Instructions to use zeeshanali01/cryptotunned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zeeshanali01/cryptotunned with Transformers:

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

pipe = pipeline("text-generation", model="zeeshanali01/cryptotunned")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zeeshanali01/cryptotunned")
model = AutoModelForCausalLM.from_pretrained("zeeshanali01/cryptotunned", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zeeshanali01/cryptotunned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zeeshanali01/cryptotunned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zeeshanali01/cryptotunned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/zeeshanali01/cryptotunned

SGLang

How to use zeeshanali01/cryptotunned 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 "zeeshanali01/cryptotunned" \
    --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": "zeeshanali01/cryptotunned",
		"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 "zeeshanali01/cryptotunned" \
        --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": "zeeshanali01/cryptotunned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use zeeshanali01/cryptotunned with Docker Model Runner:
```
docker model run hf.co/zeeshanali01/cryptotunned
```

cryptotunned / README.md

zeeshanali01

Update README.md

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	---
	license: apache-2.0
	tags:
	- trl
	- sft
	library_name: transformers
	---

	# CatMemo: Fine-Tuning Large Language Models for Financial Applications

	## Model Overview
	This model, CatMemo, is fine-tuned using Data Fusion techniques for financial applications. It was developed as part of the FinLLM Challenge Task and focuses on enhancing the performance of large language models in finance-specific tasks such as question answering, document summarization, and sentiment analysis.

	### Key Features
	- Fine-tuned on financial datasets using Supervised Fine-Tuning (SFT) techniques.
	- Optimized for Transfer Reinforcement Learning (TRL) workflows.
	- Specialized for tasks requiring domain-specific context in financial applications.

	## Usage
	You can use this model with the [Hugging Face Transformers library](https://huggingface.co/docs/transformers/) to perform financial text analysis. Below is a quick example:

	```python
	from transformers import AutoTokenizer, AutoModelForCausalLM

	# Load the model and tokenizer
	model_name = "zeeshanali01/cryptotunned"
	tokenizer = AutoTokenizer.from_pretrained(model_name)
	model = AutoModelForCausalLM.from_pretrained(model_name)

	# Tokenize input
	inputs = tokenizer("What are the key takeaways from the latest earnings report?", return_tensors="pt")

	# Generate output
	outputs = model.generate(**inputs)
	print(tokenizer.decode(outputs[0], skip_special_tokens=True))
	```

	## Training Details
	This model was fine-tuned using Data Fusion methods on domain-specific financial datasets. The training pipeline includes:
	- Preprocessing financial documents and datasets to enhance model understanding.
	- Applying Supervised Fine-Tuning (SFT) to optimize the model for financial NLP tasks.
	- Testing and evaluation on FinLLM benchmark tasks.

	## Citation
	If you use this model, please cite our work:

	```
	@inproceedings{cao2024catmemo,
	title={CatMemo at the FinLLM Challenge Task: Fine-Tuning Large Language Models using Data Fusion in Financial Applications},
	author={Cao, Yupeng and Yao, Zhiyuan and Chen, Zhi and Deng, Zhiyang},
	booktitle={Joint Workshop of the 8th Financial Technology and Natural Language Processing (FinNLP) and the 1st Agent AI for Scenario Planning (AgentScen) in conjunction with IJCAI 2023},
	pages={174},
	year={2024}
	}
	```

	## License
	This model is licensed under the Apache 2.0 License. See the [LICENSE](https://www.apache.org/licenses/LICENSE-2.0) file for details.

	## Acknowledgments
	We thank the organizers of the FinLLM Challenge Task for providing the benchmark datasets and tasks used to develop this model.

	---
	### Model Card Metadata
	- License: Apache 2.0
	- Tags: TRL, SFT
	- Library Used: Transformers