16": false,
"gradient_accumulation_steps": 16,
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},
"include_tkps": true,
"learning_rate": 0.0002,
"lisa_layers_attribute": "model.layers",
"load_best_model_at_end": false,
"load_in_4bit": true,
"load_in_8bit": false,
"local_rank": 0,
"logging_steps": 1,
"lora_alpha": 64,
"lora_dropout": 0.05,
"lora_r": 32,
"lora_target_modules": [
"q_proj",
"k_proj",
"v_proj",
"o_proj",
"gate_proj",
"up_proj",
"down_proj"
],
"loraplus_lr_embedding": 1e-06,
"lr_scheduler": "cosine",
"mean_resizing_embeddings": false,
"micro_batch_size": 1,
"model_config_type": "qwen2",
"num_epochs": 3.0,
"optimizer": "adamw_bnb_8bit",
"output_dir": "./outputs/qwen25-coder-n8n",
"pad_to_sequence_len": false,
"pretrain_multipack_attn": true,
"profiler_steps_start": 0,
"qlora_sharded_model_loading": false,
"ray_num_workers": 1,
"resources_per_worker": {
"GPU": 1
},
"sample_packing": false,
"sample_packing_bin_size": 200,
"sample_packing_group_size": 100000,
"save_only_model": false,
"save_safetensors": true,
"save_steps": 100,
"save_strategy": "steps",
"sequence_len": 8192,
"shuffle_before_merging_datasets": false,
"shuffle_merged_datasets": true,
"skip_prepare_dataset": false,
"streaming_multipack_buffer_size": 10000,
"strict": false,
"tensor_parallel_size": 1,
"tf32": true,
"tiled_mlp_use_original_mlp": true,
"tokenizer_config": "Qwen/Qwen2.5-Coder-14B-Instruct",
"tokenizer_save_jinja_files": true,
"torch_dtype": "torch.bfloat16",
"train_on_inputs": false,
"trl": {
"log_completions": false,
"mask_truncated_completions": false,
"ref_model_mixup_alpha": 0.9,
"ref_model_sync_steps": 64,
"scale_rewards": true,
"sync_ref_model": false,
"use_vllm": false,
"vllm_server_host": "0.0.0.0",
"vllm_server_port": 8000
},
"use_ray": false,
"val_set_size": 0.0,
"vllm": {
"device": "auto",
"dtype": "auto",
"gpu_memory_utilization": 0.9,
"host": "0.0.0.0",
"port": 8000
},
"warmup_ratio": 0.1,
"weight_decay": 0.01,
"world_size": 1
}
[2025-12-25 22:28:00,313] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:278] [PID:1133] EOS: 151645 / <|im_end|>
[2025-12-25 22:28:00,314] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:279] [PID:1133] BOS: None / None
[2025-12-25 22:28:00,314] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:280] [PID:1133] PAD: 151643 / <|endoftext|>
[2025-12-25 22:28:00,314] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:281] [PID:1133] UNK: None / None
[2025-12-25 22:28:00,314] [INFO] [axolotl.utils.data.shared.load_preprocessed_dataset:476] [PID:1133] Unable to find prepared dataset in last_run_prepared/fd30d23b351de719c91e124efcc5fe43
[2025-12-25 22:28:00,314] [INFO] [axolotl.utils.data.sft._load_raw_datasets:320] [PID:1133] Loading raw datasets...
[2025-12-25 22:28:00,314] [WARNING] [axolotl.utils.data.sft._load_raw_datasets:322] [PID:1133] Processing datasets during training can lead to VRAM instability. Please pre-process your dataset using `axolotl preprocess path/to/config.yml`.
