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Model: FutureMa/Qwen3-8B-Drama-Thinking
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---
license: apache-2.0
base_model: Qwen/Qwen3-8B
tags:
- qwen3
- thinking
- creative-writing
- screenwriting
- drama
- chain-of-thought
- reasoning
- ms-swift
- full-parameter-finetuning
datasets:
- custom-drama-thinking-dataset
language:
- en
- zh
library_name: transformers
pipeline_tag: text-generation
model-index:
- name: Qwen3-8B-Drama-Thinking
results:
- task:
type: text-generation
name: Creative Script Writing
metrics:
- type: thinking_depth
value: 9.0
name: Thinking Depth Score
- type: script_format
value: 9.0
name: Script Format Score
- type: dramatic_craft
value: 8.5
name: Dramatic Craft Score
---
# Qwen3-8B-Drama-Thinking
This model is a **full parameter fine-tuned** version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) on a custom drama thinking dataset with explicit creative reasoning chains.
## Model Description
- **Base Model**: Qwen3-8B (8 billion parameters)
- **Training Method**: Full Parameter Fine-tuning (NOT LoRA)
- **Training Framework**: [ms-swift](https://github.com/modelscope/ms-swift)
- **Training Data**: Custom Drama Thinking Dataset (6,319 samples, avg ~5,000 tokens)
- **Specialization**: Screenwriting with explicit `<think>...</think>` creative reasoning
- **Hardware**: 2x NVIDIA H100 80GB SXM5
- **Training Time**: 2 hours 46 minutes (3 epochs)
- **Training Cost**: ~$17.86
## Key Features
### 🎬 Professional Screenwriting Assistant
This model generates dramatic scripts with **explicit creative deliberation**:
-**Thinking Process Visible**: Uses `<think>...</think>` tags to show internal reasoning
-**Deep Character Psychology**: Analyzes motivations, defense mechanisms, subtext
-**Structural Planning**: Three-act structure, emotional arcs, pacing decisions
-**Visual Storytelling**: Symbolism, atmosphere, cinematographic choices
-**Professional Format**: Correct screenplay formatting (scene headers, action lines, dialogue)
### 📊 Performance Comparison
Compared to base Qwen3-8B:
| Metric | Base Model | Fine-Tuned | Improvement |
|--------|------------|------------|-------------|
| **Output Length** | 1,071 tokens | 3,874 tokens | **+262%** |
| **Thinking Depth** | 5/10 | 9/10 | **+80%** |
| **Creative Reasoning** | 500 tokens | 3,400 tokens | **+580%** |
| **Craft Analysis** | Generic | Professional | **Qualitative leap** |
### 🎯 Unique Value Proposition
> This is not just a text generator - it's a **creative thinking partner** that externalizes
> the entire screenwriting process: from title analysis to character psychology to structural
> planning to final execution.
## Training Details
### Training Configuration
```bash
Model: Qwen/Qwen3-8B
Template: qwen3_thinking
Training Type: Full Parameter (all 8B parameters)
Max Length: 8192 tokens (for long thinking chains)
Batch Size: 1 per device × 2 GPUs
Gradient Accum: 8 steps (effective batch size: 16)
Learning Rate: 1e-5
Epochs: 3
Optimization: DeepSpeed Zero3 + Gradient Checkpointing
Liger Kernel, BF16 mixed precision
Loss Scale: ignore_empty_think
GPU Memory: ~74.62 GB per H100 (stable)
```
### Dataset Characteristics
- **Samples**: 6,319 dramatic script continuations
- **Average Length**: ~5,000 tokens per sample
- **Max Length**: ~6,100 tokens
- **Format**: Conversations with `<think>...</think>` reasoning tags
- **Content**:
- Script opening scenes (title, description, initial dialogue)
- Extensive creative deliberation (3,000+ tokens of thinking)
- Script continuation with proper formatting
- **Style**: Dramatic, emotionally intense scenarios (conflicts, reconciliation, tragedy)
### Training Metrics
- **Final Loss**: 0.844
- **Average Loss**: 0.978
- **Loss Trajectory**: 1.602 (start) → 0.82-0.83 (end)
- **Training Speed**: ~8 seconds/iteration
- **Total Steps**: 1,185
- **Checkpoints**: 5 saved (400, 800, 900, 1000, 1185)
## Usage
### Quick Start (ms-swift)
```bash
# Install ms-swift
pip install ms-swift
# Inference (interactive mode)
swift infer \
--ckpt_dir FutureMa/Qwen3-8B-Drama-Thinking \
--template qwen3_thinking \
--max_new_tokens 4096 \
--temperature 0.7
```
### Python API
```python
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
from swift.llm import PtEngine, InferRequest, RequestConfig
# Initialize engine
engine = PtEngine(
model_id_or_path="FutureMa/Qwen3-8B-Drama-Thinking",
max_batch_size=1,
torch_dtype="bfloat16"
)
# Create prompt
prompt = """Title: The Reunion
Description: Two estranged siblings meet at their childhood home after 20 years.
