Instructions to use prithivMLmods/Qwen3-1.7B-ft-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Qwen3-1.7B-ft-bf16 with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/Qwen3-1.7B-ft-bf16")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Qwen3-1.7B-ft-bf16")
model = AutoModelForCausalLM.from_pretrained("prithivMLmods/Qwen3-1.7B-ft-bf16")
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 prithivMLmods/Qwen3-1.7B-ft-bf16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Qwen3-1.7B-ft-bf16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Qwen3-1.7B-ft-bf16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Qwen3-1.7B-ft-bf16

SGLang

How to use prithivMLmods/Qwen3-1.7B-ft-bf16 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 "prithivMLmods/Qwen3-1.7B-ft-bf16" \
    --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": "prithivMLmods/Qwen3-1.7B-ft-bf16",
		"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 "prithivMLmods/Qwen3-1.7B-ft-bf16" \
        --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": "prithivMLmods/Qwen3-1.7B-ft-bf16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/Qwen3-1.7B-ft-bf16 with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Qwen3-1.7B-ft-bf16
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen3-1.7B-ft-bf16

Qwen3-1.7B-ft-bf16 is a fine-tuned, moderately abliterated variant of the Qwen3-1.7B model. Built upon the robust Qwen3 architecture, this version emphasizes improved context awareness and moderate behavioral flexibility, while maintaining high standards in reasoning, instruction-following, and multilingual performance. It is designed to perform consistently across general-purpose dialogue, technical reasoning, creative writing, and multilingual tasks.

Key Highlights:

Improved Context Awareness: Retains and utilizes long-span contextual information effectively, making it suitable for long conversations, document analysis, and summarization.
Moderate Abliteration: Introduces controlled experimental freedoms for enhanced expressiveness and adaptability, while preserving safety and alignment.
Dual-Mode Thinking Support: Supports dynamic switching between deep logical reasoning and efficient casual dialogue, making it task-aware and context-adaptive.
Multilingual Excellence: Robust across 100+ languages, handling translation, multilingual instruction, and language-specific tasks seamlessly.
Tool and Agent Integration: Performs well in agent-driven scenarios and can interface with tools and APIs in both thinking and non-thinking modes.

Quickstart with 🤗 Transformers

pip install transformers==4.51.3
pip install huggingface_hub[hf_xet]

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Qwen3-1.7B-ft-bf16"

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# Define prompt and apply chat template
prompt = "Explain why the sky appears blue during the day and red at sunset."
messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True
)

# Tokenize input
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# Generate response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()

# Optional: Separate thinking content
try:
    index = len(output_ids) - output_ids[::-1].index(151668)  # token ID for </think>
except ValueError:
    index = 0

thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")

print("thinking content:", thinking_content)
print("content:", content)

Recommended Settings

Sampling Parameters:
- Thinking Mode: temperature=0.6, top_p=0.95, top_k=20, min_p=0.0
- Non-Thinking Mode: temperature=0.7, top_p=0.8, top_k=20, min_p=0.0
Max Token Length:
- Standard Tasks: 32768
- Complex/Extended Tasks: 38912

Prompting Guidelines

Math Problems:
"Please reason step by step, and put your final answer within \boxed{}."
MCQs:
Format: {"answer": "C"}
Dialogues:
Include only final responses in history; omit internal thinking logs for efficiency.