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Anudeep28
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Model Details
Model Provider
Anudeep28
Model Tree
deepseek-ai/DeepSeek-R1-Distill-Llama-8B
Input Modalities
Output Modalities
Supported Functionality
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.
python
from transformers import AutoModelForCausalLM, AutoTokenizer# Define a system prompt under prompt_styleprompt_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, AutoTokenizerfrom unsloth import FastLanguageModelimport torch# Load model and tokenizer from hubmodel = AutoModelForCausalLM.from_pretrained("Anudeep28/DeepSeek-R1-Distill-Llama-8B-Game-theory-V1", # your model repository nametorch_dtype=torch.float16, # use float16 for efficiencydevice_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 inferenceFastLanguageModel.for_inference(model)# Prepare your promptquestion = """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 GPUinputs = tokenizer([prompt_style.format(question, "")], return_tensors="pt").to("cuda")# Tokenize input# Generate a response using LoRA fine-tuned model with specific parametersoutputs = model.generate(input_ids=inputs.input_ids, # Tokenized input IDsattention_mask=inputs.attention_mask, # Attention mask for padding handlingmax_new_tokens=1200, # Maximum length for generated responseuse_cache=True, # Enable cache for efficient generation)# Decode the generated response from tokenized format to readable textresponse = tokenizer.batch_decode(outputs)# Extract and print only the model's response part after "### Response:"print(response[0].split("### Response:")[1])<!-- Provide a longer summary of what this model is. -->This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.- **Developed by:** [Eneru]- **Funded by [optional]:** [More Information Needed]- **Shared by [optional]:** [More Information Needed]- **Model type:** [More Information Needed]- **Language(s) (NLP):** [More Information Needed]- **License:** [More Information Needed]- **Finetuned from model [deepseek-ai/DeepSeek-R1-Distill-Llama-8B]:** [More Information Needed]### Model Sources [optional]<!-- Provide the basic links for the model. -->- **Repository:** [More Information Needed]- **Paper [optional]:** [More Information Needed]- **Demo [optional]:** [More Information Needed]## Uses<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->### Direct Use<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->[More Information Needed]### Downstream Use [optional]<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->[More Information Needed]### Out-of-Scope Use<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->[More Information Needed]## Bias, Risks, and Limitations<!-- This section is meant to convey both technical and sociotechnical limitations. -->[More Information Needed]### Recommendations<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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