README.md

---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen3-0.6B
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- moe
- moderately abliterated variant
---

![FMjPew6Vjrp4FvKe1Uz_T.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/APdJg_jMM2TAyA_mYTmCC.png)

# **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

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

```python
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.