Text Generation
Transformers
Safetensors
qwen3
conversational
text-generation-inference
4-bit precision
bitsandbytes
Instructions to use RikoteMaster/try2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RikoteMaster/try2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="RikoteMaster/try2") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("RikoteMaster/try2") model = AutoModelForCausalLM.from_pretrained("RikoteMaster/try2", 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 RikoteMaster/try2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RikoteMaster/try2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RikoteMaster/try2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/RikoteMaster/try2
- SGLang
How to use RikoteMaster/try2 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 "RikoteMaster/try2" \ --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": "RikoteMaster/try2", "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 "RikoteMaster/try2" \ --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": "RikoteMaster/try2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use RikoteMaster/try2 with Docker Model Runner:
docker model run hf.co/RikoteMaster/try2
| <|im_start|>system | |
| You are a helpful assistant, that answer STEM questions. Here is the format in which you are supposed to answer: | |
| Below you are provided with three example questions and the expected answer format you should give. Just answer with A, B, C, or D. | |
| The following are multiple choice questions (with answers) about knowledge and skills in advanced master-level STEM courses. | |
| Performance enhancing synthetic steroids are based on the structure of the hormone: | |
| A. testosterone. | |
| B. cortisol. | |
| C. progesterone. | |
| D. aldosterone. | |
| Answer:A | |
| The following are multiple choice questions (with answers) about knowledge and skills in advanced master-level STEM courses. | |
| Asp235Phe in a molecular report indicates that: | |
| A. asparagine has been replaced by phenylalanine. | |
| B. phenylalanine has been replaced by asparagine. | |
| C. aspartic acid has been replaced by phenylalanine. | |
| D. phenylalanine has been replaced by aspartic acid. | |
| Answer:C | |
| The following are multiple choice questions (with answers) about knowledge and skills in advanced master-level STEM courses. | |
| The concept of V/f control of inverters driving induction motors results in: | |
| A. constant torque operation | |
| B. speed reversal | |
| C. reduced magnetic loss | |
| D. harmonic elimination | |
| Answer:A | |
| Answer the following question in the same way:<|im_end|> | |
| {% for message in messages %}{% if message['role'] == 'user' %}{{ '<|im_start|>user\n' + message['content'] + '<|im_end|>\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n<think>\nOkay, just answer with A, B, C, or D.\n</think>' }}{% endif %} |