DeepSeek-R1-Distill-Llama-8B-Game-theory-V1

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DeepSeek-GT: Game Theory Specialized Language Model

Model Description

DeepSeek-GT is a fine-tuned version of the DeepSeek Coder Instruct 8B model, specifically optimized for game theory analysis and problem-solving. Built on the efficient DeepSeek-R1-Distill-Llama-8B architecture, this model has been trained on a curated dataset of real-world game theory scenarios, strategic analyses, and solutions.

Key Features

• Specialized Knowledge: Fine-tuned on diverse game theory examples, including Nash Equilibrium analysis, strategic decision-making, and payoff evaluations
• Structured Reasoning: Provides step-by-step analysis of game theory real life scenarios, breaking down complex strategic situations into comprehensible components
• Solution-Oriented: Generates detailed solutions with clear reasoning paths and strategic implications
• Architecture: Based on DeepSeek's 8B parameter model, offering a balance between performance and computational efficiency

Use Cases

• Analyzing strategic interactions in business and economics
• Solving game theory problems with detailed explanations
• Understanding Nash Equilibria in various scenarios
• Evaluating payoff matrices and optimal strategies
• Providing insights for decision-making in competitive situations

Model Details

• Base Model: DeepSeek-R1-Distill-Llama-8B
• Training Data: Curated dataset of game theory examples and solutions
• Input Format: Accepts scenario descriptions and questions about real life situations
• Output Format: Structured responses including scenario analysis, strategic reasoning, and solutions

Example Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
# Define a system prompt under prompt_style 
prompt_style = """Below is an instruction that describes a task, paired with an input that provides further context. 
Write a response that appropriately completes the request. 
Before answering, think carefully about the Scenario and create a step-by-step chain of thoughts to ensure a logical and accurate response.

### Instruction:
You are a Game theory expert with advanced knowledge in Game theory reasoning, solutions, and in finding the optimized output. 
Please answer the following Game theory related Scenario. 

### Scenario:
{}
"""
from transformers import AutoModelForCausalLM, AutoTokenizer
from unsloth import FastLanguageModel
import torch

# Load model and tokenizer from hub
model = AutoModelForCausalLM.from_pretrained(
    "Anudeep28/DeepSeek-R1-Distill-Llama-8B-Game-theory-V1",  # your model repository name
    torch_dtype=torch.float16,      # use float16 for efficiency
    device_map="auto",
    load_in_4bit=True           # automatically handle device placement
)
tokenizer = AutoTokenizer.from_pretrained("Anudeep28/DeepSeek-R1-Distill-Llama-8B-Game-theory-V1")

# Optional: Use Unsloth's optimization if you want the faster inference

FastLanguageModel.for_inference(model)

# Prepare your prompt
question = """Chris and Kim are attending the same conference and want to meet. 
            They can choose between swimming and hiking, but they don't know each other's choice beforehand. 
            They both prefer swimming, but only if they are together."""

# Load the inference model using FastLanguageModel (Unsloth optimizes for speed)
# merged_model = merged_model.cuda()
# FastLanguageModel.for_inference(merged_model)  # Unsloth has 2x faster inference!

# Tokenize the input question with a specific prompt format and move it to the GPU
inputs = tokenizer([prompt_style.format(question, "")], return_tensors="pt").to("cuda")

# Tokenize input
# Generate a response using LoRA fine-tuned model with specific parameters
outputs = model.generate(
    input_ids=inputs.input_ids,          # Tokenized input IDs
    attention_mask=inputs.attention_mask, # Attention mask for padding handling
    max_new_tokens=1200,                  # Maximum length for generated response
    use_cache=True,                        # Enable cache for efficient generation
)

# Decode the generated response from tokenized format to readable text
response = tokenizer.batch_decode(outputs)

# Extract and print only the model's response part after "### Response:"
print(response[0].split("### Response:")[1])
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

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## Uses

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