Instructions to use AlekseyKorshuk/rewriter-v0.3-axolotl-3b-qwen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AlekseyKorshuk/rewriter-v0.3-axolotl-3b-qwen with Transformers:

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

pipe = pipeline("text-generation", model="AlekseyKorshuk/rewriter-v0.3-axolotl-3b-qwen")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("AlekseyKorshuk/rewriter-v0.3-axolotl-3b-qwen")
model = AutoModelForMultimodalLM.from_pretrained("AlekseyKorshuk/rewriter-v0.3-axolotl-3b-qwen")
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 Settings

vLLM

How to use AlekseyKorshuk/rewriter-v0.3-axolotl-3b-qwen with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AlekseyKorshuk/rewriter-v0.3-axolotl-3b-qwen"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AlekseyKorshuk/rewriter-v0.3-axolotl-3b-qwen",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AlekseyKorshuk/rewriter-v0.3-axolotl-3b-qwen

SGLang

How to use AlekseyKorshuk/rewriter-v0.3-axolotl-3b-qwen 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 "AlekseyKorshuk/rewriter-v0.3-axolotl-3b-qwen" \
    --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": "AlekseyKorshuk/rewriter-v0.3-axolotl-3b-qwen",
		"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 "AlekseyKorshuk/rewriter-v0.3-axolotl-3b-qwen" \
        --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": "AlekseyKorshuk/rewriter-v0.3-axolotl-3b-qwen",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AlekseyKorshuk/rewriter-v0.3-axolotl-3b-qwen with Docker Model Runner:
```
docker model run hf.co/AlekseyKorshuk/rewriter-v0.3-axolotl-3b-qwen
```

See axolotl config

axolotl version: 0.4.1

base_model: Qwen/Qwen2.5-3B-Instruct

strict: false


chat_template: chatml
datasets:
  - path: AlekseyKorshuk/rewriter-v0.3-axolotl
    type: chat_template
    field_messages: messages
    message_field_role: role
    message_field_content: content
    roles:
      user:
        - user
      assistant:
        - assistant

val_set_size: 0.05
output_dir: ./outputs/out

eval_table_size: 0
eval_max_new_tokens: 1

sequence_len: 2048
sample_packing: false
eval_sample_packing: false
pad_to_sequence_len: false

wandb_project: ai-seo-rewriter
wandb_entity:
wandb_watch:
wandb_name: rewriter-v0.3-3b
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 8
eval_batch_size: 4
num_epochs: 1
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 7.5e-6

train_on_inputs: false
group_by_length: false
bf16: true
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_ratio: 0.1
evals_per_epoch: 10
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
  - full_shard
  - auto_wrap
fsdp_config:
  fsdp_limit_all_gathers: true
  fsdp_sync_module_states: true
  fsdp_offload_params: true
  fsdp_use_orig_params: false
  fsdp_cpu_ram_efficient_loading: true
  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
  fsdp_transformer_layer_cls_to_wrap: Qwen2DecoderLayer
  fsdp_state_dict_type: FULL_STATE_DICT



hub_model_id: AlekseyKorshuk/rewriter-v0.3-axolotl-3b-qwen

rewriter-v0.3-axolotl-3b-qwen

This model is a fine-tuned version of Qwen/Qwen2.5-3B-Instruct on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.4408

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: 7.5e-06
train_batch_size: 8
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 4
total_train_batch_size: 256
total_eval_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 8
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
3.0777	0.0112	1	3.1946
1.7763	0.1011	9	1.6846
1.5442	0.2022	18	1.5214
1.4478	0.3034	27	1.4789
1.4587	0.4045	36	1.4637
1.3785	0.5056	45	1.4536
1.4107	0.6067	54	1.4478
1.4244	0.7079	63	1.4429
1.4227	0.8090	72	1.4408
1.4329	0.9101	81	1.4408

Framework versions

Transformers 4.45.2
Pytorch 2.4.0+cu121
Datasets 3.0.1
Tokenizers 0.20.1

Downloads last month: 3

Safetensors

Model size

3B params

Tensor type

F32

Model tree for AlekseyKorshuk/rewriter-v0.3-axolotl-3b-qwen

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-3B-Instruct

Finetuned

(1345)

this model