Text Generation
Transformers
Safetensors
llama
Generated from Trainer
axolotl
conversational
text-generation-inference
Instructions to use dphn/dolphin-2.9-llama3-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dphn/dolphin-2.9-llama3-8b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="dphn/dolphin-2.9-llama3-8b") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("dphn/dolphin-2.9-llama3-8b") model = AutoModelForCausalLM.from_pretrained("dphn/dolphin-2.9-llama3-8b", 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]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use dphn/dolphin-2.9-llama3-8b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "dphn/dolphin-2.9-llama3-8b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dphn/dolphin-2.9-llama3-8b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/dphn/dolphin-2.9-llama3-8b
- SGLang
How to use dphn/dolphin-2.9-llama3-8b 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 "dphn/dolphin-2.9-llama3-8b" \ --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": "dphn/dolphin-2.9-llama3-8b", "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 "dphn/dolphin-2.9-llama3-8b" \ --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": "dphn/dolphin-2.9-llama3-8b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use dphn/dolphin-2.9-llama3-8b with Docker Model Runner:
docker model run hf.co/dphn/dolphin-2.9-llama3-8b
Fix Incorrect Prompt Template defined in tokenizer_config.json (#3)
Browse files- Fix Incorrect Prompt Template defined in tokenizer_config.json (f8744cd4f6268b99ee7fd790b735694acfd07b70)
Co-authored-by: Mikael <Mikael110@users.noreply.huggingface.co>
- tokenizer_config.json +1 -1
tokenizer_config.json
CHANGED
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@@ -2066,7 +2066,7 @@
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}
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},
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"bos_token": "<|begin_of_text|>",
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-
"chat_template": "{%
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"clean_up_tokenization_spaces": true,
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"eos_token": "<|im_end|>",
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"model_input_names": [
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}
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},
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"bos_token": "<|begin_of_text|>",
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+
"chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
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"clean_up_tokenization_spaces": true,
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"eos_token": "<|im_end|>",
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"model_input_names": [
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