Instructions to use eci-io/climategpt-7b-fsc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use eci-io/climategpt-7b-fsc with Transformers:

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

pipe = pipeline("text-generation", model="eci-io/climategpt-7b-fsc")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("eci-io/climategpt-7b-fsc")
model = AutoModelForCausalLM.from_pretrained("eci-io/climategpt-7b-fsc", device_map="auto")
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 Settings

vLLM

How to use eci-io/climategpt-7b-fsc with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "eci-io/climategpt-7b-fsc"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "eci-io/climategpt-7b-fsc",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/eci-io/climategpt-7b-fsc

SGLang

How to use eci-io/climategpt-7b-fsc 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 "eci-io/climategpt-7b-fsc" \
    --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": "eci-io/climategpt-7b-fsc",
		"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 "eci-io/climategpt-7b-fsc" \
        --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": "eci-io/climategpt-7b-fsc",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use eci-io/climategpt-7b-fsc with Docker Model Runner:
```
docker model run hf.co/eci-io/climategpt-7b-fsc
```

fowleras commited on Jan 18, 2024

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@@ -20,7 +20,7 @@ co2_eq_emissions:
 </blockquote>
 ClimateGPT is a family of AI models designed to synthesize interdisciplinary research on climate change.
-ClimateGPT-7B-FSB (from scratch climate) is a 7 billion parameter transformer decoder model that was pre-trained for 319.5B tokens including a collection of 4.2B tokens from curated climate documents.
 The model is further instruction fine-tuned on a dataset of instruction-completion pairs manually collected by AppTek in cooperation with climate scientists.
 [ClimateGPT-7B](https://huggingface.co/eci-io/climategpt-7b) outperforms Llama-2-70B Chat on our climate-specific benchmarks.
 The model is designed to be used together with retrieval augmentation to extend the knowledge, and increase the factuality of the model and with cascaded machine translation to increase the language coverage.

 </blockquote>
 ClimateGPT is a family of AI models designed to synthesize interdisciplinary research on climate change.
+ClimateGPT-7B-FSC (from scratch climate) is a 7 billion parameter transformer decoder model that was pre-trained for 319.5B tokens including a collection of 4.2B tokens from curated climate documents.
 The model is further instruction fine-tuned on a dataset of instruction-completion pairs manually collected by AppTek in cooperation with climate scientists.
 [ClimateGPT-7B](https://huggingface.co/eci-io/climategpt-7b) outperforms Llama-2-70B Chat on our climate-specific benchmarks.
 The model is designed to be used together with retrieval augmentation to extend the knowledge, and increase the factuality of the model and with cascaded machine translation to increase the language coverage.