107 lines
3.4 KiB
Markdown
107 lines
3.4 KiB
Markdown
---
|
|
license: apache-2.0
|
|
datasets:
|
|
- Jackrong/DeepSeek-V4-Distill-8000x
|
|
language:
|
|
- en
|
|
base_model:
|
|
- Qwen/Qwen2-0.5B-Instruct
|
|
pipeline_tag: text-generation
|
|
tags:
|
|
- slm
|
|
- trl
|
|
- text-generation-inference
|
|
- reasoning
|
|
- thinking
|
|
- chat
|
|
---
|
|
|
|
# Dragon-1.5-0.5B (qwen2-0.5b-reasoning v3.1.1)
|
|
|
|
|
|
|
|
Dragon is a lightweight general reasoning model built upon the base [Qwen2-0.5B-instruct model](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct). It offers accurate and quick text generation on a variety of topics(including code related problems).
|
|
It's small size (0.5B parameters) allows it to run comfortably on most laptop/commercial grade GPUs.
|
|
This model also offers Q/A and subject matter expert capabilities on general and code related subjects.
|
|
|
|
The Dragon-1.5 is the next generation for the [Dragon-1/1.5 series](https://huggingface.co/collections/DireDreadlord/dragon-1-15) which incorporates high-end reasoning capabilities into the standard Qwen2 architecture.
|
|
|
|
The 0.5B variant has been SFT trained on general/code reasoning traces found [here](https://huggingface.co/datasets/Jackrong/DeepSeek-V4-Distill-8000x) with further RL training carried out via. a GRPO algorithm. This endows the model with enhanced reasoning capabilities which allows it to serve higher quality and hallucination-free generations.
|
|
|
|
---
|
|
|
|
|
|
**Estimated parameters:** ~0.5B
|
|
|
|
**Architecture:** Qwen2
|
|
|
|
**Intended use:** Advanced reasoning, instruction following along with enhanced code snippet and long form code generation
|
|
|
|
---
|
|
|
|
|
|
## Training data
|
|
**Phase-1**
|
|
- Source: deepseek-v4-distill-8000x dataset (https://huggingface.co/datasets/Jackrong/DeepSeek-V4-Distill-8000x)
|
|
- Rows: ~7,716 rows templated with a custom .jinja chat format
|
|
- Training: trained for 4,000 steps on an A10 (24GB VRAM)
|
|
|
|
**Phase-2**
|
|
- Source: deepseek-v4-reasoning-code-2500 dataset (https://huggingface.co/datasets/Banaxi-Tech/Deepseek-V4-Reasoning-Code-2500)
|
|
- Rows: ~7,716 rows templated with a custom .jinja chat format
|
|
- Training: trained via. GRPO for 350 steps on an A10 (24GB VRAM)
|
|
|
|
|
|
## Usage
|
|
|
|
Install requirements:
|
|
|
|
```bash
|
|
pip install -r requirements.txt
|
|
pip install transformers datasets accelerate safetensors
|
|
```
|
|
|
|
|
|
## Usage (Hugging Face Hub)
|
|
You can load it directly from HuggingFace:
|
|
|
|
```python
|
|
import torch
|
|
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
|
|
|
|
|
|
device = "cuda" if torch.cuda.is_available() else "cpu"
|
|
|
|
model_id = "DireDreadlord/Dragon-1.5-0.5B"
|
|
model = AutoModelForCausalLM.from_pretrained(
|
|
model_id,
|
|
device_map="auto",
|
|
dtype="auto"
|
|
)
|
|
model.to(device)
|
|
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
|
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=False)
|
|
|
|
prompt = "Solve the leetcode problem 1: two sum using the hash map technique"
|
|
|
|
input_ids = tokenizer.apply_chat_template(
|
|
[{"role": "user", "content": prompt}],
|
|
add_generation_prompt=True,
|
|
return_tensors="pt",
|
|
tokenize=True,
|
|
)["input_ids"].to(device)
|
|
|
|
output = model.generate(
|
|
input_ids,
|
|
do_sample=True,
|
|
temperature=0.4,
|
|
top_k=50,
|
|
repetition_penalty=1.05,
|
|
max_new_tokens=2048,
|
|
streamer=streamer,
|
|
)
|
|
```
|
|
**For optimal long-form generation(with reasoning), set `max_new_tokens=2048`**
|
|
|
|
## Limitations
|
|
- Model for experimental use only; users should employ it as such under license. |