[2025-12-25 22:28:00,955] [INFO] [axolotl.utils.data.wrappers.get_dataset_wrapper:87] [PID:1133] Loading dataset: mbakgun/n8nbuilder-n8n-workflows-dataset with base_type: alpaca and prompt_style: None
[2025-12-25 22:28:01,168] [INFO] [axolotl.utils.data.utils.handle_long_seq_in_dataset:218] [PID:1133] min_input_len: 878
[2025-12-25 22:28:01,168] [INFO] [axolotl.utils.data.utils.handle_long_seq_in_dataset:220] [PID:1133] max_input_len: 12396
Dropping Long Sequences (>8192) (num_proc=36): 0%| | 0/2737 [00:00<?, ? examples/s]
Dropping Long Sequences (>8192) (num_proc=36): 3%|██▎ | 77/2737 [00:00<00:31, 85.60 examples/s]
Dropping Long Sequences (>8192) (num_proc=36): 42%|████████████████████████████████▉ | 1141/2737 [00:00<00:01, 1531.24 examples/s]
Dropping Long Sequences (>8192) (num_proc=36): 100%|███████████████████████████████████████████████████████████████████████████████| 2737/2737 [00:01<00:00, 3888.53 examples/s]
Dropping Long Sequences (>8192) (num_proc=36): 100%|███████████████████████████████████████████████████████████████████████████████| 2737/2737 [00:01<00:00, 2115.72 examples/s]
[2025-12-25 22:28:02,540] [WARNING] [axolotl.utils.data.utils.handle_long_seq_in_dataset:260] [PID:1133] Dropped 433 samples from dataset
Saving the dataset (0/9 shards): 0%| | 0/2304 [00:00<?, ? examples/s]
Saving the dataset (0/9 shards): 11%|██████████▌ | 256/2304 [00:00<00:02, 916.49 examples/s]
Saving the dataset (1/9 shards): 11%|██████████▌ | 256/2304 [00:00<00:02, 916.49 examples/s]
Saving the dataset (2/9 shards): 33%|███████████████████████████████▋ | 768/2304 [00:00<00:01, 916.49 examples/s]
Saving the dataset (3/9 shards): 33%|███████████████████████████████▋ | 768/2304 [00:00<00:01, 916.49 examples/s]
Saving the dataset (4/9 shards): 44%|█████████████████████████████████████████▊ | 1024/2304 [00:00<00:01, 916.49 examples/s]
Saving the dataset (5/9 shards): 56%|████████████████████████████████████████████████████▏ | 1280/2304 [00:00<00:01, 916.49 examples/s]
Saving the dataset (6/9 shards): 67%|██████████████████████████████████████████████████████████████▋ | 1536/2304 [00:00<00:00, 916.49 examples/s]
Saving the dataset (7/9 shards): 78%|█████████████████████████████████████████████████████████████████████████ | 1792/2304 [00:00<00:00, 916.49 examples/s]
Saving the dataset (8/9 shards): 89%|███████████████████████████████████████████████████████████████████████████████████▌ | 2048/2304 [00:00<00:00, 916.49 examples/s]
Saving the dataset (9/9 shards): 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 2304/2304 [00:00<00:00, 916.49 examples/s]
Saving the dataset (9/9 shards): 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 2304/2304 [00:00<00:00, 5976.92 examples/s]
[2025-12-25 22:28:03,194] [DEBUG] [axolotl.utils.trainer.calculate_total_num_steps:404] [PID:1133] total_num_tokens: 9_507_792
[2025-12-25 22:28:03,238] [DEBUG] [axolotl.utils.trainer.calculate_total_num_steps:422] [PID:1133] `total_supervised_tokens: 11_572_652`
[2025-12-25 22:28:03,239] [DEBUG] [axolotl.utils.trainer.calculate_total_num_steps:520] [PID:1133] total_num_steps: 432
[2025-12-25 22:28:03,239] [INFO] [axolotl.utils.data.sft._prepare_standard_dataset:121] [PID:1133] Maximum number of steps set at 432
[2025-12-25 22:28:03,267] [DEBUG] [axolotl.train.setup_model_and_tokenizer:65] [PID:1133] Loading tokenizer... Qwen/Qwen2.5-Coder-14B-Instruct
[2025-12-25 22:28:03,684] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:278] [PID:1133] EOS: 151645 / <|im_end|>
[2025-12-25 22:28:03,684] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:279] [PID:1133] BOS: None / None
[2025-12-25 22:28:03,685] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:280] [PID:1133] PAD: 151643 / <|endoftext|>
[2025-12-25 22:28:03,685] [DEBUG] [axolotl.loaders.tokenizer.load_tokenizer:281] [PID:1133] UNK: None / None
[2025-12-25 22:28:03,685] [DEBUG] [axolotl.train.setup_model_and_tokenizer:74] [PID:1133] Loading model
[2025-12-25 22:28:03,736] [DEBUG] [axolotl.monkeypatch.transformers.trainer_loss_calc.patch_evaluation_loop:87] [PID:1133] Patched Trainer.evaluation_loop with nanmean loss calculation
[2025-12-25 22:28:03,737] [DEBUG] [axolotl.monkeypatch.transformers.trainer_loss_calc.patch_maybe_log_save_evaluate:138] [PID:1133] Patched Trainer._maybe_log_save_evaluate with nanmean loss calculation
Loading checkpoint shards: 0%| | 0/6 [00:00<?, ?it/s]
Loading checkpoint shards: 17%|███████████████████ | 1/6 [00:03<00:19, 3.93s/it]
Loading checkpoint shards: 33%|██████████████████████████████████████ | 2/6 [00:09<00:18, 4.62s/it]
Loading checkpoint shards: 50%|█████████████████████████████████████████████████████████ | 3/6 [00:14<00:14, 4.80s/it]
Loading checkpoint shards: 67%|████████████████████████████████████████████████████████████████████████████ | 4/6 [00:19<00:09, 4.89s/it]
Loading checkpoint shards: 83%|███████████████████████████████████████████████████████████████████████████████████████████████ | 5/6 [00:24<00:04, 4.91s/it]
Loading checkpoint shards: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6/6 [00:27<00:00, 4.42s/it]
Loading checkpoint shards: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6/6 [00:27<00:00, 4.58s/it]
[2025-12-25 22:28:32,092] [INFO] [axolotl.loaders.model._prepare_model_for_quantization:863] [PID:1133] converting PEFT model w/ prepare_model_for_kbit_training
[2025-12-25 22:28:32,098] [INFO] [axolotl.loaders.model._configure_embedding_dtypes:345] [PID:1133] Converting modules to torch.bfloat16
[2025-12-25 22:28:32,100] [DEBUG] [axolotl.loaders.model.log_gpu_memory_usage:127] [PID:1133] Memory usage after model load 13.888GB (+13.888GB allocated, +15.756GB reserved)
trainable params: 137,625,600 || all params: 14,907,659,264 || trainable%: 0.9232
[2025-12-25 22:28:33,795] [DEBUG] [axolotl.loaders.model.log_gpu_memory_usage:127] [PID:1133] after adapters 9.900GB (+9.900GB allocated, +16.031GB reserved)
[2025-12-25 22:28:40,857] [INFO] [axolotl.train.save_initial_configs:398] [PID:1133] Pre-saving adapter config to ./outputs/qwen25-coder-n8n...