INT. FAMILY LIVING ROOM - DAY
SARAH (35) stands by the window, looking out at the garden.
MICHAEL (38) enters, hesitant."""
# Generate (streaming)
messages = [{'role': 'user', 'content': prompt}]
request = InferRequest(messages=messages)
config = RequestConfig(max_tokens=4096, temperature=0.7, stream=True)
for response in engine.infer([request], config)[0]:
if response:
print(response.choices[0].delta.content, end='', flush=True)
```
### Using with Transformers (requires adapting checkpoint)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(
"FutureMa/Qwen3-8B-Drama-Thinking",
torch_dtype="bfloat16",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")
messages = [
{"role": "system", "content": "You are a creative screenwriter assistant specialized in dramatic storytelling with internal reasoning."},
{"role": "user", "content": "Write a scene about forgiveness..."}
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=4096, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Example Output
**Input Prompt:**
```
Title: The Last Apology
Description: A daughter arrives at her estranged father's deathbed...
```
**Model Output** (excerpt):
```
<think>
Okay. Let's go. I'm sitting here with the opening of *The Last Apology* fresh in my mind,
and I need to figure out exactly where this goes next—not just the next scene, but the next
emotional beat, the next structural pivot...
First, the title: *The Last Apology*. That's not just poetic—it's structural. It tells me
this story is about delayed recognition, about the finality of words left unsaid...
[3,400 tokens of deep creative analysis including:]
- Title deconstruction and thematic implications
- Character psychology analysis
- Three-act structure planning
- Visual language and symbolism
- Multiple narrative paths considered
- Professional screenwriting techniques
</think>
INT. HOSPITAL ROOM - NIGHT
ANNA (28), in a wrinkled business suit, hesitates at the doorway.
DAVID (65) lies in bed, breathing labored...
[Script continues with proper formatting]
```
## Intended Use
### ✅ Recommended Use Cases
1. **Screenwriting Education**: Learn professional creative thinking process
2. **Script Ideation**: Generate story frameworks and narrative alternatives
3. **Story Consulting**: Explore "what if" scenarios with explicit reasoning
4. **Creative Brainstorming**: Understand decision-making in storytelling
5. **Draft Development**: Plan structure before execution
### ❌ Not Recommended For
1. **Final Shooting Scripts**: Requires human refinement for production
2. **Comedy/Action Genres**: Training bias toward dramatic content
3. **Long-form Series**: Single-pass generation may lack consistency
4. **Immediate Production**: Dialogue needs naturalization
## Evaluation Results
### Quantitative Metrics (vs. Base Model)
| Aspect | Score | Base Model | Improvement |
|--------|-------|------------|-------------|
| **Thinking Depth** | 9/10 | 5/10 | +80% |
| **Script Format** | 9/10 | 8/10 | +13% |
| **Dramatic Craft** | 8.5/10 | 8/10 | +6% |
| **Character Psychology** | 9/10 | 6/10 | +50% |
| **Decision Transparency** | 9/10 | 5/10 | +80% |
| **Overall** | 8.1/10 | 6.9/10 | +17% |
> **Note on Methodology:**
> *These metrics are generated using an **LLM-as-a-Judge** framework (Claude) comparing the fine-tuned model against the base model.
