---
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- text-generation
- causal-lm
- conversational
- qwen2
- qwen2.5
- transformers
- safetensors
- gguf
- unsloth
- llama.cpp
- vllm
- coding
- mathematics
base_model:
- Qwen/Qwen2.5-7B-Instruct
datasets: []
---
# Keefe-Discere
An 8B-class instruction-following language model focused on reasoning, coding, mathematics, and general-purpose assistance.
---
## Overview
**Keefe-Discere** is an independently developed language-model project by **KeefeBuild**, built around the Qwen2.5 7B instruction-tuned architecture and further adapted/merged with specialized coding and mathematics capabilities.
The project is designed as a general-purpose local language model with an emphasis on:
- Reasoning and structured problem solving
- Mathematics and quantitative tasks
- Programming and code generation
- General instruction following
- Conversational assistance
- Local and self-hosted inference
- Experimentation with model adaptation and merging
Keefe-Discere is distributed in multiple formats to make local deployment easier across different inference stacks.
> **Important:** Keefe-Discere is an independent model project and is not an official Qwen model.
---
# Model Information
| Property | Details |
|---|---|
| Model | Keefe-Discere |
| Developer | KeefeBuild |
| Model family | Qwen2 |
| Architecture | Qwen2ForCausalLM |
| Approx. parameter count | ~8B |
| Precision | BF16 |
| Hidden size | 3584 |
| Layers | 28 |
| Attention heads | 28 |
| Key/Value heads | 4 |
| Vocabulary size | 152,064 |
| Configured context length | 32,768 tokens |
| Task | Causal language modeling / text generation |
| Primary language | English |
| License | Apache-2.0 |
| Training framework | Unsloth / Transformers ecosystem |
The current repository configuration identifies a Qwen2-family causal language model with 28 transformer layers, 28 attention heads, 4 key/value heads, and a configured maximum position length of 32,768 tokens.
---
# Model Lineage
Keefe-Discere is built from the Qwen2.5 ecosystem.
The repository's merge configuration specifies:
- `Qwen/Qwen2.5-7B-Instruct` as the base model
- `KeefeBuild/Keefe-Discere` as one merge component
- `Qwen/Qwen2.5-Coder-7B-Instruct` as a coding-oriented component
- `Qwen/Qwen2.5-Math-7B-Instruct` as a mathematics-oriented component
- DARE-TIES as the merge method
The configured merge weights are:
| Component | Weight |
|---|---:|
| Keefe-Discere | 0.40 |
| Qwen2.5-Coder-7B-Instruct | 0.30 |
| Qwen2.5-Math-7B-Instruct | 0.30 |
The merge configuration uses a density of `0.53`, an INT8 mask, and BF16 output tensors.
This design is intended to combine general instruction following with stronger specialization in software development and mathematical reasoning.
---
# What Keefe-Discere Is Designed For
## General Assistance
Keefe-Discere can be used for:
- Question answering
- Explanations and tutoring
- Summarization
- Structured text generation
- Brainstorming
- Writing assistance
- Conversational applications
## Mathematics
The model is intended to support tasks such as:
- Algebra
- Arithmetic
- Equations
- Quantitative reasoning
- Mathematical explanations
- Step-by-step problem solving
- Symbolic reasoning experiments
## Programming
The model can be used for:
- Code generation
- Code explanation
- Debugging
- Refactoring
- Algorithm design
- Programming questions
- Software-development assistance
Because the model incorporates a coding-oriented Qwen component in its merge configuration, programming is an explicit area of experimentation for this project.
## Local AI
Keefe-Discere is particularly suitable for users who want to experiment with:
- Local inference
- Private/self-hosted AI
- Offline language-model applications
- Local APIs
- Ollama
- llama.cpp
- vLLM
- SGLang
- LM Studio
- Other compatible inference runtimes
---
# Supported Formats
The repository currently provides multiple deployment formats.
### BF16 Safetensors
The primary full-precision-style distribution is provided as sharded Safetensors files.
This format is intended for modern Transformers-based inference and continued model experimentation.
### GGUF
A `Q4_K_M` GGUF distribution is also provided for efficient local inference.
This format is suitable for runtimes such as:
- llama.cpp
- Ollama
- LM Studio
- Other GGUF-compatible applications
### LoRA Adapter
The repository also contains:
- `adapter_config.json`
- `adapter_model.safetensors`
These files represent a PEFT/LoRA adapter distribution.
> The adapter metadata should reference the true base checkpoint rather than the same repository to avoid a self-referential model lineage.
---
# Transformers Usage
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "KeefeBuild/Keefe-Discere"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{
"role": "user",
"content": "Explain the difference between a stack and a queue."
}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_tensors="pt",
).to(model.device)
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=512,
temperature=0.7,
top_p=0.9,
)
response = tokenizer.decode(
outputs[0][inputs["input_ids"].shape[-1]:],
skip_special_tokens=True,
)
print(response)