Instructions to use c-bone/CrystaLLM-pi_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use c-bone/CrystaLLM-pi_base with Transformers:

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

pipe = pipeline("text-generation", model="c-bone/CrystaLLM-pi_base")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("c-bone/CrystaLLM-pi_base")
model = AutoModelForCausalLM.from_pretrained("c-bone/CrystaLLM-pi_base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use c-bone/CrystaLLM-pi_base with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/c-bone/CrystaLLM-pi_base

SGLang

How to use c-bone/CrystaLLM-pi_base 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 "c-bone/CrystaLLM-pi_base" \
    --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": "c-bone/CrystaLLM-pi_base",
		"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 "c-bone/CrystaLLM-pi_base" \
        --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": "c-bone/CrystaLLM-pi_base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use c-bone/CrystaLLM-pi_base with Docker Model Runner:
```
docker model run hf.co/c-bone/CrystaLLM-pi_base
```

c-bone commited on Sep 8

Commit

ff52b5e

verified ·

1 Parent(s): 396ebdd

Training config as in the reproduction repo

Browse files

Files changed (1) hide show

training_config.jsonc +1 -3

training_config.jsonc CHANGED Viewed

@@ -54,9 +54,7 @@
   "early_stopping_patience": 10,
   "early_stopping_threshold": 0.00001,
   // Utils
-  // 42 is what the released checkpoint's training_args.bin records: this run predates the
-  // seed being passed through from the config, so the Trainer default applied.
-  "seed": 42,
   "data_seed": 1,
   "load_best_model_at_end": true,
   "metric_for_best_model": "eval_loss",

   "early_stopping_patience": 10,
   "early_stopping_threshold": 0.00001,
   // Utils
+  "seed": 1,
   "data_seed": 1,
   "load_best_model_at_end": true,
   "metric_for_best_model": "eval_loss",