Instructions to use ToastyPigeon/qwen14-creative-epoch1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ToastyPigeon/qwen14-creative-epoch1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ToastyPigeon/qwen14-creative-epoch1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ToastyPigeon/qwen14-creative-epoch1")
model = AutoModelForCausalLM.from_pretrained("ToastyPigeon/qwen14-creative-epoch1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ToastyPigeon/qwen14-creative-epoch1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ToastyPigeon/qwen14-creative-epoch1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ToastyPigeon/qwen14-creative-epoch1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ToastyPigeon/qwen14-creative-epoch1

SGLang

How to use ToastyPigeon/qwen14-creative-epoch1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ToastyPigeon/qwen14-creative-epoch1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ToastyPigeon/qwen14-creative-epoch1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ToastyPigeon/qwen14-creative-epoch1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ToastyPigeon/qwen14-creative-epoch1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ToastyPigeon/qwen14-creative-epoch1 with Docker Model Runner:
```
docker model run hf.co/ToastyPigeon/qwen14-creative-epoch1
```

See axolotl config

axolotl version: 0.8.0.dev0

# === Model Configuration ===
base_model: ToastyPigeon/qwen2.5-14b-1m-unalign-v2  
load_in_8bit: false
load_in_4bit: false

# === Training Setup ===
num_epochs: 1
micro_batch_size: 2
gradient_accumulation_steps: 1
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true

# === Evaluation ===
val_set_size: 50
evals_per_epoch: 10
eval_table_size:
eval_max_new_tokens: 256
eval_sample_packing: true

# === Hyperparameter Configuration ===
optimizer: apollo_adamw_layerwise
# Apollo-mini configuration:
optim_args: "proj=random,rank=1,scale=128.0,scale_type=tensor,update_proj_gap=200"
# Regular Apollo configuration:
# optim_args: 
optim_target_modules: all_linear
learning_rate: 1e-5
lr_scheduler: cosine
weight_decay: 0.01
warmup_ratio: 0.05

# === Data Configuration ===
datasets:
  - path: Columbidae/merge-glue-4k
    data_files: conversation-glue-4k.json
    type: chat_template
    split: train
    field_messages: conversations
    message_field_role: from
    message_field_content: value
  - path: Columbidae/merge-glue-4k
    data_files: completion-glue-4k.json
    type: completion
    split: train
    field: text

dataset_prepared_path: last_run_prepared
chat_template: tokenizer_default
# Example custom template:
# chat_template: jinja
# chat_template_jinja: |
#   {{- bos_token }}{%- for message in messages %}
#   {%- if message['role'] == 'system' %}
#   {{- '[SYSTEM_PROMPT]' + message['content'] + '[/SYSTEM_PROMPT]' }}
#   {%- elif message['role'] == 'user' %}
#   {{- '[INST]' + message['content'] + '[/INST]' }}
#   {%- elif message['role'] == 'assistant' %}
#   {{- message['content'] + eos_token }}
#   {%- endif %}
#   {%- endfor %}

# === Plugins ===
plugins:
  - axolotl.integrations.liger.LigerPlugin
#  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin

# === Hardware Optimization ===
gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_fused_linear_cross_entropy: true
#unsloth_cross_entropy_loss: true
#cut_cross_entropy: true
# Only if using multiple GPUs:
#deepspeed: axolotl/deepspeed_configs/zero3_bf16.json

# === Wandb Tracking ===
wandb_project: Qwen-14b-Revisit
# wandb_entity: [WANDB_ENTITY]
# wandb_name: [WANDB_RUN_NAME]

# === MLflow Tracking ===
#mlflow_tracking_uri: https://public-tracking.mlflow-e00zzfjq11ky6jcgtv.backbone-#e00bgn6e63256prmhq.msp.eu-north1.nebius.cloud
#mlflow_experiment_name: [EXPERIMENT NAME]  # e.g. "ms-24b-rp-inkmixv2-apollo"
#hf_mlflow_log_artifacts: true

# === Checkpointing ===
saves_per_epoch: 2
save_total_limit: 2

# === Advanced Settings ===
output_dir: ./ckpts
bf16: auto
flash_attention: true
train_on_inputs: false
group_by_length: false
save_safetensors: true
logging_steps: 1
gc_steps: 10
seed: 69

ckpts

This model is a fine-tuned version of ToastyPigeon/qwen2.5-14b-1m-unalign-v2 on the Columbidae/merge-glue-4k and the Columbidae/merge-glue-4k datasets. It achieves the following results on the evaluation set:

Loss: 2.1191

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: 1e-05
train_batch_size: 2
eval_batch_size: 2
seed: 69
optimizer: Use OptimizerNames.APOLLO_ADAMW_LAYERWISE with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=proj=random,rank=1,scale=128.0,scale_type=tensor,update_proj_gap=200
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 88
num_epochs: 1.0

Training results

Training Loss	Epoch	Step	Validation Loss
2.2922	0.0006	1	2.2110
2.2515	0.1001	178	2.1361
1.6572	0.2002	356	2.1319
2.5563	0.3003	534	2.1276
2.2536	0.4004	712	2.1223
2.1665	0.5006	890	2.1207
2.0819	0.6007	1068	2.1201
2.1335	0.7008	1246	2.1196
1.9904	0.8009	1424	2.1192
2.2689	0.9010	1602	2.1191

Framework versions

Transformers 4.50.0.dev0
Pytorch 2.6.0+cu124
Datasets 3.2.0
Tokenizers 0.21.0

Downloads last month: 1

Safetensors

Model size

15B params

Tensor type

BF16

Model tree for ToastyPigeon/qwen14-creative-epoch1

Base model

ToastyPigeon/qwen2.5-14b-1m-unalign-v2

Finetuned

(4)

this model