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", 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 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
```

ClimateChat / README.md

itpossible

Update README.md

586b27f verified about 1 year ago

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	---
	license: apache-2.0
	base_model: itpossible/JiuZhou-base
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- text-generation-inference
	- inference endpoints
	---

	## 🎉 News
	- [2025-05] Paper [TagRouter: Learning Route to LLMs through Tags for Open-Domain Text Generation Tasks](https://arxiv.org/abs/2506.12473) has been accepted by the top NLP conference ACL. [Model Download](https://huggingface.co/itpossible/TagGenerator).
	- [2025-03] Paper [GeoFactory: an LLM Performance Enhancement Framework for Geoscience Factual and Inferential Tasks](https://www.tandfonline.com/doi/full/10.1080/20964471.2025.2506291) has been accepted by the journal Big Earth Data. [Data Download](https://huggingface.co/datasets/itpossible/WikiRAG).
	- [2025-03] Paper [ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries](http://arxiv.org/abs/2506.13796) has been accepted by the International Conference on Learning Representations (ICLR). [Model Download](https://huggingface.co/itpossible/ClimateChat).
	- [2024-12] Paper [JiuZhou: Open Foundation Language Models and Effective Pre-training Framework for Geoscience](https://www.tandfonline.com/doi/full/10.1080/17538947.2025.2449708) has been accepted by the International Journal of Digital Earth. [Model Introduction](https://deepwiki.com/THU-ESIS/JiuZhou). [Project Repository](https://github.com/THU-ESIS/JiuZhou).
	- [2024-09] Released chat model [ClimateChat](https://huggingface.co/itpossible/ClimateChat).
	- [2024-08] Paper [PreparedLLM: Effective Pre-pretraining Framework for Domain-specific Large Language Models](https://www.tandfonline.com/doi/full/10.1080/20964471.2024.2396159) has been accepted by the journal Big Earth Data. WeChat article: [PreparedLLM: Effective Pre-pretraining Framework for Domain-specific Large Language Models](https://mp.weixin.qq.com/s/ugJQ9tbp6Y87xA3TOWteqw). [Model Download](https://huggingface.co/itpossible/Prepared-Llama).
	- [2024-08] Released chat model [Chinese-Mistral-7B-Instruct-v0.2](https://huggingface.co/itpossible/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](https://huggingface.co/itpossible/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](https://mp.weixin.qq.com/s/PMQmRCZMWosWMfgKRBjLlQ).
	- [2024-03] Released base model [Chinese-Mistral-7B-v0.1](https://huggingface.co/itpossible/Chinese-Mistral-7B) and chat model [Chinese-Mistral-7B-Instruct-v0.1](https://huggingface.co/itpossible/Chinese-Mistral-7B-Instruct-v0.1). [Model Introduction](https://deepwiki.com/THU-ESIS/Chinese-Mistral). [Project Repository](https://huggingface.co/itpossible/Chinese-Mistral).
	- [2024-03] Released JiuZhou's base version [JiuZhou-base](https://huggingface.co/itpossible/JiuZhou-base), instruct version [JiuZhou-instruct-v0.1](https://huggingface.co/itpossible/JiuZhou-Instruct-v0.1), and [intermediate checkpoints](https://huggingface.co/itpossible). [Model Introduction](https://deepwiki.com/THU-ESIS/JiuZhou). [Project Repository](https://github.com/THU-ESIS/JiuZhou).
	- [2024-01] Completed training of Chinese-Mistral and JiuZhou, and commenced model evaluation.

