Instructions to use issai/foggen-r15-oe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use issai/foggen-r15-oe with Transformers:

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

pipe = pipeline("text-generation", model="issai/foggen-r15-oe")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("issai/foggen-r15-oe")
model = AutoModelForCausalLM.from_pretrained("issai/foggen-r15-oe")
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

vLLM

How to use issai/foggen-r15-oe with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "issai/foggen-r15-oe"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "issai/foggen-r15-oe",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/issai/foggen-r15-oe

SGLang

How to use issai/foggen-r15-oe 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 "issai/foggen-r15-oe" \
    --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": "issai/foggen-r15-oe",
		"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 "issai/foggen-r15-oe" \
        --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": "issai/foggen-r15-oe",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use issai/foggen-r15-oe with Docker Model Runner:
```
docker model run hf.co/issai/foggen-r15-oe
```

FogGen R15-OE: open-ended task-type extension of FogGen

One additional round of FogGen training on top of issai/foggen extending the recipe to three open-ended task types: extractive reading comprehension, adversarial factual recall, and chain-of-thought math.

This is the open-ended ablation from the FogGen paper. It is not a chain continuation; it's a parallel branch from the R14 chain endpoint, used to demonstrate that the recipe extends beyond MCQ. For deployment on MCQ tasks the canonical model is issai/foggen. This ablation model is for reproducibility and open-ended use cases.

Training

Base: R14 merged (the canonical chain endpoint, issai/foggen)
Round: one additional SFT round, same recipe as every R8–R14 chain round
SFT pool (1876 rows):
- 133 SQuAD v1.1 train rows (extractive RC, English)
- 91 TruthfulQA val rows (adversarial factual recall, English)
- 152 GSM8K train rows (chain-of-thought math, English), using the model's own correct reasoning traces as SFT targets
- - 1500 R14 replay rows
Hyperparameters: identical to R14 chain (LoRA r=16 α=32 all-linear, bf16, 2 epochs, lr=5e-5)
Training cost: 49 seconds on 4× H100

Results

Local accuracy gains over R14 zero-shot:

Task	R14 raw	R15-OE raw	Δ
SQuAD v1.1	81.0%	86.5%	+5.5
TruthfulQA-gen	36.5%	40.0%	+3.5
GSM8K (CoT)	52.0%	58.0%	+6.0
Mean	56.5%	61.5%	+5.0

MCQ chain capability preservation (mean across 7 in-chain MCQ domains): 55.6% raw at R15-OE vs. 57.3% at R14 (within inter-round variance).

Routing lift over Random at τ=0.5:

SQuAD CA: R14 +1.4 → R15-OE +0.4 (headroom collapse: raw acc closes to cloud)
TruthfulQA CA: R14 −0.7 → R15-OE −2.3 (anti-calibrated, dataset-inherent; see Tian et al., 2023)
GSM8K CoT: R14 +2.2 → R15-OE +5.5 (routing improves with training)

How this differs from issai/foggen

	`issai/foggen` (R14)	`issai/foggen-r15-oe` (this)
Use case	MCQ deployment	Open-ended task types + reproducibility
Training rounds	14 sequential	14 + 1 OE extension
Trained tasks	7 MCQ domains	7 MCQ + SQuAD + TQA + GSM8K
MCQ acc (mean)	57.3% raw	55.6% raw
Chain narrative	canonical endpoint	parallel ablation branch

Quick demo

Same usage pattern as issai/foggen for MCQ. For open-ended tasks use the per-task system prompts described in the paper.

from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained(
    "issai/foggen-r15-oe", torch_dtype="bfloat16", device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("issai/foggen-r15-oe")

# Example: GSM8K with chain-of-thought
SYSTEM_GSM = """You are a self-aware math assistant.

Rules:
- Do not output <think> tags.
- First, show your step-by-step reasoning to solve the problem.
- Then assess your confidence in your answer.
- Then give your final numeric answer.
- Output format:
  Reasoning: <your step-by-step work>
  Confidence: <0.0|0.25|0.5|0.75|1.0>
  Final answer: <NUMBER>"""