đ Qwen2.5-CoderX-7B-v0.5
Qwen2.5-CoderX-7B-v0.5 is a highly specialized code generation model. It can generate complex, well - structured code with detailed explanations, acting as an AI coding assistant.
đ Quick Start
Prompting Strategies
Be specific and comprehensive when prompting the model. Clearly define the problem, desired functionalities, programming language, and constraints. Encourage structured output by using phrases like "Outline your plan before providing the code." For complex tasks, use iterative refinement.
Setting up the Environment (with Unsloth)
Install Unsloth and its dependencies in your Python environment. If using a CUDA - enabled GPU (recommended for performance), run pip install unsloth
. Refer to the Unsloth GitHub repository for the latest installation instructions and compatibility.
Running Inference
Here is a Python snippet to load and use the model with Unsloth for inference:
from unsloth import FastLanguageModel
import torch
model_name = "oscar128372/Qwen2.5-CoderX-7B-v0.5"
max_seq_length = 4096
dtype = None
load_in_4bit = True
model, tokenizer = FastLanguageModel.from_pretrained(
model_name=model_name,
max_seq_length=max_seq_length,
dtype=dtype,
load_in_4bit=load_in_4bit,
)
FastLanguageModel.for_inference(model)
tokenizer = get_chat_template(
tokenizer,
chat_template = "qwen-2.5",
)
messages = [
{"role": "user", "content": "Continue the fibonnaci sequence: 1, 1, 2, 3, 5, 8,"},
]
inputs = tokenizer.apply_chat_template(
messages,
tokenize = True,
add_generation_prompt = True,
return_tensors = "pt",
).to("cuda")
from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer, skip_prompt = True)
_ = model.generate(input_ids = inputs, streamer = text_streamer, max_new_tokens = 128,
use_cache = True, temperature = 1.5, min_p = 0.1)
⨠Features
- Complex Code Generation: Capable of handling intricate programming tasks like implementing physics engines or complex algorithms.
- Design Rationale and Planning: Often provides a "Plan of Implementation" before code generation.
- In - depth Explanations: Offers detailed explanations of the generated code.
- Adherence to Best Practices: Demonstrates good software engineering practices.
- Instruction Following: Accurately interprets and addresses complex prompts.
đĻ Installation
Install Unsloth and Dependencies
If you're using a CUDA - enabled GPU (recommended for performance), run the following command in your Python environment:
pip install unsloth
Refer to the Unsloth GitHub repository for the latest installation instructions and compatibility.
đģ Usage Examples
Basic Usage
from unsloth import FastLanguageModel
import torch
model_name = "oscar128372/Qwen2.5-CoderX-7B-v0.5"
max_seq_length = 4096
dtype = None
load_in_4bit = True
model, tokenizer = FastLanguageModel.from_pretrained(
model_name=model_name,
max_seq_length=max_seq_length,
dtype=dtype,
load_in_4bit=load_in_4bit,
)
FastLanguageModel.for_inference(model)
tokenizer = get_chat_template(
tokenizer,
chat_template = "qwen-2.5",
)
messages = [
{"role": "user", "content": "Continue the fibonnaci sequence: 1, 1, 2, 3, 5, 8,"},
]
inputs = tokenizer.apply_chat_template(
messages,
tokenize = True,
add_generation_prompt = True,
return_tensors = "pt",
).to("cuda")
from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer, skip_prompt = True)
_ = model.generate(input_ids = inputs, streamer = text_streamer, max_new_tokens = 128,
use_cache = True, temperature = 1.5, min_p = 0.1)
đ Documentation
Model Details
Property |
Details |
Developed by |
oscar128372 |
License |
Apache 2.0 |
Finetuned from |
unsloth/qwen2.5-coder-7b-instruct-bnb-4bit |
Model Version |
v0.5 |
Contact |
oscarlo121212@gmail.com |
Model Description
Qwen2.5-CoderX-7B-v0.5 is fine - tuned from unsloth/qwen2.5-coder-7b-instruct-bnb-4bit
. It is trained on a high - quality dataset of 46 examples, focusing on complex coding tasks.
Training Data
The model was fine - tuned on a proprietary dataset of 46 examples. These examples are of exceptional quality, high information density, and focus on complex tasks.
Evaluation
Qualitative Assessment: The model performs well on tasks similar to its training data, generating correct code, robust implementations, clear explanations, and well - designed plans.
Performance: It leverages the coding foundation of Qwen2.5 and the efficiency of Unsloth's 4 - bit training. Fine - tuning enhances its ability to solve complex coding problems.
Limitations and Bias
- Limited dataset size may affect generalization.
- The model may overfit to the training data style.
- It may inherit limitations from the base model.
- Human oversight is required for generated code.
- Generating long and complex responses may need significant computational resources.
Ethical Considerations
- The model is for responsible AI development.
- Users should be aware of potential misuse.
- The small dataset may have bias in reflecting diverse coding styles.
Acknowledgements
- Thanks to the Unsloth AI team for their library and base models.
- Thanks to the creators of the Qwen models.
đ License
This model is licensed under the Apache 2.0 license.