Qwen3-4B-ft-bf16

Qwen3-4B-ft-bf16 is a fine-tuned, moderately abliterated version of the Qwen3-4B model. Designed for enhanced context awareness and controlled expressiveness, this model balances precision with creativity across a wide range of tasks—from complex reasoning to natural dialogue, code generation, and multilingual understanding.

Key Features:

• Improved Context Awareness
  Retains and utilizes long-range contextual information effectively, making it ideal for long-form conversations, document understanding, and summarization tasks.

• Moderate Abliteration
  Introduces measured behavioral flexibility that enhances creativity and adaptability while maintaining reliability, alignment, and safety in outputs.

• Dual Thinking Modes
  Supports dynamic switching between thinking mode (for math, logic, and coding) and non-thinking mode (for general-purpose conversations), ensuring optimal task matching.

• Multilingual Mastery
  Excels in over 100 languages and dialects for translation, multilingual chat, and cross-lingual reasoning.

• Tool-Ready Agent Capabilities
  Designed to integrate with tool APIs and complex workflows, with consistent performance in both thinking and non-thinking contexts.

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Quickstart with Hugging Face Transformers🤗

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

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Qwen3-4B-ft-bf16"

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

# Define input
prompt = "Describe how renewable energy impacts economic development."
messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True
)

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

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

# Parse thinking content
try:
    index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
    index = 0

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

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

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Best Practices

• Sampling Settings:

  • Thinking mode: temperature=0.6, top_p=0.95, top_k=20
  • Non-thinking mode: temperature=0.7, top_p=0.8, top_k=20

• Token Length:

  • Standard: 32768 tokens
  • Extended Reasoning Tasks: up to 38912 tokens

• Prompt Design:

  • Math Problems: Add "Please reason step by step, and put your final answer within \boxed{}."
  • MCQs: Format answers as {"answer": "B"} for easy parsing.
  • Multi-turn: Omit thinking logs in conversation history for cleaner context.