Instructions to use theblackcat102/Qwen3-1.7B-nq-baseline-finetune-5epochs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use theblackcat102/Qwen3-1.7B-nq-baseline-finetune-5epochs with Transformers:

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

pipe = pipeline("text-generation", model="theblackcat102/Qwen3-1.7B-nq-baseline-finetune-5epochs")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("theblackcat102/Qwen3-1.7B-nq-baseline-finetune-5epochs")
model = AutoModelForMultimodalLM.from_pretrained("theblackcat102/Qwen3-1.7B-nq-baseline-finetune-5epochs")
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 theblackcat102/Qwen3-1.7B-nq-baseline-finetune-5epochs with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "theblackcat102/Qwen3-1.7B-nq-baseline-finetune-5epochs"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "theblackcat102/Qwen3-1.7B-nq-baseline-finetune-5epochs",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/theblackcat102/Qwen3-1.7B-nq-baseline-finetune-5epochs

SGLang

How to use theblackcat102/Qwen3-1.7B-nq-baseline-finetune-5epochs 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 "theblackcat102/Qwen3-1.7B-nq-baseline-finetune-5epochs" \
    --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": "theblackcat102/Qwen3-1.7B-nq-baseline-finetune-5epochs",
		"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 "theblackcat102/Qwen3-1.7B-nq-baseline-finetune-5epochs" \
        --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": "theblackcat102/Qwen3-1.7B-nq-baseline-finetune-5epochs",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use theblackcat102/Qwen3-1.7B-nq-baseline-finetune-5epochs with Docker Model Runner:
```
docker model run hf.co/theblackcat102/Qwen3-1.7B-nq-baseline-finetune-5epochs
```

See axolotl config

axolotl version: 0.10.0

base_model: Abner0803/Qwen3-1.7B-nq-text-100k-with_pseudo_queries
datasets:
  - path: Abner0803/nq_text-with_pseudo_query-100k-gr
    data_files: data/icl_test.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"

dataset_processes: 6
streaming: false
shuffle_merged_datasets: true
output_dir: /home/theblackcat/ICLGR/checkpoints/Qwen3-1.7B-nq-baseline-finetune-5epochs
sequence_len: 2048
sample_packing: false
flash_attention: false
xformers_attention: false
flex_attention: false
sdp_attention: true
overrides_of_model_config:
  _attn_implementation: "sdpa"

gradient_accumulation_steps: 128
micro_batch_size: 1
num_epochs: 5

optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.0001
warmup_ratio: 0.1
weight_decay: 0.0
bf16: true
tf32: false
gradient_checkpointing: true

logging_steps: 50
save_strategy: steps
save_steps: 100
save_total_limit: 3

special_tokens:
  eos_token: "<|im_end|>"

val_set_size: 0.0
wandb_project: ICLGR-NQ
wandb_entity: abnerden0803-national-taiwan-university
wandb_watch:
wandb_name: qwen3-1.7b-nq-baseline-direct-finetune-5epochs
wandb_log_model:

home/theblackcat/ICLGR/checkpoints/Qwen3-1.7B-nq-baseline-finetune-5epochs

This model is a fine-tuned version of Abner0803/Qwen3-1.7B-nq-text-100k-with_pseudo_queries on the Abner0803/nq_text-with_pseudo_query-100k-gr 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: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 128
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: 206
training_steps: 2062

Training results

Framework versions

Transformers 4.52.3
Pytorch 2.9.0+cu128
Datasets 3.6.0
Tokenizers 0.21.4

Downloads last month: 3

Safetensors

Model size

2B params

Tensor type

F32

BF16

Model tree for theblackcat102/Qwen3-1.7B-nq-baseline-finetune-5epochs

Base model

Abner0803/Qwen3-1.7B-nq-text-100k-with_pseudo_queries

Finetuned

(1)

this model

theblackcat102
/

Qwen3-1.7B-nq-baseline-finetune-5epochs

home/theblackcat/ICLGR/checkpoints/Qwen3-1.7B-nq-baseline-finetune-5epochs

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for theblackcat102/Qwen3-1.7B-nq-baseline-finetune-5epochs

Dataset used to train theblackcat102/Qwen3-1.7B-nq-baseline-finetune-5epochs