Instructions to use prithivMLmods/Qwen3-0.6B-ft-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Qwen3-0.6B-ft-bf16 with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/Qwen3-0.6B-ft-bf16")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Qwen3-0.6B-ft-bf16")
model = AutoModelForCausalLM.from_pretrained("prithivMLmods/Qwen3-0.6B-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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use prithivMLmods/Qwen3-0.6B-ft-bf16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Qwen3-0.6B-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/Qwen3-0.6B-ft-bf16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Qwen3-0.6B-ft-bf16

SGLang

How to use prithivMLmods/Qwen3-0.6B-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/Qwen3-0.6B-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/Qwen3-0.6B-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/Qwen3-0.6B-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/Qwen3-0.6B-ft-bf16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/Qwen3-0.6B-ft-bf16 with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Qwen3-0.6B-ft-bf16
```

Qwen3-0.6B-ft-bf16

Qwen3-0.6B-ft-bf16 is a fine-tuned, moderately abliterated variant based on Qwen3-0.6B, the latest generation of large language models in the Qwen series. This version emphasizes improved context awareness and balanced behavioral flexibility, offering reliable performance across a wide range of natural language tasks. It integrates moderate experimental freedoms while maintaining the core strengths of Qwen3, including instruction-following, multilingual understanding, and strong reasoning capabilities.

Key Highlights:

Improved Context Awareness: Enhanced ability to maintain and utilize long-range conversational context, particularly useful for multi-turn dialogues, summarization, and document-based reasoning tasks.
Moderate Abliteration: Introduces moderate experimental freedoms to unlock more dynamic and expressive model behavior without compromising alignment or safety.
Thinking Mode Support: Capable of switching between deep reasoning mode and lightweight conversational mode for task-optimized performance.
Multilingual Proficiency: Supports 100+ languages and dialects for translation and instruction-following in multilingual settings.
Instruction and Agent Alignment: Performs well in instruction-following, tool integration, and agent-based interactions with external environments.

Quickstart with 🤗 Transformers

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

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Qwen3-0.6B-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 = "How does a rocket reach escape velocity?"
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 (thinking mode):
- temperature=0.6, top_p=0.95, top_k=20, min_p=0.0
Sampling (non-thinking mode):
- temperature=0.7, top_p=0.8, top_k=20, min_p=0.0
Max tokens:
- General: 32768
- Complex problems: 38912

Prompting Tips

Math:
Include: "Please reason step by step, and put your final answer within \boxed{}."
MCQs:
Format response as JSON:
{"answer": "B"}
Multi-Turn Chats:
Store only the final response in conversation history; omit internal reasoning.