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
File size: 3,145 Bytes
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"schema": "RUN_IDENTITY_V1",
"run_id": "t30b2507-o7b-think-v4-tail151643",
"corrects": "PAIRED_V4",
"model": {
"hf_id": "allenai/Olmo-3-1025-7B",
"revision": "a81bae42db3975be1671e27b9c9a56da1a9f980f",
"local_path": "/workspace/code-sft-infra/models/olmo-3-1025-7b",
"tied_embeddings": false
},
"data": {
"path": "/workspace/code-sft-runs/t30b2507-q4b-tn-v2-lr1e4-e1-20260907/data/THINK_PAIRED_V4_2EP_O7B.parquet",
"rows": 9430,
"sha256": "17554a69f4b99d6d0b0861bece9e959a664084e00dc38ee76a5ba711e2d8da60",
"census": "/workspace/code-sft-runs/t30b2507-q4b-tn-v2-lr1e4-e1-20260907/data/TOKEN_CENSUS_V4_O7B_THINK.json",
"physical_2ep": "concat(selected_v4_table, selected_v4_table) same order; token columns overlaid from TOKEN_CENSUS_V4; O7B also rewrites renderer_id",
"source_1ep_path": "/workspace/code-sft-runs/t30b2507-q4b-tn-v2-lr1e4-e1-20260907/data/THINK_PAIRED_V4.parquet",
"source_1ep_sha256": "cf37c85413f69339508004611e196e42abbc2bddbd3462c209a1b97e1d820002",
"source_1ep_rows": 4715
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"seeds": {
"train": 42,
"eval": 3407
},
"parameters": {
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"target_assistant_tokens": 63378772,
"active_tokens_1ep": 31689386,
"checkpoint_target_tokens": [
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"milestone_updates": [
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"warmup_target_tokens": 3802726,
"learning_rate": 0.0001,
"row_lr": 0.0001,
"optimizer": "adamw",
"optimizer_betas": [
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],
"optimizer_eps": 1e-08,
"lora_rank": 64,
"lora_alpha": 128,
"lora_dropout": 0.0,
"lora_target_modules": [
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"k_proj",
"v_proj",
"o_proj",
"gate_proj",
"up_proj",
"down_proj"
],
"lora_weight_decay": 0.1,
"row_weight_decay": 0.0,
"max_grad_norm": 1.0,
"tokens_per_optimizer_update": 65536,
"packing": false,
"truncation": false,
"repeat_examples": false,
"precision": "bfloat16",
"trainable_dtype": "float32",
"gradient_checkpointing": true,
"eos_weight": 1.0,
"scheduler": "cosine_by_assistant_target_token_dose",
"worker_lr_scale": "rstar_dual_worker_v4._lr_scale",
"supervised_tail": [
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"trainable_token_indices": {
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"lm_head": [
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},
"untie_tied_embeddings": true,
"context": 32768,
"eval": {
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"seed": 3407,
"temperature": 0.6,
"top_p": 0.95,
"top_k": 20,
"ctx": 32768,
"stop_ids": [
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"base_reeval_same_mode": true
}
},
"code": {
"path": "/workspace/tools/rstar_dual_worker_v4.py",
"commit_or_sha256": "97a298e9ee4f0bb571baa88c7d75285ade2ea2a8a9b5875b35db25b5db9b3842",
"template": "/workspace/code-sft-runs/t30b2507-q4b-tn-v2-lr1e4-e1-20260907/templates/O7B-THINK.template_v3.json"
},
"started_at": "2026-09-08T07:50:11Z",
"endpoint": "2EP_ONLY @63378772 assistant tokens; no checkpoint picking",
"status": "RUNNING"
}
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