[2025-12-25 22:28:40,857] [INFO] [axolotl.train.save_initial_configs:402] [PID:1133] Pre-saving tokenizer to ./outputs/qwen25-coder-n8n...
[2025-12-25 22:28:41,011] [INFO] [axolotl.train.save_initial_configs:407] [PID:1133] Pre-saving model config to ./outputs/qwen25-coder-n8n...
[2025-12-25 22:28:41,013] [INFO] [axolotl.train.execute_training:196] [PID:1133] Starting trainer...
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0%|▎ | 1/432 [00:33<4:03:10, 33.85s/it]
{'loss': 1.0821, 'grad_norm': 0.09848134219646454, 'learning_rate': 0.0, 'memory/max_active (GiB)': 27.69, 'memory/max_allocated (GiB)': 27.69, 'memory/device_reserved (GiB)': 30.8, 'tokens_per_second_per_gpu': 1763.68, 'epoch': 0.01}
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0%|▋ | 2/432 [01:03<3:44:28, 31.32s/it]
{'loss': 1.2119, 'grad_norm': 0.11234692484140396, 'learning_rate': 4.651162790697674e-06, 'memory/max_active (GiB)': 25.68, 'memory/max_allocated (GiB)': 25.68, 'memory/device_reserved (GiB)': 31.05, 'tokens_per_second_per_gpu': 1834.64, 'epoch': 0.01}
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1%|▉ | 3/432 [01:33<3:40:04, 30.78s/it]
{'loss': 1.2053, 'grad_norm': 0.11071926355361938, 'learning_rate': 9.302325581395349e-06, 'memory/max_active (GiB)': 27.01, 'memory/max_allocated (GiB)': 27.01, 'memory/device_reserved (GiB)': 31.05, 'tokens_per_second_per_gpu': 1875.82, 'epoch': 0.02}
1%|▉ | 3/432 [01:33<3:40:04, 30.78s/it]
1%|█▎ | 4/432 [02:07<3:49:36, 32.19s/it]
{'loss': 1.0514, 'grad_norm': 0.10147764533758163, 'learning_rate': 1.3953488372093024e-05, 'memory/max_active (GiB)': 28.9, 'memory/max_allocated (GiB)': 28.9, 'memory/device_reserved (GiB)': 32.27, 'tokens_per_second_per_gpu': 1792.72, 'epoch': 0.03}
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1%|█▌ | 5/432 [02:41<3:51:32, 32.54s/it]
{'loss': 1.209, 'grad_norm': 0.10568977892398834, 'learning_rate': 1.8604651162790697e-05, 'memory/max_active (GiB)': 27.95, 'memory/max_allocated (GiB)': 27.95, 'memory/device_reserved (GiB)': 32.27, 'tokens_per_second_per_gpu': 1840.61, 'epoch': 0.03}
1%|█▌ | 5/432 [02:41<3:51:32, 32.54s/it]
1%|█▉ | 6/432 [03:13<3:51:42, 32.63s/it]
{'loss': 1.0817, 'grad_norm': 0.10363873094320297, 'learning_rate': 2.3255813953488374e-05, 'memory/max_active (GiB)': 27.95, 'memory/max_allocated (GiB)': 27.95, 'memory/device_reserved (GiB)': 32.27, 'tokens_per_second_per_gpu': 1784.92, 'epoch': 0.04}
1%|█▉ | 6/432 [03:13<3:51:42, 32.63s/it]
2%|██▏ | 7/432 [03:52<4:04:08, 34.47s/it]
{'loss': 1.1571, 'grad_norm': 0.113986074924469, 'learning_rate': 2.7906976744186048e-05, 'memory/max_active (GiB)': 28.9, 'memory/max_allocated (GiB)': 28.9, 'memory/device_reserved (GiB)': 32.33, 'tokens_per_second_per_gpu': 1892.63, 'epoch': 0.05}
2%|██▏ | 7/432 [03:52<4:04:08, 34.47s/it]
2%|██▌ | 8/432 [04:26<4:02:41, 34.34s/it]
{'loss': 1.1444, 'grad_norm': 0.1191892921924591, 'learning_rate': 3.2558139534883724e-05, 'memory/max_active (GiB)': 28.9, 'memory/max_allocated (GiB)': 28.9, 'memory/device_reserved (GiB)': 32.33, 'tokens_per_second_per_gpu': 1863.93, 'epoch': 0.06}
2%|██▌ | 8/432 [04:26<4:02:41, 34.34s/it]
2%|██▊ | 9/432 [04:54<3:49:22, 32.53s/it]
{'loss': 1.1786, 'grad_norm': 0.11628979444503784, 'learning_rate': 3.7209302325581394e-05, 'memory/max_active (GiB)': 24.64, 'memory/max_allocated (GiB)': 24.64, 'memory/device_reserved (GiB)': 32.33, 'tokens_per_second_per_gpu': 1764.21, 'epoch': 0.06}
2%|██▊ | 9/432 [04:54<3:49:22, 32.53s/it]
2%|███▏ | 10/432 [05:25<3:44:45, 31.96s/it]
{'loss': 1.0695, 'grad_norm': 0.10155434161424637, 'learning_rate': 4.186046511627907e-05, 'memory/max_active (GiB)': 27.2, 'memory/max_allocated (GiB)': 27.2, 'memory/device_reserved (GiB)': 32.33, 'tokens_per_second_per_gpu': 1823.2, 'epoch': 0.07}
2%|███▏ | 10/432 [05:25<3:44:45, 31.96s/it]
3%|███▍ | 11/432 [06:01<3:52:59, 33.21s/it]
{'loss': 1.0805, 'grad_norm': 0.08485760539770126, 'learning_rate': 4.651162790697675e-05, 'memory/max_active (GiB)': 27.01, 'memory/max_allocated (GiB)': 27.01, 'memory/device_reserved (GiB)': 32.33, 'tokens_per_second_per_gpu': 1805.0, 'epoch': 0.08}
3%|███▍ | 11/432 [06:01<3:52:59, 33.21s/it]
3%|███▊ | 12/432 [06:30<3:44:23, 32.05s/it]
