Instructions to use TeamPV/qwen1p7-qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TeamPV/qwen1p7-qa with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-1.7B")
model = PeftModel.from_pretrained(base_model, "TeamPV/qwen1p7-qa")

Transformers

How to use TeamPV/qwen1p7-qa with Transformers:

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

pipe = pipeline("text-generation", model="TeamPV/qwen1p7-qa")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("TeamPV/qwen1p7-qa")
model = AutoModelForMultimodalLM.from_pretrained("TeamPV/qwen1p7-qa")
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 TeamPV/qwen1p7-qa with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TeamPV/qwen1p7-qa"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TeamPV/qwen1p7-qa",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TeamPV/qwen1p7-qa

SGLang

How to use TeamPV/qwen1p7-qa 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 "TeamPV/qwen1p7-qa" \
    --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": "TeamPV/qwen1p7-qa",
		"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 "TeamPV/qwen1p7-qa" \
        --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": "TeamPV/qwen1p7-qa",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TeamPV/qwen1p7-qa with Docker Model Runner:
```
docker model run hf.co/TeamPV/qwen1p7-qa
```

See axolotl config

axolotl version: 0.13.0.dev0

base_model: Qwen/Qwen3-1.7B

# Quantization

bnb_config_kwargs:
  bnb_4bit_compute_dtype: bfloat16
  bnb_4bit_quant_type: nf4
  bnb_4bit_use_double_quant: true

datasets:
  - path: TeamPV/sharegpt-mistral-onr
    split: train
    type: chat_template
    conversation: messages  # Your dataset has 'messages' field
ds_type: json

# Use model's built-in chat template

val_set_size: 0.0
test_datasets:
  - path: TeamPV/sharegpt-mistral-onr
    split: validation
    type: chat_template
    conversation: messages
    
eval_sample_packing: false
eval_batch_size: 6
eval_steps: 30000
early_stopping_patience: 3


# Tokenization  
chat_template: tokenizer_default
sequence_len: 1200
pad_to_sequence_len: true
sample_packing: false

special_tokens:
  pad_token: "</s>"
  
# LoRA/DoRA
adapter: lora
lora_r: 32
lora_alpha: 64
lora_dropout: 0.05
lora_target_modules:
  - q_proj
  - k_proj
  - v_proj
  - o_proj
  - up_proj
  - down_proj
  - gate_proj
peft_use_dora: false
output_dir: /output/qwen1p7
use_tensorboard: true

# Training
micro_batch_size: 5
gradient_accumulation_steps: 1
num_epochs: 4
learning_rate: 0.00005
lr_scheduler: cosine
warmup_ratio: 0.10

# Optimizer
# optimizer: adamw_torch_fused
optimizer: adamw_bnb_8bit
bf16: true
fp16: false
# tf32: true

# Attention
flash_attention: true

# Memory
gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
  
# Checkpointing
save_steps: 30000


save_total_limit: 2
load_best_model_at_end: true

# Logging
logging_steps: 50

# HuggingFace Hub upload
hub_model_id: TeamPV/mistral-nemo-onr-dora-1p7  # Your HF repo name
hub_strategy: end  # Options: end, every_save, checkpoint, all_checkpoints
hf_use_auth_token: true

# Optional: make repo private

mistral-nemo-onr-dora-1p7

This model is a fine-tuned version of Qwen/Qwen3-1.7B on the TeamPV/sharegpt-mistral-onr dataset. It achieves the following results on the evaluation set:

Loss: 1.0987
Memory/max Active (gib): 14.15
Memory/max Allocated (gib): 14.15
Memory/device Reserved (gib): 14.87

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: 5e-05
train_batch_size: 5
eval_batch_size: 6
seed: 42
optimizer: Use OptimizerNames.ADAMW_BNB 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: 7353
training_steps: 73530

Training results

Training Loss	Epoch	Step	Validation Loss	Active (gib)	Allocated (gib)	Reserved (gib)
No log	0	0	3.6536	14.08	14.08	14.15
1.0537	1.6319	30000	1.1174	14.15	14.15	14.85
0.9286	3.2639	60000	1.0987	14.15	14.15	14.87

Framework versions

PEFT 0.17.1
Transformers 4.57.0
Pytorch 2.7.1+cu126
Datasets 4.0.0
Tokenizers 0.22.1

Downloads last month: 1

Model tree for TeamPV/qwen1p7-qa

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Adapter

(529)

this model