Text Generation
Transformers
Safetensors
olmo3
code
reasoning
lora-merged
livecodebench
conversational
Instructions to use modrill/code-think-o7b-20260908 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use modrill/code-think-o7b-20260908 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="modrill/code-think-o7b-20260908") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("modrill/code-think-o7b-20260908") model = AutoModelForCausalLM.from_pretrained("modrill/code-think-o7b-20260908", 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 modrill/code-think-o7b-20260908 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "modrill/code-think-o7b-20260908" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "modrill/code-think-o7b-20260908", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/modrill/code-think-o7b-20260908
- SGLang
How to use modrill/code-think-o7b-20260908 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 "modrill/code-think-o7b-20260908" \ --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": "modrill/code-think-o7b-20260908", "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 "modrill/code-think-o7b-20260908" \ --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": "modrill/code-think-o7b-20260908", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use modrill/code-think-o7b-20260908 with Docker Model Runner:
docker model run hf.co/modrill/code-think-o7b-20260908
Download adapter/TOKEN_ROWS_META.json from modrill/code-think-o7b-20260908: direct link, hf CLI and curl.
- Browser
- Download file 272 Bytes
-
https://huggingface.co/modrill/code-think-o7b-20260908/resolve/main/adapter/TOKEN_ROWS_META.json
- Command line
-
hf download hf://modrill/code-think-o7b-20260908/adapter/TOKEN_ROWS_META.json
-
curl -L -o TOKEN_ROWS_META.json https://huggingface.co/modrill/code-think-o7b-20260908/resolve/main/adapter/TOKEN_ROWS_META.json
272 Bytes
| {"base_rev":"a81bae42db3975be1671e27b9c9a56da1a9f980f","dtype":"float32","embed_delta_l2":4.383120059967041,"hidden":4096,"ids":[100257],"lm_head_delta_l2":1.3613775968551636,"schema":"TOKEN_ROWS_META_V1","tied_before":false,"untied":true,"worker":"rstar_dual_worker_v4"} | |