Instructions to use Dat1710/countdown-grpo-qwen2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Dat1710/countdown-grpo-qwen2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Math-1.5B") model = PeftModel.from_pretrained(base_model, "Dat1710/countdown-grpo-qwen2") - Transformers
How to use Dat1710/countdown-grpo-qwen2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Dat1710/countdown-grpo-qwen2") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Dat1710/countdown-grpo-qwen2", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Dat1710/countdown-grpo-qwen2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Dat1710/countdown-grpo-qwen2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Dat1710/countdown-grpo-qwen2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Dat1710/countdown-grpo-qwen2
- SGLang
How to use Dat1710/countdown-grpo-qwen2 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 "Dat1710/countdown-grpo-qwen2" \ --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": "Dat1710/countdown-grpo-qwen2", "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 "Dat1710/countdown-grpo-qwen2" \ --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": "Dat1710/countdown-grpo-qwen2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Dat1710/countdown-grpo-qwen2 with Docker Model Runner:
docker model run hf.co/Dat1710/countdown-grpo-qwen2
Upload tokenizer
Browse files- tokenizer.json +2 -2
- tokenizer_config.json +0 -2
tokenizer.json
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
|
| 3 |
+
size 11421896
|
tokenizer_config.json
CHANGED
|
@@ -201,9 +201,7 @@
|
|
| 201 |
"extra_special_tokens": {},
|
| 202 |
"model_max_length": 131072,
|
| 203 |
"pad_token": "<|endoftext|>",
|
| 204 |
-
"padding_side": "left",
|
| 205 |
"split_special_tokens": false,
|
| 206 |
"tokenizer_class": "Qwen2Tokenizer",
|
| 207 |
-
"truncation_side": "left",
|
| 208 |
"unk_token": null
|
| 209 |
}
|
|
|
|
| 201 |
"extra_special_tokens": {},
|
| 202 |
"model_max_length": 131072,
|
| 203 |
"pad_token": "<|endoftext|>",
|
|
|
|
| 204 |
"split_special_tokens": false,
|
| 205 |
"tokenizer_class": "Qwen2Tokenizer",
|
|
|
|
| 206 |
"unk_token": null
|
| 207 |
}
|