Instructions to use trl-internal-testing/tiny-Gemma2ForCausalLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use trl-internal-testing/tiny-Gemma2ForCausalLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="trl-internal-testing/tiny-Gemma2ForCausalLM") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("trl-internal-testing/tiny-Gemma2ForCausalLM") model = AutoModelForCausalLM.from_pretrained("trl-internal-testing/tiny-Gemma2ForCausalLM", device_map="auto") 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]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use trl-internal-testing/tiny-Gemma2ForCausalLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "trl-internal-testing/tiny-Gemma2ForCausalLM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "trl-internal-testing/tiny-Gemma2ForCausalLM", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/trl-internal-testing/tiny-Gemma2ForCausalLM
- SGLang
How to use trl-internal-testing/tiny-Gemma2ForCausalLM 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 "trl-internal-testing/tiny-Gemma2ForCausalLM" \ --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": "trl-internal-testing/tiny-Gemma2ForCausalLM", "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 "trl-internal-testing/tiny-Gemma2ForCausalLM" \ --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": "trl-internal-testing/tiny-Gemma2ForCausalLM", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use trl-internal-testing/tiny-Gemma2ForCausalLM with Docker Model Runner:
docker model run hf.co/trl-internal-testing/tiny-Gemma2ForCausalLM
Upload Gemma2ForCausalLM
Browse files- config.json +5 -2
- generation_config.json +1 -2
- model.safetensors +1 -1
config.json
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"attention_dropout": 0.0,
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"attn_logit_softcapping": 50.0,
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"bos_token_id": 2,
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"cache_implementation": "hybrid",
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"eos_token_id": 1,
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"final_logit_softcapping": 30.0,
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"head_dim": 256,
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"hidden_size": 8,
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"initializer_range": 0.02,
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"intermediate_size": 32,
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"max_position_embeddings": 8192,
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"model_type": "gemma2",
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"num_attention_heads": 4,
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"rope_theta": 10000.0,
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"sliding_window": 4096,
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"torch_dtype": "float32",
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"transformers_version": "4.
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"use_cache": true,
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"vocab_size": 256249
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}
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"attention_dropout": 0.0,
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"attn_logit_softcapping": 50.0,
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"bos_token_id": 2,
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"eos_token_id": 1,
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"final_logit_softcapping": 30.0,
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"head_dim": 256,
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"hidden_size": 8,
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"initializer_range": 0.02,
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"intermediate_size": 32,
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"layer_types": [
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"sliding_attention",
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"full_attention"
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],
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"max_position_embeddings": 8192,
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"model_type": "gemma2",
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"num_attention_heads": 4,
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"rope_theta": 10000.0,
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"sliding_window": 4096,
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"torch_dtype": "float32",
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"transformers_version": "4.55.0.dev0",
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"use_cache": true,
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"vocab_size": 256249
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}
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generation_config.json
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{
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"cache_implementation": "hybrid",
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"eos_token_id": 1,
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"transformers_version": "4.
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"transformers_version": "4.55.0.dev0"
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model.safetensors
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size 8405552
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