Triangle104/Megatron-Opus-7B-Exp-Q4_K_M-GGUF

This model was converted to GGUF format from prithivMLmods/Megatron-Opus-7B-Exp using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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Megatron-Opus-7B-Exp is based on the Qwen 2.5 7B modality architecture, designed to enhance the reasoning capabilities of 7B-parameter models. It has been fine-tuned on a Synthetic dataset entries based on one half of Qwen’s QWQ and DeepSeek R1, further optimizing its chain-of-thought (CoT) reasoning and logical problem-solving abilities. The model demonstrates significant improvements in context understanding, structured data processing, and long-context comprehension, making it ideal for complex reasoning tasks, instruction-following, and text generation.

Key Improvements

Advanced Reasoning & Logic: Optimized for multi-step problem-solving, logical deduction, and contextual analysis.
Fine-Tuned Instruction Following: Generates precise responses, structured outputs (e.g., JSON), and extended long-form text (8K+ tokens).
Greater Adaptability: Excels in role-playing, multi-turn dialogues, and diverse system prompts.
Long-Context Support: Handles up to 128K tokens and generates up to 8K tokens per output.
Multilingual Proficiency: Supports over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, and more.

Quickstart with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Megatron-Opus-7B-Exp"

model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto", trust_remote_code=True ) tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Explain the concept of logical reasoning in AI." messages = [ {"role": "system", "content": "You are an expert AI assistant specialized in reasoning and logic."}, {"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=512 ) 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] print(response)

Intended Use

Advanced Logical & Analytical Reasoning: Designed for problem-solving, multi-step deductions, and cognitive reasoning tasks.
Mathematical & Scientific Computation: Supports theorem proving, complex calculations, and scientific knowledge retrieval.
Code Generation & Debugging: Generates optimized code, detects errors, and improves programming workflows.
Structured Data Analysis: Processes tables, JSON, and structured formats for data-centric applications.
Multilingual Reasoning & Translation: High proficiency across 29+ languages for international applications.
Extended Text Generation: Capable of generating research papers, instructional guides, and in-depth reports.

Limitations

High Computational Requirements: Due to its 7B parameters and 128K context support, it requires powerful GPUs or TPUs for efficient inference.
Language-Specific Variability: Performance may differ across supported languages, especially for low-resource languages.
Potential Error Accumulation: Long-form text generation can introduce inconsistencies over extended outputs.
Limited Real-World Awareness: Knowledge is restricted to training data and may not reflect recent world events.
Prompt Sensitivity: The quality of responses depends on the specificity and clarity of the input prompt.

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Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/Megatron-Opus-7B-Exp-Q4_K_M-GGUF --hf-file megatron-opus-7b-exp-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Megatron-Opus-7B-Exp-Q4_K_M-GGUF --hf-file megatron-opus-7b-exp-q4_k_m.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Triangle104/Megatron-Opus-7B-Exp-Q4_K_M-GGUF --hf-file megatron-opus-7b-exp-q4_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Megatron-Opus-7B-Exp-Q4_K_M-GGUF --hf-file megatron-opus-7b-exp-q4_k_m.gguf -c 2048