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

Libraries

How to use issai/foggen with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("issai/foggen")
model = AutoModelForCausalLM.from_pretrained("issai/foggen")
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 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "issai/foggen"
# 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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/issai/foggen

SGLang

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

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

CCRss commited on 5 days ago

Commit

3dee25e

verified ·

1 Parent(s): 3c4700c

README: embed system overview figure with caption at top

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@@ -20,7 +20,12 @@ library_name: transformers
 > **A 0.6B parameter edge LLM trained to emit a calibrated verbalized confidence score before its answer, enabling efficient edge–cloud routing without an external router.**
-FogGen is a small, self-aware edge model that knows when to answer locally and when to defer to a stronger cloud model. The model emits a discrete confidence score (one of `0.0, 0.25, 0.5, 0.75, 1.0`) before producing its answer, and a routing threshold `τ` decides whether to keep the local answer or escalate to the cloud.
 The released checkpoint is the endpoint (`R14`) of a 14-round continual-learning chain that trained the model across seven domains: finance, science, coding, law, math, Kazakh culture, and medicine.

 > **A 0.6B parameter edge LLM trained to emit a calibrated verbalized confidence score before its answer, enabling efficient edge–cloud routing without an external router.**
+![FogGen overview: (a) self-aware routing at inference, (b) self-evolving training loop](./foggen_overview.png)
+**At a glance.** FogGen is a small, self-aware edge model that knows when to answer locally and when to defer to a stronger cloud model. The figure above summarizes the two halves of the recipe:
+- **(a) Inference — self-aware routing.** The edge model `M_N` (Qwen3-0.6B) processes a query and emits two output spans in one forward pass: a *confidence span* (positions 1–8, e.g. `Confidence: 0.75`) followed by an *answer span* (positions 10–13, e.g. `Final answer: B`). The routing decision compares the parsed confidence `c` to a threshold `τ`: if `c ≥ τ` the edge answer is returned; otherwise the query is routed to the cloud model.
+- **(b) Training — self-evolving data loop.** Each round consumes a cloud-labeled dataset (Stage 1), uses the current checkpoint `M_N` to self-sample 8 generations per question at T=0.7 to derive a confidence bucket via the `k correct → bucket` mapping (Stage 2), then trains the next checkpoint `M_{N+1}` on the resulting `(question, confidence, answer)` triples via SFT with LoRA merge (Stage 3).
 The released checkpoint is the endpoint (`R14`) of a 14-round continual-learning chain that trained the model across seven domains: finance, science, coding, law, math, Kazakh culture, and medicine.