Instructions to use camel-ai/CAMEL-33B-Combined-Data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use camel-ai/CAMEL-33B-Combined-Data with Transformers:

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

pipe = pipeline("text-generation", model="camel-ai/CAMEL-33B-Combined-Data")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("camel-ai/CAMEL-33B-Combined-Data")
model = AutoModelForCausalLM.from_pretrained("camel-ai/CAMEL-33B-Combined-Data")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use camel-ai/CAMEL-33B-Combined-Data with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "camel-ai/CAMEL-33B-Combined-Data"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "camel-ai/CAMEL-33B-Combined-Data",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/camel-ai/CAMEL-33B-Combined-Data

SGLang

How to use camel-ai/CAMEL-33B-Combined-Data 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 "camel-ai/CAMEL-33B-Combined-Data" \
    --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": "camel-ai/CAMEL-33B-Combined-Data",
		"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 "camel-ai/CAMEL-33B-Combined-Data" \
        --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": "camel-ai/CAMEL-33B-Combined-Data",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use camel-ai/CAMEL-33B-Combined-Data with Docker Model Runner:
```
docker model run hf.co/camel-ai/CAMEL-33B-Combined-Data
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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Check out the documentation for more information.

CAMEL-33B-Combined-Data is a chat large language model obtained by finetuning LLaMA-33B model on a total of 229K conversations collected through our CAMEL framework, 100K English public conversations from ShareGPT that can be found here, and 52K instructions from Alpaca dataset that can be found here. We evaluate our model offline using EleutherAI's language model evaluation harness used by Huggingface's Open LLM Benchmark. CAMEL-33B scores an average of 64.2.

Regarding the prompt format, we follow the same prompt as LMSYS's FastChat Vicuna-13B-1.1 conversation template. It assumes a conversation between a user and AI assistant seperated by a </s> at the end of every role message. More details can be found here.

license: cc-by-nc-4.0

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	50.79
ARC (25-shot)	62.97
HellaSwag (10-shot)	83.83
MMLU (5-shot)	58.98
TruthfulQA (0-shot)	50.21
Winogrande (5-shot)	78.3
GSM8K (5-shot)	14.1
DROP (3-shot)	7.12

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Quantizations

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CAMEL-33B-Combined-Data

license: cc-by-nc-4.0

Open LLM Leaderboard Evaluation Results

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