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---
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
---
# **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. |