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.