| --- |
| 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) |
