Instructions to use modrill/code-think-o7b-20260908 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

code-think-o7b-20260908

Research checkpoint: allenai/Olmo-3-1025-7B (a81bae42db3975be1671e27b9c9a56da1a9f980f) after V4 LoRA SFT on Qwen3-30B-A3B-Thinking-2507 traces (OLMo-tokenized V4 payload), then merged to full weights.

Repo name uses OLMo-3 because RUN_IDENTITY.model.hf_id is allenai/Olmo-3-1025-7B (not OLMo-2).

This is a research checkpoint, not a product. Single-seed diagnostic numbers only. Do not treat DEV256 as a leaderboard claim.

License: Apache-2.0, inherited from allenai/Olmo-3-1025-7B (verified from the local base README.md: license: apache-2.0).

Base, teacher, and data


Student	`allenai/Olmo-3-1025-7B` revision `a81bae42db3975be1671e27b9c9a56da1a9f980f`
Teacher	`Qwen/Qwen3-30B-A3B-Thinking-2507` traces (V4 paired think payload, OLMo renderer / tokenizer)
Problems	4715 unique problems (`source_1ep_rows`); physical 2-epoch concat = 9430 rows
Dose	31,689,386 assistant tokens / epoch (OLMo tokenizer; not the Qwen 32.4M count); endpoint 63,378,772 assistant tokens (2 epochs)
Train seed	42

Recipe

LoRA r64 / α128, dropout 0.0, seven projections: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Embeddings / lm_head frozen except two-sided trainable B-row for 100257 (<|endoftext|>). Token id from adapter/TOKEN_ROWS_META.json.
Assistant supervised tail: <|endoftext|> (100257)
LR 1e-4, AdamW (β 0.9/0.95), cosine over assistant-token dose, warmup 6% (3,802,726 / 63,378,772 tokens), weight decay 0.1
bf16, no packing, no truncation, context 32768 at train time
2 epochs, physical concat. Endpoint-only score; no checkpoint picking.
Chat template: olmo3-lcb-noprefill (no generation-prompt <think> prefill). Bundled as chat_template.jinja.

Merged weights are the 2-epoch endpoint (step-000904, 63,378,772 assistant tokens). LoRA + B-row are under adapter/.

Evaluation (DEV256)

256-problem LiveCodeBench-derived dev split. Seed 3407, think mode, no <think> prefill, max generation ~32k, sandbox-verified pass@1. Temperature 0.6, top-p 0.95, top-k 20.

Cap = generations that hit the 32k length limit without closing </think>.

Model	pass@1	Cap	Notes
code-think-o7b-20260908	56/256	149	this repo; seed 3407
Olmo-3-1025-7B (same contract, think)	15/256	104	bare base, seed 3407

Single seed. These are research checkpoints, not product scores.

Usage

Merged full weights; no PEFT required at inference. The pinned OLMo template supplies a default system turn. Do not prefill <think>.

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "modrill/code-think-o7b-20260908"
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype="bfloat16", device_map="auto"
)

messages = [{"role": "user", "content": problem_statement}]
prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
output = model.generate(
    **inputs,
    max_new_tokens=32768,
    do_sample=True,
    temperature=0.6,
    top_p=0.95,
    top_k=20,
)
print(tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False))

Stop ids used in the official eval: 100257 (<|endoftext|>), 100265 (<|im_end|>).

Repo layout

Root: merged HF weights (config.json, model.safetensors, tokenizer, generation_config.json, chat_template.jinja) plus OFFICIAL_MERGE_RECEIPT.json
adapter/: LoRA, token_rows_both_sides.safetensors, TOKEN_ROWS_META.json, checkpoint MANIFEST.json
provenance/: train RUN_IDENTITY.json, TRAINING_CONFIG.json, POLICY.json; DEV256 COMPLETE.json
MANIFEST.sha256

Optimizer / resume states are not included.

Downloads last month: 37

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for modrill/code-think-o7b-20260908

Base model

allenai/Olmo-3-1025-7B

Finetuned

(62)

this model