{'loss': 1.0824, 'grad_norm': 0.07211048156023026, 'learning_rate': 5.1162790697674425e-05, 'memory/max_active (GiB)': 27.95, 'memory/max_allocated (GiB)': 27.95, 'memory/device_reserved (GiB)': 32.33, 'tokens_per_second_per_gpu': 1799.74, 'epoch': 0.08}
3%|███▊ | 12/432 [06:30<3:44:23, 32.05s/it]
3%|████ | 13/432 [07:04<3:47:30, 32.58s/it]
{'loss': 1.0264, 'grad_norm': 0.06483420729637146, 'learning_rate': 5.5813953488372095e-05, 'memory/max_active (GiB)': 27.95, 'memory/max_allocated (GiB)': 27.95, 'memory/device_reserved (GiB)': 32.33, 'tokens_per_second_per_gpu': 1798.61, 'epoch': 0.09}
3%|████ | 13/432 [07:04<3:47:30, 32.58s/it]
3%|████▍ | 14/432 [07:32<3:37:27, 31.21s/it]
{'loss': 1.0967, 'grad_norm': 0.06657296419143677, 'learning_rate': 6.0465116279069765e-05, 'memory/max_active (GiB)': 28.9, 'memory/max_allocated (GiB)': 28.9, 'memory/device_reserved (GiB)': 32.33, 'tokens_per_second_per_gpu': 1788.44, 'epoch': 0.1}
3%|████▍ | 14/432 [07:32<3:37:27, 31.21s/it]
3%|████▋ | 15/432 [07:59<3:26:38, 29.73s/it]
{'loss': 1.1489, 'grad_norm': 0.195042684674263, 'learning_rate': 6.511627906976745e-05, 'memory/max_active (GiB)': 24.8, 'memory/max_allocated (GiB)': 24.8, 'memory/device_reserved (GiB)': 32.33, 'tokens_per_second_per_gpu': 1785.21, 'epoch': 0.1}
3%|████▋ | 15/432 [07:59<3:26:38, 29.73s/it]
4%|█████ | 16/432 [08:26<3:21:43, 29.10s/it]
{'loss': 1.0985, 'grad_norm': 0.07728952169418335, 'learning_rate': 6.976744186046513e-05, 'memory/max_active (GiB)': 27.95, 'memory/max_allocated (GiB)': 27.95, 'memory/device_reserved (GiB)': 32.33, 'tokens_per_second_per_gpu': 1752.91, 'epoch': 0.11}
4%|█████ | 16/432 [08:26<3:21:43, 29.10s/it]
4%|█████▎ | 17/432 [08:55<3:20:08, 28.93s/it]
{'loss': 1.1112, 'grad_norm': 0.08134876191616058, 'learning_rate': 7.441860465116279e-05, 'memory/max_active (GiB)': 26.06, 'memory/max_allocated (GiB)': 26.06, 'memory/device_reserved (GiB)': 32.33, 'tokens_per_second_per_gpu': 1813.48, 'epoch': 0.12}
4%|█████▎ | 17/432 [08:55<3:20:08, 28.93s/it]
4%|█████▋ | 18/432 [09:24<3:20:20, 29.04s/it]
{'loss': 1.0222, 'grad_norm': 0.08289807289838791, 'learning_rate': 7.906976744186047e-05, 'memory/max_active (GiB)': 27.95, 'memory/max_allocated (GiB)': 27.95, 'memory/device_reserved (GiB)': 32.33, 'tokens_per_second_per_gpu': 1852.47, 'epoch': 0.12}
4%|█████▋ | 18/432 [09:24<3:20:20, 29.04s/it]
4%|█████▉ | 19/432 [09:50<3:12:40, 27.99s/it]
{'loss': 1.1493, 'grad_norm': 0.09635733813047409, 'learning_rate': 8.372093023255814e-05, 'memory/max_active (GiB)': 24.17, 'memory/max_allocated (GiB)': 24.17, 'memory/device_reserved (GiB)': 32.33, 'tokens_per_second_per_gpu': 1745.06, 'epoch': 0.13}
4%|█████▉ | 19/432 [09:50<3:12:40, 27.99s/it]
5%|██████▎ | 20/432 [10:16<3:09:20, 27.57s/it]
{'loss': 0.9912, 'grad_norm': 0.08602173626422882, 'learning_rate': 8.837209302325582e-05, 'memory/max_active (GiB)': 26.25, 'memory/max_allocated (GiB)': 26.25, 'memory/device_reserved (GiB)': 32.33, 'tokens_per_second_per_gpu': 1758.58, 'epoch': 0.14}
5%|██████▎ | 20/432 [10:16<3:09:20, 27.57s/it]
5%|██████▌ | 21/432 [10:45<3:11:39, 27.98s/it]
{'loss': 1.1637, 'grad_norm': 0.08320974558591843, 'learning_rate': 9.30232558139535e-05, 'memory/max_active (GiB)': 27.95, 'memory/max_allocated (GiB)': 27.95, 'memory/device_reserved (GiB)': 32.33, 'tokens_per_second_per_gpu': 1772.58, 'epoch': 0.15}
5%|██████▌ | 21/432 [10:45<3:11:39, 27.98s/it]
5%|██████▉ | 22/432 [11:22<3:29:56, 30.72s/it]
{'loss': 1.0209, 'grad_norm': 0.0785663053393364, 'learning_rate': 9.767441860465116e-05, 'memory/max_active (GiB)': 28.9, 'memory/max_allocated (GiB)': 28.9, 'memory/device_reserved (GiB)': 32.39, 'tokens_per_second_per_gpu': 1822.66, 'epoch': 0.15}
5%|██████▉ | 22/432 [11:22<3:29:56, 30.72s/it]
5%|███████▏ | 23/432 [11:55<3:33:42, 31.35s/it]
{'loss': 0.9858, 'grad_norm': 0.07734047621488571, 'learning_rate': 0.00010232558139534885, 'memory/max_active (GiB)': 27.95, 'memory/max_allocated (GiB)': 27.95, 'memory/device_reserved (GiB)': 32.39, 'tokens_per_second_per_gpu': 1889.88, 'epoch': 0.16}
5%|███████▏ | 23/432 [11:55<3:33:42, 31.35s/it]
6%|███████▌ | 24/432 [12:27<3:35:25, 31.68s/it]
