107 lines
3.4 KiB
Markdown
107 lines
3.4 KiB
Markdown
---
|
|
license: apache-2.0
|
|
language:
|
|
- en
|
|
base_model:
|
|
- Qwen/Qwen3-4B
|
|
pipeline_tag: text-generation
|
|
library_name: transformers
|
|
tags:
|
|
- moe
|
|
- moderately abliterated variant
|
|
- text-generation-inference
|
|
---
|
|
|
|
|
|
|
|
# **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.
|
|
|
|
---
|
|
|
|
## Quickstart with Hugging Face Transformers🤗
|
|
|
|
```bash
|
|
pip install transformers==4.51.3
|
|
pip install huggingface_hub[hf_xet]
|
|
```
|
|
|
|
```python
|
|
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)
|
|
```
|
|
|
|
---
|
|
|
|
## 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. |