Instructions to use emberian/h-15b-e1-replay with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use emberian/h-15b-e1-replay with Transformers:

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

pipe = pipeline("text-generation", model="emberian/h-15b-e1-replay")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("emberian/h-15b-e1-replay")
model = AutoModelForCausalLM.from_pretrained("emberian/h-15b-e1-replay", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use emberian/h-15b-e1-replay with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "emberian/h-15b-e1-replay"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "emberian/h-15b-e1-replay",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/emberian/h-15b-e1-replay

SGLang

How to use emberian/h-15b-e1-replay 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 "emberian/h-15b-e1-replay" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "emberian/h-15b-e1-replay",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "emberian/h-15b-e1-replay" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "emberian/h-15b-e1-replay",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use emberian/h-15b-e1-replay with Docker Model Runner:
```
docker model run hf.co/emberian/h-15b-e1-replay
```

h · 1.5B — one epoch, not yet judged

Falcon-H1-1.5B-Deep-Base continued-pretrained for one epoch (471,890,484 tokens) on a private library plus room transcripts with 12.5% FineWeb-Edu replay, in JAX on a TPU v5e-8. Private and unevaluated: it has been through no benchmark audit, no room bank, and no human read. Do not treat it as a resident. The evaluated one is emberian/h-05b-replay.

slice	base	after one epoch
library holdout	3.0868	2.7052
fixed-32	3.2793	2.9028
room holdout	2.9710	2.4422

Better than the 0.5B resident on every slice (2.857 / 3.016 / 2.586), on the same 65,536-vocabulary tokenizer as its own base. Loss has never been this project's instrument: one earlier arm fixed the model's belief about when a room ends with no loss change at all, and another won on loss while losing the room.

Prompting is the room format: a frame paragraph, a blank line, then name: text turns separated by blank lines, ending with h:. Stop on "\n\n", no repetition penalty, temperature 0.7, top-p 0.9.

fp32 weights, 1,125 tensors, 1,554,872,208 parameters, 66 layers. Corpus not distributed.

Downloads last month: -

Safetensors

Model size

2B params

Tensor type

F32

Model tree for emberian/h-15b-e1-replay

Base model

tiiuae/Falcon-H1-1.5B-Deep-Base

Finetuned

(4)

this model