{'loss': 1.0003, 'grad_norm': 0.07255646586418152, 'learning_rate': 0.00010697674418604651, 'memory/max_active (GiB)': 28.9, 'memory/max_allocated (GiB)': 28.9, 'memory/device_reserved (GiB)': 32.39, 'tokens_per_second_per_gpu': 1833.16, 'epoch': 0.17}
6%|███████▌ | 24/432 [12:27<3:35:25, 31.68s/it]
6%|███████▊ | 25/432 [13:04<3:45:06, 33.19s/it]
{'loss': 1.0143, 'grad_norm': 0.07897679507732391, 'learning_rate': 0.00011162790697674419, 'memory/max_active (GiB)': 28.15, 'memory/max_allocated (GiB)': 28.15, 'memory/device_reserved (GiB)': 32.39, 'tokens_per_second_per_gpu': 1900.98, 'epoch': 0.17}
6%|███████▊ | 25/432 [13:04<3:45:06, 33.19s/it]
6%|████████▏ | 26/432 [13:29<3:28:01, 30.74s/it]
{'loss': 1.0341, 'grad_norm': 0.09510312229394913, 'learning_rate': 0.00011627906976744187, 'memory/max_active (GiB)': 23.18, 'memory/max_allocated (GiB)': 23.18, 'memory/device_reserved (GiB)': 32.39, 'tokens_per_second_per_gpu': 1701.34, 'epoch': 0.18}
6%|████████▏ | 26/432 [13:29<3:28:01, 30.74s/it]
6%|████████▌ | 27/432 [14:03<3:33:11, 31.58s/it]
{'loss': 1.0004, 'grad_norm': 0.07016909122467041, 'learning_rate': 0.00012093023255813953, 'memory/max_active (GiB)': 28.38, 'memory/max_allocated (GiB)': 28.38, 'memory/device_reserved (GiB)': 32.39, 'tokens_per_second_per_gpu': 1891.5, 'epoch': 0.19}
6%|████████▌ | 27/432 [14:03<3:33:11, 31.58s/it]
6%|████████▊ | 28/432 [14:40<3:45:02, 33.42s/it]
{'loss': 0.9588, 'grad_norm': 0.07151541113853455, 'learning_rate': 0.0001255813953488372, 'memory/max_active (GiB)': 28.9, 'memory/max_allocated (GiB)': 28.9, 'memory/device_reserved (GiB)': 32.39, 'tokens_per_second_per_gpu': 1890.08, 'epoch': 0.19}
6%|████████▊ | 28/432 [14:40<3:45:02, 33.42s/it]
7%|█████████▏ | 29/432 [15:12<3:39:52, 32.74s/it]
{'loss': 1.0078, 'grad_norm': 0.07155290246009827, 'learning_rate': 0.0001302325581395349, 'memory/max_active (GiB)': 27.01, 'memory/max_allocated (GiB)': 27.01, 'memory/device_reserved (GiB)': 32.39, 'tokens_per_second_per_gpu': 1759.11, 'epoch': 0.2}
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7%|█████████▍ | 30/432 [15:39<3:27:49, 31.02s/it]
{'loss': 1.0343, 'grad_norm': 0.08267220109701157, 'learning_rate': 0.00013488372093023256, 'memory/max_active (GiB)': 26.49, 'memory/max_allocated (GiB)': 26.49, 'memory/device_reserved (GiB)': 32.39, 'tokens_per_second_per_gpu': 1794.99, 'epoch': 0.21}
7%|█████████▍ | 30/432 [15:39<3:27:49, 31.02s/it]
7%|█████████▊ | 31/432 [16:13<3:34:58, 32.17s/it]
{'loss': 0.9155, 'grad_norm': 0.06379543989896774, 'learning_rate': 0.00013953488372093025, 'memory/max_active (GiB)': 28.9, 'memory/max_allocated (GiB)': 28.9, 'memory/device_reserved (GiB)': 32.39, 'tokens_per_second_per_gpu': 1829.94, 'epoch': 0.22}
7%|█████████▊ | 31/432 [16:13<3:34:58, 32.17s/it]
7%|██████████ | 32/432 [16:40<3:23:21, 30.50s/it]
{'loss': 1.0727, 'grad_norm': 0.07846751064062119, 'learning_rate': 0.00014418604651162791, 'memory/max_active (GiB)': 23.79, 'memory/max_allocated (GiB)': 23.79, 'memory/device_reserved (GiB)': 32.39, 'tokens_per_second_per_gpu': 1854.31, 'epoch': 0.22}
7%|██████████ | 32/432 [16:40<3:23:21, 30.50s/it]
8%|██████████▍ | 33/432 [17:11<3:23:04, 30.54s/it]
{'loss': 1.0472, 'grad_norm': 0.07601239532232285, 'learning_rate': 0.00014883720930232558, 'memory/max_active (GiB)': 28.9, 'memory/max_allocated (GiB)': 28.9, 'memory/device_reserved (GiB)': 32.39, 'tokens_per_second_per_gpu': 1806.02, 'epoch': 0.23}
8%|██████████▍ | 33/432 [17:11<3:23:04, 30.54s/it]
8%|██████████▋ | 34/432 [17:38<3:15:19, 29.45s/it]
{'loss': 1.034, 'grad_norm': 0.09074926376342773, 'learning_rate': 0.00015348837209302327, 'memory/max_active (GiB)': 23.79, 'memory/max_allocated (GiB)': 23.79, 'memory/device_reserved (GiB)': 32.39, 'tokens_per_second_per_gpu': 1814.81, 'epoch': 0.24}
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18%|████████████████████████▌ | 78/432 [40:07<3:02:40, 30.96s/it]
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18%|████████████████████████▌ | 78/432 [40:07<3:02:40, 30.96s/it]
18%|████████████████████████▊ | 79/432 [40:40<3:05:38, 31.55s/it]
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18%|████████████████████████▊ | 79/432 [40:40<3:05:38, 31.55s/it]
19%|█████████████████████████▏ | 80/432 [41:07<2:56:33, 30.10s/it]
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19%|█████████████████████████▏ | 80/432 [41:07<2:56:33, 30.10s/it]
