Triangle104/Viper-Coder-HybridMini-v1.3-Q4_K_M-GGUF

This model was converted to GGUF format from prithivMLmods/Viper-Coder-HybridMini-v1.3 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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Viper-Coder-HybridMini-v1.3

Viper-Coder-HybridMini-v1.3 is based on the Qwen 2.5 7B modality architecture, designed to be the best for coding and reasoning tasks. It has been fine-tuned on a synthetic dataset leveraging the latest coding logits and CoT datasets, 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 coding tasks, instruction-following, and text generation.

Key Improvements

Best-in-Class Coding Proficiency: Enhanced understanding of programming languages, debugging, and code generation.
Fine-Tuned Instruction Following: Optimized for precise responses, structured outputs (e.g., JSON, YAML), and extended text generation (8K+ tokens).
Advanced Logical & Mathematical Reasoning: Improved multi-step problem-solving and theorem proving.
Long-Context Mastery: Handles up to 128K tokens with an output capability of 8K tokens per response.
Multilingual Code Support: Excels in Python, JavaScript, C++, Java, SQL, and other major programming languages, with documentation in 29+ languages.

Quickstart with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Viper-Coder-HybridMini-v1.3"

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

prompt = "Write a Python function to merge two sorted lists." messages = [ {"role": "system", "content": "You are an advanced AI assistant with expert-level coding and reasoning abilities."}, {"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

Elite Coding & Debugging: Best-in-class model for writing, analyzing, and optimizing code.
Complex Algorithmic Reasoning: Solves intricate logic problems and algorithm-based challenges.
Scientific & Mathematical Computation: Advanced support for formulas, equations, and theorem verification.
Structured Data Processing: Seamlessly handles JSON, XML, SQL, and data pipeline automation.
Multilingual Programming Support: Proficient in Python, JavaScript, C++, Java, Go, and more.
Extended Technical Content Generation: Ideal for writing documentation, research papers, and technical blogs.

Limitations

Moderate Computational Demand: Requires GPUs/TPUs for smooth inference due to 7B parameters, but more lightweight than larger models.
Language-Specific Variability: Performance may vary across different programming languages.
Possible Error Propagation: Extended text outputs might introduce logical inconsistencies.
Limited Real-World Awareness: The model does not have access to real-time internet updates.
Prompt Sensitivity: Performance depends on how well the prompt is structured.

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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/Viper-Coder-HybridMini-v1.3-Q4_K_M-GGUF --hf-file viper-coder-hybridmini-v1.3-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Viper-Coder-HybridMini-v1.3-Q4_K_M-GGUF --hf-file viper-coder-hybridmini-v1.3-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/Viper-Coder-HybridMini-v1.3-Q4_K_M-GGUF --hf-file viper-coder-hybridmini-v1.3-q4_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Viper-Coder-HybridMini-v1.3-Q4_K_M-GGUF --hf-file viper-coder-hybridmini-v1.3-q4_k_m.gguf -c 2048