	## Download

	\| Model Series \| Model \| Download Link \| Description \|
	\|-----------------------\|-------------------------------------\|------------------------------------------------------------\|------------------------------------------------------------------\|
	\| JiuZhou \| JiuZhou-base \| [Huggingface](https://huggingface.co/itpossible/JiuZhou-base) \| Base model (Rich in geoscience knowledge) \|
	\| JiuZhou \| JiuZhou-Instruct-v0.1 \| [Huggingface](https://huggingface.co/itpossible/Chinese-Mistral-7B-Instruct-v0.1) \| Instruct model (Instruction alignment caused a loss of some geoscience knowledge, but it has instruction-following ability) <br> LoRA fine-tuned on Alpaca_GPT4 in both Chinese and English and GeoSignal \|
	\| JiuZhou \| JiuZhou-Instruct-v0.2 \| [HuggingFace](https://huggingface.co/itpossible/Chinese-Mistral-7B-Instruct-v0.2)<br>[Wisemodel](https://wisemodel.cn/models/itpossible/Chinese-Mistral-7B-Instruct-v0.2) \| Instruct model (Instruction alignment caused a loss of some geoscience knowledge, but it has instruction-following ability) <br> Fine-tuned with high-quality general instruction data \|
	\| ClimateChat \| ClimateChat \| [HuggingFace](https://huggingface.co/itpossible/ClimateChat)<br>[Wisemodel](https://wisemodel.cn/models/itpossible/ClimateChat) \| Instruct model <br> Fine-tuned on JiuZhou-base for instruction following \|
	\| Chinese-Mistral \| Chinese-Mistral-7B \| [HuggingFace](https://huggingface.co/itpossible/Chinese-Mistral-7B-v0.1)<br>[Wisemodel](https://wisemodel.cn/models/itpossible/Chinese-Mistral-7B-v0.1)<br>[ModelScope](https://www.modelscope.cn/models/itpossible/Chinese-Mistral-7B-v0.1) \| Base model \|
	\| Chinese-Mistral \| Chinese-Mistral-7B-Instruct-v0.1 \| [HuggingFace](https://huggingface.co/itpossible/Chinese-Mistral-7B-Instruct-v0.1)<br>[Wisemodel](https://wisemodel.cn/models/itpossible/Chinese-Mistral-7B-Instruct-v0.1)<br>[ModelScope](https://www.modelscope.cn/models/itpossible/Chinese-Mistral-7B-Instruct-v0.1) \| Instruct model <br> LoRA fine-tuned with Alpaca_GPT4 in both Chinese and English \|
	\| Chinese-Mistral \| Chinese-Mistral-7B-Instruct-v0.2 \| [HuggingFace](https://huggingface.co/itpossible/Chinese-Mistral-7B-Instruct-v0.2)<br>[Wisemodel](https://wisemodel.cn/models/itpossible/Chinese-Mistral-7B-Instruct-v0.2) \| Instruct model <br> LoRA fine-tuned with a million high-quality instructions \|
	\| PreparedLLM \| Prepared-Llama \| [Huggingface](https://huggingface.co/itpossible/Prepared-Llama)<br>[Wisemodel](https://wisemodel.cn/models/itpossible/PREPARED-Llama) \| Base model <br> Continual pretraining with a small number of geoscience data <br> Recommended to use JiuZhou \|

	---
	license: apache-2.0
	base_model: itpossible/JiuZhou-base
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- text-generation-inference
	- inference endpoints
	---

	## 🎉 News
	- [2025-05] Paper [TagRouter: Learning Route to LLMs through Tags for Open-Domain Text Generation Tasks](https://arxiv.org/abs/2506.12473) has been accepted by the top NLP conference ACL. [Model Download](https://huggingface.co/itpossible/TagGenerator).
	- [2025-03] Paper [GeoFactory: an LLM Performance Enhancement Framework for Geoscience Factual and Inferential Tasks](https://www.tandfonline.com/doi/full/10.1080/20964471.2025.2506291) has been accepted by the journal Big Earth Data. [Data Download](https://huggingface.co/datasets/itpossible/WikiRAG).
	- [2025-03] Paper [ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries](http://arxiv.org/abs/2506.13796) has been accepted by the International Conference on Learning Representations (ICLR). [Model Download](https://huggingface.co/itpossible/ClimateChat).
	- [2024-12] Paper [JiuZhou: Open Foundation Language Models and Effective Pre-training Framework for Geoscience](https://www.tandfonline.com/doi/full/10.1080/17538947.2025.2449708) has been accepted by the International Journal of Digital Earth. [Model Introduction](https://deepwiki.com/THU-ESIS/JiuZhou). [Project Repository](https://github.com/THU-ESIS/JiuZhou).
	- [2024-09] Released chat model [ClimateChat](https://huggingface.co/itpossible/ClimateChat).
	- [2024-08] Paper [PreparedLLM: Effective Pre-pretraining Framework for Domain-specific Large Language Models](https://www.tandfonline.com/doi/full/10.1080/20964471.2024.2396159) has been accepted by the journal Big Earth Data. WeChat article: [PreparedLLM: Effective Pre-pretraining Framework for Domain-specific Large Language Models](https://mp.weixin.qq.com/s/ugJQ9tbp6Y87xA3TOWteqw). [Model Download](https://huggingface.co/itpossible/Prepared-Llama).
	- [2024-08] Released chat model [Chinese-Mistral-7B-Instruct-v0.2](https://huggingface.co/itpossible/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](https://huggingface.co/itpossible/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](https://mp.weixin.qq.com/s/PMQmRCZMWosWMfgKRBjLlQ).