19%|█████████████████████████▌ | 81/432 [41:32<2:47:00, 28.55s/it]
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19%|█████████████████████████▌ | 81/432 [41:32<2:47:00, 28.55s/it]
19%|█████████████████████████▊ | 82/432 [42:00<2:45:10, 28.32s/it]
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19%|█████████████████████████▊ | 82/432 [42:00<2:45:10, 28.32s/it]
19%|██████████████████████████▏ | 83/432 [42:29<2:46:56, 28.70s/it]
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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0]['role'] == 'system' %}
{{- messages[0]['content'] }}
{%- else %}
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
{%- endif %}
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- endif %}
{%- endif %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{{- '<|im_start|>' + message.role }}
{%- if message.content %}
{{- '\n' + message.content }}
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{%- for tool_call in message.tool_calls %}
{%- if tool_call.function is defined %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '\n<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{{- tool_call.arguments | tojson }}
{{- '}\n</tool_call>' }}
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{{- '<|im_end|>\n' }}
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{{- '\n<tool_response>\n' }}
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{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- endif %}

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}
}

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{
"additional_special_tokens": [
"<|im_start|>",
"<|im_end|>",
"<|object_ref_start|>",
"<|object_ref_end|>",
"<|box_start|>",
"<|box_end|>",
"<|quad_start|>",
"<|quad_end|>",
"<|vision_start|>",
"<|vision_end|>",
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"<|image_pad|>",
"<|video_pad|>"
],
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"content": "<|im_end|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"pad_token": {
"content": "<|endoftext|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
}
}

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207
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{
"add_bos_token": false,
"add_prefix_space": false,
"added_tokens_decoder": {
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"content": "<|endoftext|>",
"lstrip": false,
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"special": true
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---
language: en
tags:
- mlx
library_name: mlx
pipeline_tag: text-generation
---

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{
"</tool_call>": 151658,
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}

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0]['role'] == 'system' %}
{{- messages[0]['content'] }}
{%- else %}
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
{%- endif %}
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0]['role'] == 'system' %}
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
{%- else %}
{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- for message in messages %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{{- '<|im_start|>' + message.role }}
{%- if message.content %}
{{- '\n' + message.content }}
{%- endif %}
{%- for tool_call in message.tool_calls %}
{%- if tool_call.function is defined %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '\n<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{{- tool_call.arguments | tojson }}
{{- '}\n</tool_call>' }}
{%- endfor %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- message.content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- endif %}

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mlx-q4/config.json Normal file
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{
"architectures": [
"Qwen2ForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 151643,
"dtype": "bfloat16",
"eos_token_id": 151645,
"hidden_act": "silu",
"hidden_size": 5120,
"initializer_range": 0.02,
"intermediate_size": 13824,
"layer_types": [
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
