Text Generation
Transformers
PyTorch
English
crystalcoder
llm
code
custom_code
Eval Results (legacy)
Instructions to use IFM/CrystalChat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IFM/CrystalChat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="IFM/CrystalChat", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("IFM/CrystalChat", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use IFM/CrystalChat with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "IFM/CrystalChat" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "IFM/CrystalChat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/IFM/CrystalChat
- SGLang
How to use IFM/CrystalChat 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 "IFM/CrystalChat" \ --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": "IFM/CrystalChat", "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 "IFM/CrystalChat" \ --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": "IFM/CrystalChat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use IFM/CrystalChat with Docker Model Runner:
docker model run hf.co/IFM/CrystalChat
Update README.md
Browse files
README.md
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The summary of the instruction tuning data is as follows:
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<center><img src="data_table.jpg" alt="Instruction Data"/></center>
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# Instruction Format
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In the above code, the list `[1, 2, 3, 4, 5]` is passed as an argument to the `squared_sum_list` function. The function calculates the sum of the squares of the elements in the list, which is `1 + 4 + 9 + 16 + 25 = 55`. The function then returns this result, which is printed to the console.</s>
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<!-- ## CrystalChat DataMix
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| Subset | Tokens (Billion) |
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| ----------- | ----------- |
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| OASST1-guanaco | 4.46 |
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| SlimOrca | 225.63 |
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| ShareGPT | 112.91 |
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| Evol-ShareGPT | 85.95 |
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| ChatLogs | 29.34 |
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| CodeAlpaca | 2.62 |
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| Rosetta Code | 7.99 |
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| Evol-CodeAlpaca 1 | 73.80 |
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| Evol-CodeAlpaca 2 | 34.91 |
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| HTML Instruction | 43.67 |
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| General Textbooks | 85.59 |
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| Programming Books | 395.63 |
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| Total | 1102.52 | -->
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# Evaluation
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Coming Soon!
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The summary of the instruction tuning data is as follows:
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<!-- <center><img src="data_table.jpg" alt="Instruction Data"/></center> -->
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## CrystalChat DataMix
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| Subset | Tokens (Billion) |
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| ----------- | ----------- |
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| OASST1-guanaco | 4.46 |
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| SlimOrca | 225.63 |
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| ShareGPT | 112.91 |
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| Evol-ShareGPT | 85.95 |
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| ChatLogs | 29.34 |
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| CodeAlpaca | 2.62 |
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| Rosetta Code | 7.99 |
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| Evol-CodeAlpaca 1 | 73.80 |
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| Evol-CodeAlpaca 2 | 34.91 |
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| HTML Instruction | 43.67 |
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| General Textbooks | 85.59 |
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| Programming Books | 395.63 |
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| Total | 1102.52 |
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# Instruction Format
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In the above code, the list `[1, 2, 3, 4, 5]` is passed as an argument to the `squared_sum_list` function. The function calculates the sum of the squares of the elements in the list, which is `1 + 4 + 9 + 16 + 25 = 55`. The function then returns this result, which is printed to the console.</s>
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# Evaluation
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Coming Soon!
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