Instructions to use textcleanlm/1.7B-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use textcleanlm/1.7B-SFT with Transformers:

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

pipe = pipeline("text-generation", model="textcleanlm/1.7B-SFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("textcleanlm/1.7B-SFT")
model = AutoModelForCausalLM.from_pretrained("textcleanlm/1.7B-SFT")
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 textcleanlm/1.7B-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "textcleanlm/1.7B-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "textcleanlm/1.7B-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/textcleanlm/1.7B-SFT

SGLang

How to use textcleanlm/1.7B-SFT 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 "textcleanlm/1.7B-SFT" \
    --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": "textcleanlm/1.7B-SFT",
		"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 "textcleanlm/1.7B-SFT" \
        --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": "textcleanlm/1.7B-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use textcleanlm/1.7B-SFT with Docker Model Runner:
```
docker model run hf.co/textcleanlm/1.7B-SFT
```

See axolotl config

axolotl version: 0.11.0

base_model: Qwen/Qwen3-1.7B

# plugins:
#   - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
strict: false

# plugins:
#   - axolotl.integrations.liger.LigerPlugin

# liger_rope: true
# liger_rms_norm: true
# liger_glu_activation: true
# liger_layer_norm: true
# liger_fused_linear_cross_entropy: true

datasets:
  - path: sumuks/essential-web-v1.0-sample-100M-with-cleaned-responses-sft
    type: chat_template
    field_messages: conversations
    split: train
val_set_size: 0.05
dataset_prepared_path: dataset/prepared_dataset_1.7b

train_on_inputs: false
output_dir: ./output/1.7B-Instruct-Tuned-New-Data
chat_template: qwen3
sequence_len: 8192
sample_packing: true
eval_sample_packing: true
# pad_to_sequence_len: true

wandb_project: essential-web-sft
wandb_name: qwen3-1.7b-sft-new-data

gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
flash_attention: true
micro_batch_size: 1
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5
num_epochs: 1

load_best_model_at_end: true
metric_for_best_model: loss
greater_is_better: false

early_stopping_patience: 3
bf16: auto
tf32: true

logging_steps: 5

deepspeed: ./configs_prod/zero3.json

save_steps: 500
eval_steps: 500

warmup_ratio: 0.05
# save_first_step: true

output/1.7B-Instruct-Tuned-New-Data

This model is a fine-tuned version of Qwen/Qwen3-1.7B on the sumuks/essential-web-v1.0-sample-100M-with-cleaned-responses-sft dataset. It achieves the following results on the evaluation set:

Loss: 0.3669

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: 2e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 4
total_train_batch_size: 8
total_eval_batch_size: 2
optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 164
training_steps: 3297

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0	0	0.8829
0.3689	0.1517	500	0.4088
0.3919	0.3033	1000	0.3952
0.386	0.4550	1500	0.3839
0.409	0.6066	2000	0.3755
0.3473	0.7583	2500	0.3694
0.3518	0.9099	3000	0.3669

Framework versions

Transformers 4.53.1
Pytorch 2.7.1+cu126
Datasets 3.6.0
Tokenizers 0.21.2

Downloads last month: 3

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for textcleanlm/1.7B-SFT

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Finetuned

(748)

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