### Qualitative Improvements
-**Professional Voice**: Sounds like experienced screenwriter
-**Structural Thinking**: Explicit three-act planning
-**Meta-Awareness**: "This isn't just a script. It's a reckoning."
-**Non-Linear Reasoning**: Considers alternatives, backtracks, refines
-**Craft-Oriented**: Explains why choices serve the story
## Limitations
1. **Thinking Verbosity**: Generates ~3,400 tokens of thinking (87% of output)
- May be excessive for quick tasks
- Consider using `max_new_tokens` to limit length
2. **Incomplete Execution**: Token budget consumed by thinking
- Many planned scenes not fully generated
- May need 6,000-8,000 token limit for complete scripts
3. **Dialogue Naturalness**: More direct/literary than conversational
- Training data style influences output
- May need post-processing for natural speech
4. **Training Data Bias**: Skews toward melodramatic scenarios
- Less suited for subtle/realistic dialogue
- Best for emotionally intense stories
## Training Insights
### What Made This Successful
1. **8192 Token Context**: Essential for capturing full thinking chains
- Initial assumption of 2048 would have truncated data
- Average sample length: ~5,000 tokens
2. **DeepSpeed Zero3**: Required (not optional)
- Single H100: Would need ~109-114 GB (OOM)
- Zero3 sharding: ~74.62 GB per card ✅
3. **Full Parameter Training**: Worth the cost
- Deeper capability transfer than LoRA
- Better thinking process internalization
- Cost: $17.86 (2.8 hours) vs ~$5 for LoRA
4. **Quality Training Data**: 6,319 long-form reasoning examples
- Actual creative process in `<think>` tags
- High-quality dramatic writing
## Citation
```bibtex
@misc{qwen3-drama-thinking-2025,
author = {FutureMa},
title = {Qwen3-8B-Drama-Thinking: Full Parameter Fine-tuning for Creative Screenwriting},
year = {2025},
publisher = {HuggingFace},
howpublished = {\url{https://huggingface.co/FutureMa/Qwen3-8B-Drama-Thinking}},
note = {Full parameter fine-tuning on 6,319 drama samples with explicit reasoning chains}
}
```
## News & Updates
**[2025-12-23]** 🎉 **DramaBench Dataset is now open-source!** Evaluate your drama script generation with our comprehensive 6-dimensional benchmark framework (Format Standards, Narrative Efficiency, Character Consistency, Emotional Depth, Logic Consistency, Conflict Handling).
- 📊 Dataset: [FutureMa/DramaBench](https://huggingface.co/datasets/FutureMa/DramaBench)
- 📄 Paper: [arXiv:2512.19012](https://arxiv.org/abs/2512.19012)
- 🌐 Demo: [dramabench.pages.dev](https://dramabench.pages.dev/)
---
## Acknowledgments
- **Base Model**: [Qwen Team](https://huggingface.co/Qwen) - Qwen3-8B
- **Training Framework**: [ms-swift](https://github.com/modelscope/ms-swift) - ModelScope SWIFT
- **Infrastructure**: [Lambda Cloud](https://lambdalabs.com/) - 2x H100 80GB SXM5
- **Dataset**: Custom Drama Thinking Dataset (6,319 samples)
## Model Card Contact
For questions or feedback:
- **HuggingFace**: [@FutureMa](https://huggingface.co/FutureMa)
- **GitHub Issues**: Report via ms-swift repository
---
**Training Date**: 2025-12-08
**Training Duration**: 2h 46m
**Model Size**: ~16GB (BF16 precision)
**Recommended VRAM**: 16GB+ for inference

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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- if message.tool_calls %}
{%- for tool_call in message.tool_calls %}
{%- if (loop.first and content) or (not loop.first) %}
{{- '\n' }}
{%- endif %}
{%- if tool_call.function %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{%- if tool_call.arguments is string %}
{{- tool_call.arguments }}
{%- else %}
{{- tool_call.arguments | tojson }}
{%- endif %}
{{- '}\n</tool_call>' }}
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{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- if enable_thinking is defined and enable_thinking is false %}
{{- '<think>\n\n</think>\n\n' }}
{%- endif %}
{%- endif %}

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