Instructions to use HerrHruby/MR_midtrain_9B_v4_half with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HerrHruby/MR_midtrain_9B_v4_half with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="HerrHruby/MR_midtrain_9B_v4_half")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HerrHruby/MR_midtrain_9B_v4_half")
model = AutoModelForCausalLM.from_pretrained("HerrHruby/MR_midtrain_9B_v4_half", 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 HerrHruby/MR_midtrain_9B_v4_half with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HerrHruby/MR_midtrain_9B_v4_half"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HerrHruby/MR_midtrain_9B_v4_half",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/HerrHruby/MR_midtrain_9B_v4_half

SGLang

How to use HerrHruby/MR_midtrain_9B_v4_half 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 "HerrHruby/MR_midtrain_9B_v4_half" \
    --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": "HerrHruby/MR_midtrain_9B_v4_half",
		"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 "HerrHruby/MR_midtrain_9B_v4_half" \
        --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": "HerrHruby/MR_midtrain_9B_v4_half",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use HerrHruby/MR_midtrain_9B_v4_half with Docker Model Runner:
```
docker model run hf.co/HerrHruby/MR_midtrain_9B_v4_half
```

MR_midtrain_9B_v4_half

File size: 1,893 Bytes

ef4115e

---
base_model: Qwen/Qwen3.5-9B
library_name: transformers
tags: [meta-reasoning, sft, data-scaling]
---

# MR_midtrain_9B_v4 — half corpus

A data-scaling point for [`MR_midtrain_9B_v4`](https://huggingface.co/HerrHruby/MR_midtrain_9B_v4):
the same v4 midtrain SFT recipe on **37,404 rows / 2,244 problems**, which is
the half of the full corpus
([`MR_midtrain_V4_sft`](https://huggingface.co/datasets/HerrHruby/MR_midtrain_V4_sft),
74,796 rows / 4,518 problems).

## Scaling result

| corpus | rows | problems | converged val loss |
|---|---|---|---|
| full | 74,796 | 4,518 | 0.6444 |
| half | 37,404 | 2,244 | 0.6586 |
| quarter | 18,714 | 1,095 | 0.6734 |

All three lie on `val_loss = 0.6444 + 0.0145 * log2(74796/rows)` to within
0.0003 — each halving of the corpus costs ~0.0145 val loss, log-linear across
the whole 4x range with no threshold.

**This is held-out next-token loss, not a judged score.** Scaffold-eval numbers
for these checkpoints are not yet measured.

## Corpus construction

Scaling points are **nested** (quarter ⊂ half ⊂ full), split by *problem* rather
than by row: rows within a problem are different layers of one exploration, so a
row-level split would keep nearly every problem and measure intra-trajectory
redundancy instead of corpus size. The validation set is byte-identical at every
point.

## Training

Identical to the reference run except corpus size: Qwen3.5-9B + 4 MR special
tokens, bf16 FSDP, max_length 65536, effective batch 128, lr 2e-5 cosine to
0.01x, warmup 3%, weight decay 0.01, 6 epochs. Stopped at step 1600 of 1752 (91.3% through the cosine schedule, lr ~5.7e-7 vs a final 2.0e-7) because val loss had been flat to four decimals for 300 steps and the remaining allocation was better spent on the quarter run.

Uses the same `<direction>` / `<summary>` scaffold protocol as v4 — see the
base model card for the prompt format.