Instructions to use LLM360/Crystal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LLM360/Crystal with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LLM360/Crystal", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("LLM360/Crystal", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use LLM360/Crystal with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LLM360/Crystal" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LLM360/Crystal", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/LLM360/Crystal
- SGLang
How to use LLM360/Crystal 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 "LLM360/Crystal" \ --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": "LLM360/Crystal", "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 "LLM360/Crystal" \ --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": "LLM360/Crystal", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use LLM360/Crystal with Docker Model Runner:
docker model run hf.co/LLM360/Crystal
Commit ·
57b9d49
1
Parent(s): 4d41e2b
Update README.md
Browse files
README.md
CHANGED
|
@@ -22,7 +22,7 @@ By comparing CrystalCoder with other similar work, CrystalCoder is quite balance
|
|
| 22 |
|
| 23 |
| Model | Trained Tokens | ARC | HellaSwag | MMLU (5-shot) | TruthfulQA | Language Avg. | HumanEval (pass@1) | MBPP (pass@1) | Coding Avg. | Avg. of Avg.|
|
| 24 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|
| 25 |
-
| Mistral 7B | - | 59.98 | 83.31 | 64.16 | 42.15 |
|
| 26 |
| **CrystalCoder 7B** | 1.4T | 47.01 | 71.97 | 48.78 | 35.91 | 50.92 | 28.38 | 36.38 | 32.38 | 41.65 |
|
| 27 |
| CodeLlaMA 7B | 2.5T | 39.93 | 60.80 | 31.12 | 37.82 | 42.42 | 33.50 | 41.40 | 37.45 | 39.94 |
|
| 28 |
| OpenLLaMA v2 7B | 1T | 43.60 | 72.20 | 41.29 | 35.54 | 48.18 | 15.32 | 12.69 | 28.01 | 38.10 |
|
|
|
|
| 22 |
|
| 23 |
| Model | Trained Tokens | ARC | HellaSwag | MMLU (5-shot) | TruthfulQA | Language Avg. | HumanEval (pass@1) | MBPP (pass@1) | Coding Avg. | Avg. of Avg.|
|
| 24 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|
| 25 |
+
| Mistral 7B | - | 59.98 | 83.31 | 64.16 | 42.15 | 62.40 | 29.12 | 38.78 | 33.95 | 48.68 |
|
| 26 |
| **CrystalCoder 7B** | 1.4T | 47.01 | 71.97 | 48.78 | 35.91 | 50.92 | 28.38 | 36.38 | 32.38 | 41.65 |
|
| 27 |
| CodeLlaMA 7B | 2.5T | 39.93 | 60.80 | 31.12 | 37.82 | 42.42 | 33.50 | 41.40 | 37.45 | 39.94 |
|
| 28 |
| OpenLLaMA v2 7B | 1T | 43.60 | 72.20 | 41.29 | 35.54 | 48.18 | 15.32 | 12.69 | 28.01 | 38.10 |
|