---
library_name: transformers
tags:
- math
- text-generation-inference
- code
- 3B
license: llama3.2
language:
- en
base_model:
- meta-llama/Llama-3.2-3B-Instruct
pipeline_tag: text-generation
---
![zxdfzdfvadsv.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/FX4IIp4uNpb7uaez3dt9H.png)

# **Ganymede-Llama-3.3-3B-Preview**  

>  Ganymede-Llama-3.3-3B-Preview is based on the **Llama-3.2-3B-Instruct** architecture, featuring **unlocked abliterated** capabilities and **improved mathematical analysis**. Fine-tuned on a high-quality synthetic dataset derived from Llama's **Instruct** series, it excels in **chain-of-thought (CoT) reasoning, logical problem-solving, and structured data comprehension**. The model is ideal for complex reasoning tasks, instruction-following, and text generation, with superior adaptability across **multi-turn conversations and long-context tasks**.  

### **Key Improvements**  
1. **Unlocked Abliterated Reasoning**: Enhanced **multi-step problem-solving, logical deduction, and contextual analysis**.  
2. **Mathematical & Analytical Excellence**: Stronger capabilities in **math problem-solving, theorem proving, and complex numerical analysis**.  
3. **Fine-Tuned Instruction Following**: Generates structured responses (e.g., **JSON, XML, Markdown**) and **long-form text (4K+ tokens)**.  
4. **Extended Long-Context Support**: Handles up to **128K tokens** with improved memory retention and coherence over long passages.  
5. **Advanced Adaptability**: Excels in **role-playing, multi-turn dialogues, and diverse system prompts**.  
6. **Multilingual Proficiency**: Supports over **20 languages**, including **English, Chinese, French, Spanish, Portuguese, German, and more**.  

### **Quickstart with Transformers**  

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Ganymede-Llama-3.3-3B-Preview"

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, logic, and mathematical analysis."},
    {"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=256
)
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 **multi-step problem-solving, deductive reasoning, and cognitive tasks**.  
- **Enhanced Mathematical Computation**: Excels in **numerical analysis, theorem proving, symbolic reasoning, and complex calculations**.  
- **Code Generation & Debugging**: Generates **optimized code**, detects **bugs**, and enhances **programming workflows**.  
- **Structured Data Processing**: Handles **tables, JSON, and structured formats** for data-centric applications.  
- **Multilingual Reasoning & Translation**: High proficiency across **20+ languages** for **global AI applications**.  
- **Extended Text Generation**: Ideal for generating **technical documentation, research papers, instructional guides, and in-depth reports**.  

### **Limitations**  
1. **Moderate Computational Requirements**: Requires **mid-to-high-end consumer GPUs** for optimal inference.  
2. **Language-Specific Variability**: Performance may differ across languages, particularly for **low-resource languages**.  
3. **Potential Error Accumulation**: Long-form text generation may introduce **inconsistencies** over extended outputs.  
4. **Limited Real-World Awareness**: Knowledge is restricted to **training data** and may not reflect **recent world events**.  
5. **Prompt Sensitivity**: The quality of responses depends on **prompt clarity and specificity**.