Text Generation
Transformers
PyTorch
TensorBoard
Safetensors
llama
Generated from Trainer
axolotl
dpo
trl
conversational
text-generation-inference
Instructions to use PhoenixB/f8f8e9ee-4db7-4425-92fe-29b1ad691f10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PhoenixB/f8f8e9ee-4db7-4425-92fe-29b1ad691f10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="PhoenixB/f8f8e9ee-4db7-4425-92fe-29b1ad691f10") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("PhoenixB/f8f8e9ee-4db7-4425-92fe-29b1ad691f10") model = AutoModelForCausalLM.from_pretrained("PhoenixB/f8f8e9ee-4db7-4425-92fe-29b1ad691f10", 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 PhoenixB/f8f8e9ee-4db7-4425-92fe-29b1ad691f10 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "PhoenixB/f8f8e9ee-4db7-4425-92fe-29b1ad691f10" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "PhoenixB/f8f8e9ee-4db7-4425-92fe-29b1ad691f10", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/PhoenixB/f8f8e9ee-4db7-4425-92fe-29b1ad691f10
- SGLang
How to use PhoenixB/f8f8e9ee-4db7-4425-92fe-29b1ad691f10 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 "PhoenixB/f8f8e9ee-4db7-4425-92fe-29b1ad691f10" \ --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": "PhoenixB/f8f8e9ee-4db7-4425-92fe-29b1ad691f10", "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 "PhoenixB/f8f8e9ee-4db7-4425-92fe-29b1ad691f10" \ --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": "PhoenixB/f8f8e9ee-4db7-4425-92fe-29b1ad691f10", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use PhoenixB/f8f8e9ee-4db7-4425-92fe-29b1ad691f10 with Docker Model Runner:
docker model run hf.co/PhoenixB/f8f8e9ee-4db7-4425-92fe-29b1ad691f10
Training in progress, step 100, checkpoint
Browse files- last-checkpoint/adapter_model.safetensors +1 -1
- last-checkpoint/global_step100/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt +3 -0
- last-checkpoint/global_step100/bf16_zero_pp_rank_1_mp_rank_00_optim_states.pt +3 -0
- last-checkpoint/global_step100/mp_rank_00_model_states.pt +3 -0
- last-checkpoint/latest +1 -1
- last-checkpoint/scheduler.pt +1 -1
- last-checkpoint/trainer_state.json +153 -3
last-checkpoint/adapter_model.safetensors
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|
| 1505 |
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|
| 1506 |
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|
| 1507 |
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|
| 1508 |
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|
| 1509 |
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"step": 100
|
| 1510 |
}
|
| 1511 |
],
|
| 1512 |
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|
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|
| 1521 |
"should_evaluate": false,
|
| 1522 |
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|
| 1523 |
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|
| 1524 |
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"should_training_stop": true
|
| 1525 |
},
|
| 1526 |
"attributes": {}
|
| 1527 |
}
|