Text Generation
Transformers
PyTorch
Safetensors
English
multilingual
llama
phi
nlp
math
code
chat
conversational
phi3
reasoning
CoT
Eval Results (legacy)
text-generation-inference
Instructions to use Pinkstack/Parm-2-CoT-14B-16k-o1-QwQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Pinkstack/Parm-2-CoT-14B-16k-o1-QwQ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Pinkstack/Parm-2-CoT-14B-16k-o1-QwQ") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Pinkstack/Parm-2-CoT-14B-16k-o1-QwQ") model = AutoModelForCausalLM.from_pretrained("Pinkstack/Parm-2-CoT-14B-16k-o1-QwQ") 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 Pinkstack/Parm-2-CoT-14B-16k-o1-QwQ with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Pinkstack/Parm-2-CoT-14B-16k-o1-QwQ" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Pinkstack/Parm-2-CoT-14B-16k-o1-QwQ", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Pinkstack/Parm-2-CoT-14B-16k-o1-QwQ
- SGLang
How to use Pinkstack/Parm-2-CoT-14B-16k-o1-QwQ 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 "Pinkstack/Parm-2-CoT-14B-16k-o1-QwQ" \ --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": "Pinkstack/Parm-2-CoT-14B-16k-o1-QwQ", "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 "Pinkstack/Parm-2-CoT-14B-16k-o1-QwQ" \ --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": "Pinkstack/Parm-2-CoT-14B-16k-o1-QwQ", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Pinkstack/Parm-2-CoT-14B-16k-o1-QwQ with Docker Model Runner:
docker model run hf.co/Pinkstack/Parm-2-CoT-14B-16k-o1-QwQ
chat_template updated to use cot
Browse files- tokenizer_config.json +1 -1
tokenizer_config.json
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}
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},
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"bos_token": "<|endoftext|>",
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"chat_template": "{% for message in messages %}{% if (message['role'] == 'system') %}{{'<|im_start|>system<|im_sep|>' + message['content'] + '<|im_end|>'}}{% elif (message['role'] == 'user') %}{{'<|im_start|>user<|im_sep|>' + message['content'] + '<|im_end|>'}}{% elif (message['role'] == 'assistant') %}{{'<|im_start|>assistant<|im_sep|>' + message['content'] + '<|im_end|>'}}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant<|im_sep|>
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"extra_special_tokens": {},
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}
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},
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"bos_token": "<|endoftext|>",
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+
"chat_template": "{% for message in messages %}{% if (message['role'] == 'system') %}{{'<|im_start|>system<|im_sep|>' + message['content'] + '<|im_end|>'}}{% elif (message['role'] == 'user') %}{{'<|im_start|>user<|im_sep|>' + message['content'] + '<|im_end|>'}}{% elif (message['role'] == 'assistant') %}{{'<|im_start|>assistant<|im_sep|>' + message['content'] + '{{ .CoT }}<|CoT|>' + message['content'] + '<|FinalAnswer|>' + '<|im_end|>'}}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant<|im_sep|> }}{% endif %}",
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"extra_special_tokens": {},
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