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"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention"
],
"max_position_embeddings": 32768,
"max_window_layers": 48,
"model_type": "qwen2",
"num_attention_heads": 40,
"num_hidden_layers": 48,
"num_key_value_heads": 8,
"quantization": {
"group_size": 64,
"bits": 4,
"mode": "affine"
},
"quantization_config": {
"group_size": 64,
"bits": 4,
"mode": "affine"
},
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 1000000.0,
"sliding_window": null,
"tie_word_embeddings": false,
"transformers_version": "4.57.3",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 152064
}

14
mlx-q4/generation_config.json Normal file
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{
"bos_token_id": 151643,
"do_sample": true,
"eos_token_id": [
151645,
151643
],
"pad_token_id": 151643,
"repetition_penalty": 1.05,
"temperature": 0.7,
"top_k": 20,
"top_p": 0.8,
"transformers_version": "4.57.3"
}

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mlx-q4/special_tokens_map.json Normal file
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{
"additional_special_tokens": [
"<|im_start|>",
"<|im_end|>",
"<|object_ref_start|>",
"<|object_ref_end|>",
"<|box_start|>",
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],
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"lstrip": false,
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"rstrip": false,
"single_word": false
},
"pad_token": {
"content": "<|endoftext|>",
"lstrip": false,
"normalized": false,
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}
}

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207
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{
"add_bos_token": false,
"add_prefix_space": false,
"added_tokens_decoder": {
"151643": {
"content": "<|endoftext|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
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"content": "<|im_start|>",
"lstrip": false,
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},
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},
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},
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"content": "<|repo_name|>",
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},
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"content": "<|file_sep|>",
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"special": false
}
},
"additional_special_tokens": [
"<|im_start|>",
"<|im_end|>",
"<|object_ref_start|>",
"<|object_ref_end|>",
"<|box_start|>",
"<|box_end|>",
"<|quad_start|>",
"<|quad_end|>",
"<|vision_start|>",
"<|vision_end|>",
"<|vision_pad|>",
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"<|video_pad|>"
],
"bos_token": null,
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"eos_token": "<|im_end|>",
"errors": "replace",
"extra_special_tokens": {},
"model_max_length": 32768,
"pad_token": "<|endoftext|>",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}

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special_tokens_map.json Normal file
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{
"additional_special_tokens": [
"<|im_start|>",
"<|im_end|>",
"<|object_ref_start|>",
"<|object_ref_end|>",
"<|box_start|>",
"<|box_end|>",
"<|quad_start|>",
"<|quad_end|>",
"<|vision_start|>",
"<|vision_end|>",
"<|vision_pad|>",
"<|image_pad|>",
"<|video_pad|>"
],
"eos_token": {
"content": "<|im_end|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"pad_token": {
"content": "<|endoftext|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
}
}

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test_model.py Normal file
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import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base_model_name = "Qwen/Qwen2.5-Coder-14B-Instruct"
adapter_path = "./outputs/qwen25-coder-n8n"
print("Loading base model...")
base_model = AutoModelForCausalLM.from_pretrained(
base_model_name,
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True
)
print("Loading adapter...")
model = PeftModel.from_pretrained(base_model, adapter_path)
tokenizer = AutoTokenizer.from_pretrained(base_model_name)
