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Model: hpcgroup/hpc-coder-v2-1.3b Source: Original Platform
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README.md
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README.md
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---
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library_name: transformers
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tags:
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- code
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- hpc
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- parallel
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- axonn
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datasets:
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- hpcgroup/hpc-instruct
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- ise-uiuc/Magicoder-OSS-Instruct-75K
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- nickrosh/Evol-Instruct-Code-80k-v1
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language:
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- en
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pipeline_tag: text-generation
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---
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# HPC-Coder-v2
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The HPC-Coder-v2-1.3b model is an HPC code LLM fine-tuned on an instruction dataset catered to common HPC topics such as parallelism, optimization, accelerator porting, etc.
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This version is a fine-tuning of the [Deepseek Coder 1.3b](https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-base) model.
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It is fine-tuned on the [hpc-instruct](https://huggingface.co/datasets/hpcgroup/hpc-instruct), [oss-instruct](https://huggingface.co/datasets/ise-uiuc/Magicoder-OSS-Instruct-75K), and [evol-instruct](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) datasets.
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We utilized the distributed training library [AxoNN](https://github.com/axonn-ai/axonn) to fine-tune in parallel across many GPUs.
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[HPC-Coder-v2-1.3b](https://huggingface.co/hpcgroup/hpc-coder-v2-1.3b), [HPC-Coder-v2-6.7b](https://huggingface.co/hpcgroup/hpc-coder-v2-6.7b), and [HPC-Coder-v2-16b](https://huggingface.co/hpcgroup/hpc-coder-v2-16b) are the most capable open-source LLMs for parallel and HPC code generation.
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HPC-Coder-v2-16b is currently the best performing open-source LLM on the [ParEval](https://github.com/parallelcodefoundry/ParEval) parallel code generation benchmark in terms of _correctness_ and _performance_.
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It scores similarly to 34B and commercial models like Phind-V2 and GPT-4 on parallel code generation.
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HPC-Coder-v2-6.7b is not far behind the 16b in terms of performance.
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## Using HPC-Coder-v2
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The model is provided as a standard huggingface model with safetensor weights.
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It can be used with [transformers pipelines](https://huggingface.co/docs/transformers/en/main_classes/pipelines), [vllm](https://github.com/vllm-project/vllm), or any other standard model inference framework.
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HPC-Coder-v2 is an instruct model and prompts need to be formatted as instructions for best results.
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It was trained with the following instruct template:
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```md
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Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Instruction:
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{instruction}
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### Response:
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```
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## Quantized Models
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4 and 8 bit quantized weights are available in the GGUF format for use with [llama.cpp](https://github.com/ggerganov/llama.cpp).
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The 4 bit model requires ~0.8 GB memory and can be found [here](https://huggingface.co/hpcgroup/hpc-coder-v2-1.3b-Q4_K_S-GGUF).
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The 8 bit model requires ~1.4 GB memory and can be found [here](https://huggingface.co/hpcgroup/hpc-coder-v2-1.3b-Q8_0-GGUF).
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Further information on how to use them with llama.cpp can be found in [its documentation](https://github.com/ggerganov/llama.cpp).
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config.json
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config.json
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{
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"_name_or_path": "deepseek-ai/deepseek-coder-1.3b-base",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 32013,
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"eos_token_id": 32014,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 5504,
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"max_position_embeddings": 16384,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 16,
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"num_hidden_layers": 24,
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"num_key_value_heads": 16,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-06,
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"rope_scaling": {
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"factor": 4.0,
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"type": "linear"
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},
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"rope_theta": 100000,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.42.3",
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"use_cache": true,
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"vocab_size": 32256
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}
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generation_config.json
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generation_config.json
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{
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"transformers_version": "4.42.3"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 2692969128
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special_tokens_map.json
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tokenizer.json
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tokenizer_config.json
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