This repo contains the full precision source code, in "safe tensors" format to generate GGUFs, GPTQ, EXL2, AWQ, HQQ and other formats. The source code can also be used directly.
This model contains Brainstorm 20x, combined with Microsoft's 7B Coder (instruct model):
https://huggingface.co/microsoft/NextCoder-7B
Information on the 7B Microsoft NextCoder model below, followed by Brainstorm 20x adapter (by DavidAU) and then a complete help section for running LLM / AI models.
The Brainstorm adapter improves code generation, and unique code solving abilities.
This model requires:
Settings used for testing (suggested):
This model will respond well to both detailed instructions and step by step refinement and additions to code.
As this is an instruct model, it will also benefit from a detailed system prompt too.
For simpler coding problems, lower quants will work well; but for complex/multi-step problem solving suggest Q6 or Q8.
NextCoder: Robust Adaptation of Code LMs to Diverse Code Edits (ICML'2025)
NextCoder is the latest series of Code-Editing large language models developed using the Qwen2.5-Coder Instruct variants as base and trained with novel Selective Knowledge Transfer finetuning methodology as introduced in the paper. NextCoder family model comes in 3 different sizes 7, 14, 32 billion parameters, to meet the needs of different developers. Following are the key improvements:
This repo contains the NextCoder-7B model, which has the following features:
For more details, please refer to our blog, GitHub, Paper.
The code of NextCoder is based on Qwen2.5 base models which has been in the latest Hugging face transformers and we advise you to use the latest version of transformers.
With transformers<4.37.0, you will encounter the following error:
KeyError: 'qwen2'
Here provides a code snippet with apply_chat_template to show you how to load the tokenizer and model and how to generate contents.
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "microsoft/NextCoder-7B"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = """
Fix the following function that divides two numbers to handle all the edge cases:
def divide(a, b)
returm a/b
"""
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=1024
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
| Models | HUMANEVALFIX | CANITEDIT | AIDER | POLYGLOT |
|---|---|---|---|---|
| QwenCoder-2.5-3B | 73.2 | 37.1 | 36.8 | - |
| QwenCoder-2.5-3B-LoRA | 64.6 | 36.2 | 35.8 | - |
| QwenCoder-2.5-3B-SFT | 76.2 | 32.4 | 30.1 | - |
| NextCoder-3B | 75.6 | 42.4 | 37.6 | - |
| QwenCoder-2.5-7B | 73.8 | 48.1 | 59.4 | - |
| QwenCoder-2.5-7B-LoRA | 70.7 | 44.3 | 40.6 | - |
| QwenCoder-2.5-7B-SFT | 70.1 | 36.7 | 48.9 | - |
| NextCoder-7B | 81.1 | 50.5 | 65.7 | - |
| QwenCoder-2.5-14B | 87.8 | 58.1 | 66.9 | 9.3 |
| QwenCoder-2.5-14B-LoRA | 78.0 | 50.9 | 66.2 | 5.3 |
| QwenCoder-2.5-14B-SFT | 79.9 | 42.4 | 36.8 | 3.1 |
| NextCoder-14B | 89.8 | 60.2 | 72.2 | 12.2 |
| QwenCoder-2.5-32B | 90.2 | 61.0 | 72.9 | 16.4 |
| QwenCoder-2.5-32B-LoRA | 82.3 | 52.4 | 60.2 | 6.7 |
| QwenCoder-2.5-32B-SFT | 81.7 | 49.5 | 66.9 | 8.4 |
| NextCoder-32B | 88.9 | 62.4 | 74.7 | 23.6 |
Comparison of base QwenCoder-2.5 models of different sizes and their SELEKT-enhanced versions across three code editing benchmarks.
Detailed evaluation results are reported in this 📑 paper.
See more here:
https://huggingface.co/microsoft/NextCoder-7B
Brainstorm 20x
The BRAINSTORM process was developed by David_AU.
Some of the core principals behind this process are discussed in this scientific paper : Progressive LLaMA with Block Expansion .
However I went in a completely different direction from what was outlined in this paper.
What is "Brainstorm" ?
The reasoning center of an LLM is taken apart, reassembled, and expanded.
In this case for this model: 20 times
Then these centers are individually calibrated. These "centers" also interact with each other. This introduces subtle changes into the reasoning process. The calibrations further adjust - dial up or down - these "changes" further. The number of centers (5x,10x etc) allow more "tuning points" to further customize how the model reasons so to speak.
The core aim of this process is to increase the model's detail, concept and connection to the "world", general concept connections, prose quality and prose length without affecting instruction following.
This will also enhance any creative use case(s) of any kind, including "brainstorming", creative art form(s) and like case uses.
Here are some of the enhancements this process brings to the model's performance:
Other "lab" observations:
The process to modify the model occurs at the root level - source files level. The model can quanted as a GGUF, EXL2, AWQ etc etc.
For more information / other Qwen/Mistral Coders / additional settings see:
[ https://huggingface.co/DavidAU/Qwen2.5-MOE-2x-4x-6x-8x__7B__Power-CODER__19B-30B-42B-53B-gguf ]
CHANGE THE NUMBER OF ACTIVE EXPERTS:
See this document:
https://huggingface.co/DavidAU/How-To-Set-and-Manage-MOE-Mix-of-Experts-Model-Activation-of-Experts
Settings: CHAT / ROLEPLAY and/or SMOOTHER operation of this model:
In "KoboldCpp" or "oobabooga/text-generation-webui" or "Silly Tavern" ;
Set the "Smoothing_factor" to 1.5
: in KoboldCpp -> Settings->Samplers->Advanced-> "Smooth_F"
: in text-generation-webui -> parameters -> lower right.
: In Silly Tavern this is called: "Smoothing"
NOTE: For "text-generation-webui"
-> if using GGUFs you need to use "llama_HF" (which involves downloading some config files from the SOURCE version of this model)
Source versions (and config files) of my models are here:
OTHER OPTIONS:
Increase rep pen to 1.1 to 1.15 (you don't need to do this if you use "smoothing_factor")
If the interface/program you are using to run AI MODELS supports "Quadratic Sampling" ("smoothing") just make the adjustment as noted.
Highest Quality Settings / Optimal Operation Guide / Parameters and Samplers
This a "Class 1" model:
For all settings used for this model (including specifics for its "class"), including example generation(s) and for advanced settings guide (which many times addresses any model issue(s)), including methods to improve model performance for all use case(s) as well as chat, roleplay and other use case(s) please see:
You can see all parameters used for generation, in addition to advanced parameters and samplers to get the most out of this model here:
Base model
Qwen/Qwen2.5-7B