Instructions to use open-sci/sft__ot30k_Qwen3-1.7B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use open-sci/sft__ot30k_Qwen3-1.7B-Base with Transformers:

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

pipe = pipeline("text-generation", model="open-sci/sft__ot30k_Qwen3-1.7B-Base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("open-sci/sft__ot30k_Qwen3-1.7B-Base")
model = AutoModelForCausalLM.from_pretrained("open-sci/sft__ot30k_Qwen3-1.7B-Base", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use open-sci/sft__ot30k_Qwen3-1.7B-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "open-sci/sft__ot30k_Qwen3-1.7B-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "open-sci/sft__ot30k_Qwen3-1.7B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/open-sci/sft__ot30k_Qwen3-1.7B-Base

SGLang

How to use open-sci/sft__ot30k_Qwen3-1.7B-Base 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 "open-sci/sft__ot30k_Qwen3-1.7B-Base" \
    --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": "open-sci/sft__ot30k_Qwen3-1.7B-Base",
		"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 "open-sci/sft__ot30k_Qwen3-1.7B-Base" \
        --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": "open-sci/sft__ot30k_Qwen3-1.7B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use open-sci/sft__ot30k_Qwen3-1.7B-Base with Docker Model Runner:
```
docker model run hf.co/open-sci/sft__ot30k_Qwen3-1.7B-Base
```

sftf679a5c592c8dffbQwen-Qwen3-1-7B-Basfa2-qwen3-stepsQwen--Qwen3-1.7B-Bas

This model was trained from scratch on the /gpfs/scratch/ehpc524/ot/hf_hub/datasets/open-thoughts_open_thoughts3-1.2_m_30000_samples/default/0.0.0/f679a5c592c8dffb 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: 4e-05
train_batch_size: 1
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 32
gradient_accumulation_steps: 4
total_train_batch_size: 128
total_eval_batch_size: 256
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 5.0

Training results

Framework versions

Transformers 4.55.0
Pytorch 2.10.0+cu128
Datasets 4.8.4
Tokenizers 0.21.4

Downloads last month: 177

Safetensors

Model size

2B params

Tensor type

BF16

Collection including open-sci/sft__ot30k_Qwen3-1.7B-Base

MV post-training 1.7B checkpoints

Collection

92 items • Updated 1 day ago

sft__f679a5c592c8dffb__Qwen-Qwen3-1-7B-Bas__fa2-qwen3-steps__Qwen--Qwen3-1.7B-Bas

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Collection including open-sci/sft__ot30k_Qwen3-1.7B-Base

sftf679a5c592c8dffbQwen-Qwen3-1-7B-Basfa2-qwen3-stepsQwen--Qwen3-1.7B-Bas