	- [2024-03] Released base model [Chinese-Mistral-7B-v0.1](https://huggingface.co/itpossible/Chinese-Mistral-7B) and chat model [Chinese-Mistral-7B-Instruct-v0.1](https://huggingface.co/itpossible/Chinese-Mistral-7B-Instruct-v0.1). [Model Introduction](https://deepwiki.com/THU-ESIS/Chinese-Mistral). [Project Repository](https://huggingface.co/itpossible/Chinese-Mistral).
	- [2024-03] Released JiuZhou's base version [JiuZhou-base](https://huggingface.co/itpossible/JiuZhou-base), instruct version [JiuZhou-instruct-v0.1](https://huggingface.co/itpossible/JiuZhou-Instruct-v0.1), and [intermediate checkpoints](https://huggingface.co/itpossible). [Model Introduction](https://deepwiki.com/THU-ESIS/JiuZhou). [Project Repository](https://github.com/THU-ESIS/JiuZhou).
	- [2024-01] Completed training of Chinese-Mistral and JiuZhou, and commenced model evaluation.

	## Download

	\| Model Series \| Model \| Download Link \| Description \|
	\|-----------------------\|-------------------------------------\|------------------------------------------------------------\|------------------------------------------------------------------\|
	\| JiuZhou \| JiuZhou-base \| [Huggingface](https://huggingface.co/itpossible/JiuZhou-base) \| Base model (Rich in geoscience knowledge) \|
	\| JiuZhou \| JiuZhou-Instruct-v0.1 \| [Huggingface](https://huggingface.co/itpossible/Chinese-Mistral-7B-Instruct-v0.1) \| Instruct model (Instruction alignment caused a loss of some geoscience knowledge, but it has instruction-following ability) <br> LoRA fine-tuned on Alpaca_GPT4 in both Chinese and English and GeoSignal \|
	\| JiuZhou \| JiuZhou-Instruct-v0.2 \| [HuggingFace](https://huggingface.co/itpossible/Chinese-Mistral-7B-Instruct-v0.2)<br>[Wisemodel](https://wisemodel.cn/models/itpossible/Chinese-Mistral-7B-Instruct-v0.2) \| Instruct model (Instruction alignment caused a loss of some geoscience knowledge, but it has instruction-following ability) <br> Fine-tuned with high-quality general instruction data \|
	\| ClimateChat \| ClimateChat \| [HuggingFace](https://huggingface.co/itpossible/ClimateChat)<br>[Wisemodel](https://wisemodel.cn/models/itpossible/ClimateChat) \| Instruct model <br> Fine-tuned on JiuZhou-base for instruction following \|
	\| Chinese-Mistral \| Chinese-Mistral-7B \| [HuggingFace](https://huggingface.co/itpossible/Chinese-Mistral-7B-v0.1)<br>[Wisemodel](https://wisemodel.cn/models/itpossible/Chinese-Mistral-7B-v0.1)<br>[ModelScope](https://www.modelscope.cn/models/itpossible/Chinese-Mistral-7B-v0.1) \| Base model \|
	\| Chinese-Mistral \| Chinese-Mistral-7B-Instruct-v0.1 \| [HuggingFace](https://huggingface.co/itpossible/Chinese-Mistral-7B-Instruct-v0.1)<br>[Wisemodel](https://wisemodel.cn/models/itpossible/Chinese-Mistral-7B-Instruct-v0.1)<br>[ModelScope](https://www.modelscope.cn/models/itpossible/Chinese-Mistral-7B-Instruct-v0.1) \| Instruct model <br> LoRA fine-tuned with Alpaca_GPT4 in both Chinese and English \|
	\| Chinese-Mistral \| Chinese-Mistral-7B-Instruct-v0.2 \| [HuggingFace](https://huggingface.co/itpossible/Chinese-Mistral-7B-Instruct-v0.2)<br>[Wisemodel](https://wisemodel.cn/models/itpossible/Chinese-Mistral-7B-Instruct-v0.2) \| Instruct model <br> LoRA fine-tuned with a million high-quality instructions \|
	\| PreparedLLM \| Prepared-Llama \| [Huggingface](https://huggingface.co/itpossible/Prepared-Llama)<br>[Wisemodel](https://wisemodel.cn/models/itpossible/PREPARED-Llama) \| Base model <br> Continual pretraining with a small number of geoscience data <br> Recommended to use JiuZhou \|