Viper-Coder-v0.1-GGUF is based on the Qwen 2.5 14B modality architecture, designed to enhance the reasoning capabilities of 14B-parameter models. It has been fine-tuned on a synthetic dataset based on 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 reasoning tasks, instruction-following, and text generation.

Key Improvements

Enhanced Knowledge and Expertise: Improved mathematical reasoning, coding proficiency, and structured data processing.
Fine-Tuned Instruction Following: Optimized for precise responses, structured outputs (e.g., JSON), and generating long texts (8K+ tokens).
Greater Adaptability: Better role-playing capabilities and resilience to 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

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Viper-Coder-v0.1-GGUF"

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

prompt = "Give me a short introduction to large language models."
messages = [
    {"role": "system", "content": "You are an advanced AI assistant with expert-level reasoning and knowledge."},
    {"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 Reasoning & Context Understanding: Designed for logical deduction, multi-step problem-solving, and complex knowledge-based tasks.
Mathematical & Scientific Problem-Solving: Enhanced capabilities for calculations, theorem proving, and scientific queries.
Code Generation & Debugging: Generates and optimizes code across multiple programming languages.
Structured Data Analysis: Processes tables, JSON, and structured outputs, making it ideal for data-centric tasks.
Multilingual Applications: High proficiency in over 29 languages, enabling global-scale applications.
Extended Content Generation: Supports detailed document writing, research reports, and instructional guides.

Limitations

High Computational Requirements: Due to its 14B parameters and 128K context support, it requires powerful GPUs or TPUs for efficient inference.
Language-Specific Variability: Performance may vary across supported languages, especially for low-resource languages.
Potential Error Accumulation: Long-text generation can sometimes introduce inconsistencies over extended outputs.
Limited Real-World Awareness: Knowledge is restricted to training data and may not reflect recent world events.
Prompt Sensitivity: Outputs can depend on the specificity and clarity of the input prompt.