Text Generation
Transformers
Safetensors
English
llama
Merge
Eval Results (legacy)
text-generation-inference
Instructions to use altomek/CodeRosa-70B-AB1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use altomek/CodeRosa-70B-AB1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="altomek/CodeRosa-70B-AB1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("altomek/CodeRosa-70B-AB1") model = AutoModelForCausalLM.from_pretrained("altomek/CodeRosa-70B-AB1") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use altomek/CodeRosa-70B-AB1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "altomek/CodeRosa-70B-AB1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "altomek/CodeRosa-70B-AB1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/altomek/CodeRosa-70B-AB1
- SGLang
How to use altomek/CodeRosa-70B-AB1 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 "altomek/CodeRosa-70B-AB1" \ --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": "altomek/CodeRosa-70B-AB1", "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 "altomek/CodeRosa-70B-AB1" \ --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": "altomek/CodeRosa-70B-AB1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use altomek/CodeRosa-70B-AB1 with Docker Model Runner:
docker model run hf.co/altomek/CodeRosa-70B-AB1
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README.md
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@@ -193,6 +193,7 @@ Please remember that all CodeRosa-70B-AB1 models operate under the llama2 licens
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- [2.4bpw](https://huggingface.co/altomek/CodeRosa-70B-AB1-2.4bpw-EXL2) --> 24GB VRAM
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- [measurements](https://huggingface.co/altomek/measurements/resolve/main/CodeRosa-AB1_measurement.json) --> ExLlamav2 measurments
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### [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_altomek__CodeRosa-70B-AB1)
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|Winogrande (5-shot) |81.29|
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|GSM8k (5-shot) |44.50|
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### PS
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I welcome your comments about this model.
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- [2.4bpw](https://huggingface.co/altomek/CodeRosa-70B-AB1-2.4bpw-EXL2) --> 24GB VRAM
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- [measurements](https://huggingface.co/altomek/measurements/resolve/main/CodeRosa-AB1_measurement.json) --> ExLlamav2 measurments
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### [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_altomek__CodeRosa-70B-AB1)
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|Winogrande (5-shot) |81.29|
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|GSM8k (5-shot) |44.50|
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### PS
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I welcome your comments about this model.
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