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Model: adriangg04/TheLastOfUs-QA Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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datasets:
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- adriangg04/the-last-of-us-instruction-dataset
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language:
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- en
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base_model:
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- Qwen/Qwen2.5-7B-Instruct
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new_version: Qwen/Qwen2.5-7B-Instruct
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- agent
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- text-generation
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- question-answering
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- the-last-of-us
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- qwen
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- fine-tuned
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model-index:
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- name: TheLastOfUs-QA
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results:
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- task:
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type: question-answering
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dataset:
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name: the-last-of-us-instruction-dataset
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type: the-last-of-us-instruction-dataset
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metrics:
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- name: Evaluation Loss
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type: loss
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value: 1.011
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- name: Evaluation Entropy
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type: entropy
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value: 1.011
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---
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[](https://github.com/4drian04/qwen-7b-tlou-qa-finetuning)
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# TheLastOfUs-QA: Fine-tuned Model on Qwen2.5-7B-Instructed for The Last of Us
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This model is a fine-tuned version of the base model <a href="https://huggingface.co/Qwen/Qwen2.5-7B-Instruct">**Qwen2.5-7B-Instructed**</a>, specifically adapted to answer questions and generate text related to the universe of **The Last of Us**.
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## Description
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The model was trained to understand and generate content about the story, characters, events, and lore of the video game **The Last of Us**. Thanks to fine-tuning with the specialized dataset **the-last-of-us-instruction-dataset**, this model is capable of providing coherent and detailed answers to any query about this universe.
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This model is ideal for:
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- Creating conversational assistants that answer questions about The Last of Us.
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- Generate narrative or explanatory content based on the game's lore.
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- Support creative projects related to the post-apocalyptic world of The Last of Us.
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## Training Dataset
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The model was trained using the <a href="https://huggingface.co/datasets/adriangg04/the-last-of-us-instruction-dataset">**the-last-of-us-instruction-dataset**</a>, a custom dataset containing instructions and questions about the game's universe, as well as answers based on the official narrative and key story elements.
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## Training Details
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- **Base model:** Qwen/Qwen2.5-7B-Instruct
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- **Method:** QLoRA (4-bit) + PEFT
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**LoRA**
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- r=16, alpha=32, dropout=0.05
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- target: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
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**Training**
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- epochs=3, lr=1e-4, scheduler=cosine
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- batch=4, grad_accum=4 (effective=16)
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- warmup=0.03
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**Optimization**
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- optimizer: paged_adamw_8bit
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- bf16 + gradient checkpointing
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**Quantization**
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- 4-bit (nf4), double quant, bfloat16 compute
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**Eval & Saving**
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- eval/save: each epoch
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- best model: eval_loss
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## LoRA Merge
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After fine-tuning, the LoRA adapters were merged into the base model weights.
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### Why merge?
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Merging the LoRA adapters has several advantages:
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- **Simpler usage**: The model can be used directly without loading additional adapters.
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- **Better compatibility**: Works seamlessly with standard inference pipelines.
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- **Easier deployment**: No need to manage separate LoRA weights.
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- **Improved portability**: A single model file is easier to share and integrate.
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### Notes
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- The performance is equivalent to using the LoRA adapters during inference.
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- This repository provides the **fully merged model**, ready for immediate use.
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## Hardware
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The model was fine-tuned using:
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- GPU: NVIDIA T4
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- Precision: bfloat16 + 4-bit quantization
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- Frameworks:
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- Transformers
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- PEFT
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- TRL (SFTTrainer)
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- BitsAndBytes
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## Training Efficiency
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Thanks to QLoRA and 4-bit quantization:
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- Only a small percentage of parameters were trained (LoRA adapters)
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- Reduced VRAM usage, enabling training on a single GPU
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- Maintained strong performance while being computationally efficient
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## Prompt Format
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This model follows a chat-based format using roles:
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- system
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- user
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- assistant
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Example:
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messages = [
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{"role": "system", "content": "You are an expert on The Last of Us"},
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{"role": "user", "content": "Who is Ellie?"}
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]
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## Example of Use
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You can load the model directly with Transformers:
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```python
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from transformers import pipeline, AutoTokenizer
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MODEL_NAME = "adriangg04/TheLastOfUs-QA"
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
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pipe = pipeline(
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"text-generation",
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model=MODEL_NAME,
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tokenizer=tokenizer,
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device_map="auto"
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)
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# Prompt de prueba simple
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messages = [
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{"role": "system", "content": "You are an expert on The Last of Us"},
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{"role": "user", "content": "What is the main reason for Ellie's journey to Seattle in The Last of Us?"}
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]
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prompt = 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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response = pipe(
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prompt,
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max_new_tokens=200,
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temperature=0.5
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)
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answer = response[0]["generated_text"]
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print("Prompt:", messages[1]["content"])
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print("Response:", answer)
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```
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**Disclaimer:** This model is not affiliated with, endorsed by, or approved by Naughty Dog, Sony Interactive Entertainment, or PlayStation. All content related to *The Last of Us* is used solely for professional and research purposes. Copyrights and trademarks belong to their respective owners.
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54
chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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61
config.json
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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],
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"max_position_embeddings": 32768,
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"max_window_layers": 28,
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"model_type": "qwen2",
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"num_attention_heads": 28,
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"num_hidden_layers": 28,
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"num_key_value_heads": 4,
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"pad_token_id": null,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_type": "default"
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"transformers_version": "5.2.0",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 152064
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}
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14
generation_config.json
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generation_config.json
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{
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"bos_token_id": 151643,
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"do_sample": true,
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"repetition_penalty": 1.05,
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"temperature": 0.7,
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"top_k": 20,
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"top_p": 0.8,
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"transformers_version": "5.2.0"
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}
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3
model.safetensors
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model.safetensors
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size 15231272152
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3
tokenizer.json
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3
tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
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size 11421892
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29
tokenizer_config.json
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"eos_token": "<|im_end|>",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null
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}
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