GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
Anudeep28
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
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_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, 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 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 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 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 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. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed]- **Hours used:** [More Information Needed]- **Cloud Provider:** [More Information Needed]- **Compute Region:** [More Information Needed]- **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]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, 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 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 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 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 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. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed]- **Hours used:** [More Information Needed]- **Cloud Provider:** [More Information Needed]- **Compute Region:** [More Information Needed]- **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]