README.md

---
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

<p align="center">
  <strong>An 8B-class instruction-following language model focused on reasoning, coding, mathematics, and general-purpose assistance.</strong>
</p>

<p align="center">
  <a href="https://huggingface.co/KeefeBuild/Keefe-Discere">
    <img src="https://img.shields.io/badge/Hugging%20Face-Keefe--Discere-orange" alt="Hugging Face">
  </a>
  <img src="https://img.shields.io/badge/Parameters-~8B-blue" alt="Parameters">
  <img src="https://img.shields.io/badge/Precision-BF16-blue" alt="Precision">
  <img src="https://img.shields.io/badge/License-Apache--2.0-green" alt="License">
  <img src="https://img.shields.io/badge/Context-32K-purple" alt="Context">
</p>

---

## 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)