Instructions to use itpossible/ClimateChat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use itpossible/ClimateChat with Transformers:

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

pipe = pipeline("text-generation", model="itpossible/ClimateChat")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("itpossible/ClimateChat")
model = AutoModelForCausalLM.from_pretrained("itpossible/ClimateChat")
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

vLLM

How to use itpossible/ClimateChat with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/itpossible/ClimateChat

SGLang

How to use itpossible/ClimateChat 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 "itpossible/ClimateChat" \
    --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": "itpossible/ClimateChat",
		"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 "itpossible/ClimateChat" \
        --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": "itpossible/ClimateChat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use itpossible/ClimateChat with Docker Model Runner:
```
docker model run hf.co/itpossible/ClimateChat
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🎉 News

[2025-05] Paper TagRouter: Learning Route to LLMs through Tags for Open-Domain Text Generation Tasks has been accepted by the top NLP conference ACL. Model Download.
[2025-03] Paper GeoFactory: an LLM Performance Enhancement Framework for Geoscience Factual and Inferential Tasks has been accepted by the journal Big Earth Data. Data Download.
[2025-03] Paper ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries has been accepted by the International Conference on Learning Representations (ICLR). Model Download.
[2024-12] Paper JiuZhou: Open Foundation Language Models and Effective Pre-training Framework for Geoscience has been accepted by the International Journal of Digital Earth. Model Introduction. Project Repository.
[2024-09] Released chat model ClimateChat.
[2024-08] Paper PreparedLLM: Effective Pre-pretraining Framework for Domain-specific Large Language Models has been accepted by the journal Big Earth Data. WeChat article: PreparedLLM: Effective Pre-pretraining Framework for Domain-specific Large Language Models. Model Download.
[2024-08] Released chat model Chinese-Mistral-7B-Instruct-v0.2, featuring significantly improved language understanding and multi-turn conversation capabilities.
[2024-06] Released chat model JiuZhou-Instruct-v0.2, with significantly enhanced language understanding and multi-turn conversation capabilities.
[2024-05] WeChat Article: Chinese Vocabulary Expansion Incremental Pretraining for Large Language Models: Chinese-Mistral Released.
[2024-03] Released base model Chinese-Mistral-7B-v0.1 and chat model Chinese-Mistral-7B-Instruct-v0.1. Model Introduction. Project Repository.
[2024-03] Released JiuZhou's base version JiuZhou-base, instruct version JiuZhou-instruct-v0.1, and intermediate checkpoints. Model Introduction. Project Repository.
[2024-01] Completed training of Chinese-Mistral and JiuZhou, and commenced model evaluation.

Download

Model Series	Model	Download Link	Description
JiuZhou	JiuZhou-base	Huggingface	Base model (Rich in geoscience knowledge)
JiuZhou	JiuZhou-Instruct-v0.1	Huggingface	Instruct model (Instruction alignment caused a loss of some geoscience knowledge, but it has instruction-following ability) LoRA fine-tuned on Alpaca_GPT4 in both Chinese and English and GeoSignal
JiuZhou	JiuZhou-Instruct-v0.2	HuggingFace Wisemodel	Instruct model (Instruction alignment caused a loss of some geoscience knowledge, but it has instruction-following ability) Fine-tuned with high-quality general instruction data
ClimateChat	ClimateChat	HuggingFace Wisemodel	Instruct model Fine-tuned on JiuZhou-base for instruction following
Chinese-Mistral	Chinese-Mistral-7B	HuggingFace Wisemodel ModelScope	Base model
Chinese-Mistral	Chinese-Mistral-7B-Instruct-v0.1	HuggingFace Wisemodel ModelScope	Instruct model LoRA fine-tuned with Alpaca_GPT4 in both Chinese and English
Chinese-Mistral	Chinese-Mistral-7B-Instruct-v0.2	HuggingFace Wisemodel	Instruct model LoRA fine-tuned with a million high-quality instructions
PreparedLLM	Prepared-Llama	Huggingface Wisemodel	Base model Continual pretraining with a small number of geoscience data Recommended to use JiuZhou