Instructions to use Abner0803/qwen_nq_compressed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Abner0803/qwen_nq_compressed with Transformers:

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

pipe = pipeline("text-generation", model="Abner0803/qwen_nq_compressed")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Abner0803/qwen_nq_compressed")
model = AutoModelForCausalLM.from_pretrained("Abner0803/qwen_nq_compressed")
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 Abner0803/qwen_nq_compressed with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Abner0803/qwen_nq_compressed"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Abner0803/qwen_nq_compressed",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Abner0803/qwen_nq_compressed

SGLang

How to use Abner0803/qwen_nq_compressed 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 "Abner0803/qwen_nq_compressed" \
    --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": "Abner0803/qwen_nq_compressed",
		"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 "Abner0803/qwen_nq_compressed" \
        --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": "Abner0803/qwen_nq_compressed",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Abner0803/qwen_nq_compressed with Docker Model Runner:
```
docker model run hf.co/Abner0803/qwen_nq_compressed
```

See axolotl config

axolotl version: 0.16.1

base_model: Qwen/Qwen3-1.7B-Base

datasets:
  - path: nq_text_compressed_axolotl/train_with_pseudo_axolotl.jsonl
    type: chat_template
    chat_template: tokenizer_default_fallback_chatml
    field_messages: conversations
    message_property_mappings:
      role: role
      content: content
    roles:
      assistant:
        - assistant
        - gpt
        - model
      user:
        - user
        - human
      system:
        - system

roles_to_train: ["assistant"]
train_on_eos: "turn"

shuffle_merged_datasets: true
output_dir: ./checkpoint/Qwen3-1.7B-nq_text_compressed-with_pseudo-lr1e-4-10epochs
sequence_len: 1024
sample_packing: true
pad_to_sequence_len: true
flash_attention: false
xformers_attention: false
flex_attention: false
sdp_attention: true

gradient_accumulation_steps: 32
micro_batch_size: 4
dataloader_num_workers: 2
num_epochs: 10

optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 1e-4
warmup_ratio: 0.1
weight_decay: 0.0
bf16: true
tf32: false
gradient_checkpointing: true

logging_steps: 1000
save_strategy: epoch
save_total_limit: 1

special_tokens:
  eos_token: <|im_end|>

wandb_project: "ICLGR-NQ"
wandb_entity: "abnerden0803-national-taiwan-university"
wandb_name: "qwen3-1.7b-nq_text_compressed-pseudo-lr1e-4-10epochs"
wandb_log_model:
wandb_watch:

val_set_size: 0.0

checkpoint/Qwen3-1.7B-nq_text_compressed-with_pseudo-lr1e-4-10epochs

This model is a fine-tuned version of Qwen/Qwen3-1.7B-Base on the nq_text_compressed_axolotl/train_with_pseudo_axolotl.jsonl dataset.

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: 0.0001
train_batch_size: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 32
total_train_batch_size: 128
optimizer: Use OptimizerNames.ADAMW_TORCH 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: 319
training_steps: 3190

Training results

Framework versions

Transformers 5.5.0
Pytorch 2.8.0+cu128
Datasets 4.5.0
Tokenizers 0.22.2

Downloads last month: 2

Safetensors

Model size

2B params

Tensor type

F32

BF16

Model tree for Abner0803/qwen_nq_compressed

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

(381)

this model