Instructions to use trl-internal-testing/tiny-BeamForCausalLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use trl-internal-testing/tiny-BeamForCausalLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="trl-internal-testing/tiny-BeamForCausalLM") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("trl-internal-testing/tiny-BeamForCausalLM") model = AutoModelForCausalLM.from_pretrained("trl-internal-testing/tiny-BeamForCausalLM", 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=256) 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-BeamForCausalLM 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-BeamForCausalLM" # 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-BeamForCausalLM", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/trl-internal-testing/tiny-BeamForCausalLM
- SGLang
How to use trl-internal-testing/tiny-BeamForCausalLM 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-BeamForCausalLM" \ --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-BeamForCausalLM", "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-BeamForCausalLM" \ --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-BeamForCausalLM", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use trl-internal-testing/tiny-BeamForCausalLM with Docker Model Runner:
docker model run hf.co/trl-internal-testing/tiny-BeamForCausalLM
File size: 1,696 Bytes
b39bd41 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | {
"backend": "tokenizers",
"bos_token": null,
"eos_token": "<|return|>",
"extra_special_tokens": [],
"is_local": false,
"local_files_only": false,
"model_max_length": 1048576,
"pad_token": "<|endoftext|>",
"reflection_grammar_version": "harmony_xml",
"response_template": {
"defaults": {
"role": "assistant"
},
"fields": {
"content": {
"close": [
"<|return|>",
"<|end|>",
"<|call|>"
],
"content": "text",
"open": "<|channel|>final<|message|>"
},
"thinking": {
"close": [
"<|end|><|start|>assistant",
"<|constrain|><|start|>assistant",
"<|start|>assistant"
],
"content": "text",
"open": "<|channel|>analysis<|message|>"
},
"tool_calls": {
"close": [
"<|end|><|start|>assistant",
"<|call|>"
],
"content": "xml-inline",
"content_args": {
"tag_pattern": "<rfl:parameter name=\"(?P<key>[^\"]+)\">\\n(?P<value>.*?)\\n(?:</rfl:parameter>|(?=<rfl:parameter name=\"))",
"value_parser": {
"args": {
"strip": false
},
"name": "text"
}
},
"open_pattern": " to=(?P<name>[^\\s<]+)<\\|channel\\|>analysis <\\|constrain\\|>xml<\\|message\\|>",
"repeats": true,
"transform": {
"function": {
"arguments": "{content}",
"name": "{name}"
},
"type": "function"
}
}
},
"start_anchor_pattern": "<\\|start\\|>assistant"
},
"tokenizer_class": "TokenizersBackend",
"unk_token": null
}
|