Text Generation
Transformers
Safetensors
llama
JS
HTML
GAME
GAMIA
conversational
text-generation-inference
Instructions to use RAANA-IA/Gamia-lisaGame with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RAANA-IA/Gamia-lisaGame with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="RAANA-IA/Gamia-lisaGame") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("RAANA-IA/Gamia-lisaGame") model = AutoModelForCausalLM.from_pretrained("RAANA-IA/Gamia-lisaGame") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use RAANA-IA/Gamia-lisaGame with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RAANA-IA/Gamia-lisaGame" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RAANA-IA/Gamia-lisaGame", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/RAANA-IA/Gamia-lisaGame
- SGLang
How to use RAANA-IA/Gamia-lisaGame with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "RAANA-IA/Gamia-lisaGame" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RAANA-IA/Gamia-lisaGame", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "RAANA-IA/Gamia-lisaGame" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RAANA-IA/Gamia-lisaGame", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use RAANA-IA/Gamia-lisaGame with Docker Model Runner:
docker model run hf.co/RAANA-IA/Gamia-lisaGame
Model save
Browse files- README.md +55 -0
- chat_template.jinja +15 -0
- tokenizer.json +0 -0
- tokenizer_config.json +21 -0
README.md
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---
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library_name: transformers
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license: other
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base_model: RAANA-IA/Gamia-pygame-v1
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tags:
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- generated_from_trainer
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model-index:
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- name: Gamia-lisaGame
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# Gamia-lisaGame
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This model is a fine-tuned version of [RAANA-IA/Gamia-pygame-v1](https://huggingface.co/RAANA-IA/Gamia-pygame-v1) on an unknown dataset.
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 3e-05
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- train_batch_size: 1
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 4
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: cosine
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- num_epochs: 2
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### Training results
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### Framework versions
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- Transformers 5.0.0
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- Pytorch 2.10.0+cu128
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- Datasets 4.0.0
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- Tokenizers 0.22.2
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chat_template.jinja
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{% for message in messages %}
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{% if message['role'] == 'user' %}
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{{ '<|user|>
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' + message['content'] + eos_token }}
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{% elif message['role'] == 'system' %}
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{{ '<|system|>
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' + message['content'] + eos_token }}
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{% elif message['role'] == 'assistant' %}
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{{ '<|assistant|>
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' + message['content'] + eos_token }}
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{% endif %}
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{% if loop.last and add_generation_prompt %}
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{{ '<|assistant|>' }}
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{% endif %}
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{% endfor %}
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tokenizer.json
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See raw diff
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tokenizer_config.json
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{
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"add_prefix_space": null,
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"backend": "tokenizers",
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"bos_token": "<s>",
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"clean_up_tokenization_spaces": false,
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"eos_token": "</s>",
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"is_local": false,
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"legacy": false,
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"max_length": 512,
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"model_max_length": 2048,
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"model_specific_special_tokens": {},
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"pad_token": "</s>",
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"padding_side": "right",
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"sp_model_kwargs": {},
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"stride": 0,
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"tokenizer_class": "TokenizersBackend",
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"truncation_side": "right",
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"truncation_strategy": "longest_first",
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"unk_token": "<unk>",
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"use_default_system_prompt": false
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}
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