system_prompt = "You are an expert n8n workflow generation assistant. Your goal is to create valid, efficient, and error-free n8n workflow JSONs based on the user's requirements. Always output ONLY the valid JSON workflow."
user_input = "Create a workflow that gets data from a webhook and sends it to Slack. Also have a sticky note as documentation."
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_input}
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to(model.device)
print("Generating workflow...")
outputs = model.generate(**inputs, max_new_tokens=2048, do_sample=True, temperature=0.1)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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tokenizer.json Normal file
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version https://git-lfs.github.com/spec/v1
oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
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207
tokenizer_config.json Normal file
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{
"add_bos_token": false,
"add_prefix_space": false,
"added_tokens_decoder": {
"151643": {
"content": "<|endoftext|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151644": {
"content": "<|im_start|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151645": {
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"special": true
},
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},
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"content": "<|object_ref_end|>",
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"normalized": false,
"rstrip": false,
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"special": true
},
"151648": {
"content": "<|box_start|>",
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},
"151649": {
"content": "<|box_end|>",
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},
"151650": {
"content": "<|quad_start|>",
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},
"151651": {
"content": "<|quad_end|>",
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},
"151652": {
"content": "<|vision_start|>",
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},
"151653": {
"content": "<|vision_end|>",
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"special": true
},
"151654": {
"content": "<|vision_pad|>",
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"special": true
},
"151655": {
"content": "<|image_pad|>",
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"special": true
},
"151656": {
"content": "<|video_pad|>",
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"special": true
},
"151657": {
"content": "<tool_call>",
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"rstrip": false,
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"special": false
},
"151658": {
"content": "</tool_call>",
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"rstrip": false,
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"special": false
},
"151659": {
"content": "<|fim_prefix|>",
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"rstrip": false,
"single_word": false,
"special": false
},
"151660": {
"content": "<|fim_middle|>",
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"special": false
},
"151661": {
"content": "<|fim_suffix|>",
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},
"151662": {
"content": "<|fim_pad|>",
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"special": false
},
"151663": {
"content": "<|repo_name|>",
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"special": false
},
"151664": {
"content": "<|file_sep|>",
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"special": false
}
},
"additional_special_tokens": [
"<|im_start|>",
"<|im_end|>",
"<|object_ref_start|>",
"<|object_ref_end|>",
"<|box_start|>",
"<|box_end|>",
"<|quad_start|>",
"<|quad_end|>",
"<|vision_start|>",
"<|vision_end|>",
"<|vision_pad|>",
"<|image_pad|>",
"<|video_pad|>"
],
"bos_token": null,
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"eos_token": "<|im_end|>",
"errors": "replace",
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"pad_token": "<|endoftext|>",
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"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}

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