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@@ -9,4 +9,69 @@ library_name: transformers
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  tags:
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  - coder
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  - text-generation-inference
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  tags:
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  - coder
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  - text-generation-inference
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+ ---
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+ ![coderx.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/ncJZH_SSIpEr16oAq4qDF.png)
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+ # **Viper-Coder-v0.1-GGUF**
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+
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+ 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.
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+
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+ ### **Key Improvements**
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+ 1. **Enhanced Knowledge and Expertise**: Improved mathematical reasoning, coding proficiency, and structured data processing.
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+ 2. **Fine-Tuned Instruction Following**: Optimized for precise responses, structured outputs (e.g., JSON), and generating long texts (8K+ tokens).
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+ 3. **Greater Adaptability**: Better role-playing capabilities and resilience to diverse system prompts.
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+ 4. **Long-Context Support**: Handles up to **128K tokens** and generates up to **8K tokens** per output.
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+ 5. **Multilingual Proficiency**: Supports over **29 languages**, including Chinese, English, French, Spanish, Portuguese, German, and more.
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+
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+ ### **Quickstart with Transformers**
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+
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+ ```python
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+
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+ model_name = "prithivMLmods/Viper-Coder-v0.1-GGUF"
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+
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+ model = AutoModelForCausalLM.from_pretrained(
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+ model_name,
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+ torch_dtype="auto",
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+ device_map="auto",
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+ trust_remote_code=True
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+ )
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+ tokenizer = AutoTokenizer.from_pretrained(model_name)
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+
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+ prompt = "Give me a short introduction to large language models."
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+ messages = [
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+ {"role": "system", "content": "You are an advanced AI assistant with expert-level reasoning and knowledge."},
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+ {"role": "user", "content": prompt}
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+ ]
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+ text = tokenizer.apply_chat_template(
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+ messages,
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+ tokenize=False,
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+ add_generation_prompt=True
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+ )
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+ model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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+
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+ generated_ids = model.generate(
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+ **model_inputs,
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+ max_new_tokens=512
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+ )
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+ generated_ids = [
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+ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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+ ]
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+
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+ response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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+ print(response)
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+ ```
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+
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+ ### **Intended Use**
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+ - **Advanced Reasoning & Context Understanding**: Designed for logical deduction, multi-step problem-solving, and complex knowledge-based tasks.
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+ - **Mathematical & Scientific Problem-Solving**: Enhanced capabilities for calculations, theorem proving, and scientific queries.
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+ - **Code Generation & Debugging**: Generates and optimizes code across multiple programming languages.
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+ - **Structured Data Analysis**: Processes tables, JSON, and structured outputs, making it ideal for data-centric tasks.
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+ - **Multilingual Applications**: High proficiency in over 29 languages, enabling global-scale applications.
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+ - **Extended Content Generation**: Supports detailed document writing, research reports, and instructional guides.
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+
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+ ### **Limitations**
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+ 1. **High Computational Requirements**: Due to its **14B parameters** and **128K context support**, it requires powerful GPUs or TPUs for efficient inference.
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+ 2. **Language-Specific Variability**: Performance may vary across supported languages, especially for low-resource languages.
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+ 3. **Potential Error Accumulation**: Long-text generation can sometimes introduce inconsistencies over extended outputs.
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+ 4. **Limited Real-World Awareness**: Knowledge is restricted to training data and may not reflect recent world events.
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+ 5. **Prompt Sensitivity**: Outputs can depend on the specificity and clarity of the input prompt.