Instructions to use floydchow7/MNLP_M3_rag_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use floydchow7/MNLP_M3_rag_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="floydchow7/MNLP_M3_rag_model") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("floydchow7/MNLP_M3_rag_model") model = AutoModelForCausalLM.from_pretrained("floydchow7/MNLP_M3_rag_model") 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
- vLLM
How to use floydchow7/MNLP_M3_rag_model with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "floydchow7/MNLP_M3_rag_model" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "floydchow7/MNLP_M3_rag_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/floydchow7/MNLP_M3_rag_model
- SGLang
How to use floydchow7/MNLP_M3_rag_model 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 "floydchow7/MNLP_M3_rag_model" \ --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": "floydchow7/MNLP_M3_rag_model", "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 "floydchow7/MNLP_M3_rag_model" \ --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": "floydchow7/MNLP_M3_rag_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use floydchow7/MNLP_M3_rag_model with Docker Model Runner:
docker model run hf.co/floydchow7/MNLP_M3_rag_model
Upload tokenizer
Browse files- tokenizer_config.json +1 -8
tokenizer_config.json
CHANGED
|
@@ -227,21 +227,14 @@
|
|
| 227 |
"<|video_pad|>"
|
| 228 |
],
|
| 229 |
"bos_token": null,
|
| 230 |
-
"chat_template": "
|
| 231 |
"clean_up_tokenization_spaces": false,
|
| 232 |
"eos_token": "<|endoftext|>",
|
| 233 |
"errors": "replace",
|
| 234 |
"extra_special_tokens": {},
|
| 235 |
-
"max_length": 512,
|
| 236 |
"model_max_length": 131072,
|
| 237 |
-
"pad_to_multiple_of": null,
|
| 238 |
"pad_token": "<|endoftext|>",
|
| 239 |
-
"pad_token_type_id": 0,
|
| 240 |
-
"padding_side": "right",
|
| 241 |
"split_special_tokens": false,
|
| 242 |
-
"stride": 0,
|
| 243 |
"tokenizer_class": "Qwen2Tokenizer",
|
| 244 |
-
"truncation_side": "right",
|
| 245 |
-
"truncation_strategy": "longest_first",
|
| 246 |
"unk_token": null
|
| 247 |
}
|
|
|
|
| 227 |
"<|video_pad|>"
|
| 228 |
],
|
| 229 |
"bos_token": null,
|
| 230 |
+
"chat_template": "You are an expert model answering multiple choice STEM questions. For each question, write the answer by giving the letter of the correct choice followed by a period, a space, and then the full text of that choice after 'Answer: ', like this: \"Answer: [letter]. [full answer]\". Answer only the current question, on the format shown. Do not generate additional questions, explanations, or any other text. Here are examples of questions with answers, always answer on this format:\n \n The following are multiple choice questions (with answers) about knowledge and skills in advanced master-level STEM courses.\n \n One process in the formation of sedimentary rocks is when rocks are\n A. compressed by moving plates.\n B. heated and subjected to high pressure.\n C. broken up and deposited in layers.\n D. moved up along fault planes.\n Answer: C. broken up and deposited in layers.\n \n The following are multiple choice questions (with answers) about knowledge and skills in advanced master-level STEM courses.\n \n Which of these can be described as a system of stars, gases, dust, and other matter that orbits a common center of gravity?\n A. an asteroid\n B. a galaxy\n C. a nebula\n D. a comet\n Answer: B. a galaxy\n \n The following are multiple choice questions (with answers) about knowledge and skills in advanced master-level STEM courses.\n \n Which is NOT a fossil fuel?\n A. Coal\n B. Oil\n C. Wood\n D. Natural gas\n Answer: C. Wood\n \n {% for message in messages %}Add commentMore actions\n {{ message.content }}\n {% endfor %}",
|
| 231 |
"clean_up_tokenization_spaces": false,
|
| 232 |
"eos_token": "<|endoftext|>",
|
| 233 |
"errors": "replace",
|
| 234 |
"extra_special_tokens": {},
|
|
|
|
| 235 |
"model_max_length": 131072,
|
|
|
|
| 236 |
"pad_token": "<|endoftext|>",
|
|
|
|
|
|
|
| 237 |
"split_special_tokens": false,
|
|
|
|
| 238 |
"tokenizer_class": "Qwen2Tokenizer",
|
|
|
|
|
|
|
| 239 |
"unk_token": null
|
| 240 |
}
|