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 Qwen 7b Coder Instruct FUSED with open-r1's OlympicCoder-7B (instruct model) creating an 11B, 42 layers, 507 tensors model.
https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct
https://huggingface.co/open-r1/OlympicCoder-7B
Information on the models below, and then a complete help section for running LLM / AI models.
The FUSING process enhances model performance and the model has minimal to no "reasoning" blocks.
Ask the model for code, and you get code asap.
Source is in float32 precision to preserve Microsoft Next Coder's 32 bit source.
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
OlympicCoder-7B is a code model that achieves strong performance on competitive coding benchmarks such as LiveCodeBench and the 2024 International Olympiad in Informatics.
We compare the performance of OlympicCoder models on two main benchmarks for competitive coding:
v4_v5 subset of livecodebench/code_generation_lite, which corresponds to 268 problems. We use lighteval to evaluate models on LiveCodeBench using the sampling parameters described here.The OlympicCoder models were post-trained exclusively on C++ solutions generated by DeepSeek-R1. As a result the performance on LiveCodeBench should be considered to be partially out-of-domain, since this expects models to output solutions in Python.
Here's how you can run the model using the pipeline() function from 🤗 Transformers:
# pip install transformers
# pip install accelerate
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="open-r1/OlympicCoder-7B", torch_dtype=torch.bfloat16, device_map="auto")
# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
{"role": "user", "content": "Write a python program to calculate the 10th Fibonacci number"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=8000, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
#<|im_start|>user
#Write a python program to calculate the 10th fibonacci number<|im_end|>
#<|im_start|>assistant
#<think>Okay, I need to write a Python program that calculates the 10th Fibonacci number. Hmm, the Fibonacci sequence starts with 0 and 1. Each subsequent number is the sum of the two preceding ones. So the sequence goes: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, and so on. ...
To ensure that the model consistently outputs a long chain-of-thought, we have edited the chat template to prefill the first assistant turn with a
<think>token. As a result, the outputs from this model will not show the opening<think>token if you use the model'sgenerate()method. To apply reinforcement learning with a format reward, either prepend the<think>token to the model's completions or amend the chat template to remove the prefill.
The following hyperparameters were used during training:
Also see:
https://huggingface.co/open-r1/OlympicCoder-7B
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: