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README.md ADDED
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+ ---
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+ base_model: Xenova/llama2.c-stories110M
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+ inference: true
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+ model_type: llama
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+ quantized_by: mgoin
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+ tags:
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+ - nm-vllm
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+ - sparse
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+ ---
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+
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+ ## llama2.c-stories110M-pruned50
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+ This repo contains model files for [llama2.c 110M tinystories](https://huggingface.co/Xenova/llama2.c-stories110M) optimized for [NM-vLLM](https://github.com/neuralmagic/nm-vllm), a high-throughput serving engine for compressed LLMs.
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+
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+ This model was pruned with [SparseGPT](https://arxiv.org/abs/2301.00774), using [SparseML](https://github.com/neuralmagic/sparseml).
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+
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+ ## Inference
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+ Install [NM-vLLM](https://github.com/neuralmagic/nm-vllm) for fast inference and low memory-usage:
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+ ```bash
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+ pip install nm-vllm[sparse]
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+ ```
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+ Run in a Python pipeline for local inference:
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+ ```python
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+ from vllm import LLM, SamplingParams
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+
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+ model = LLM("nm-testing/llama2.c-stories110M-pruned50", sparsity="sparse_w16a16")
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+ prompt = "Hello my name is"
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+
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+ sampling_params = SamplingParams(max_tokens=100, temperature=0)
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+ outputs = model.generate(prompt, sampling_params=sampling_params)
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+ print(outputs[0].outputs[0].text)
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+ ```
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+
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+ ## Prompt template
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+
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+ N/A
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+
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+ ## Sparsification
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+ For details on how this model was sparsified, see the `recipe.yaml` in this repo and follow the instructions below.
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+
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+ Install [SparseML](https://github.com/neuralmagic/sparseml):
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+ ```bash
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+ git clone https://github.com/neuralmagic/sparseml
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+ pip install -e "sparseml[transformers]"
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+ ```
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+
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+ Replace the recipe as you like and run this one-shot compression script to apply SparseGPT:
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+ ```python
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+ import sparseml.transformers
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+
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+ original_model_name = "Xenova/llama2.c-stories110M"
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+ calibration_dataset = "open_platypus"
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+ output_directory = "output/"
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+
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+ recipe = """
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+ test_stage:
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+ obcq_modifiers:
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+ SparseGPTModifier:
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+ sparsity: 0.5
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+ sequential_update: true
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+ targets: ['re:model.layers.\d*$']
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+ """
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+
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+ # Apply SparseGPT to the model
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+ sparseml.transformers.oneshot(
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+ model=original_model_name,
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+ dataset=calibration_dataset,
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+ recipe=recipe,
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+ output_dir=output_directory,
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+ )
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+ ```
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+
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+ ## Slack
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+
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+ For further support, and discussions on these models and AI in general, join [Neural Magic's Slack Community](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ)
config.json ADDED
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+ "architectures": [
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+ "LlamaForCausalLM"
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+ "attention_bias": false,
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+ "attention_dropout": 0.0,
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+ "bos_token_id": 1,
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+ "max_position_embeddings": 1024,
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+ "pretraining_tp": 1,
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+ "format": "sparse_bitmask",
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+ "global_sparsity": 38.77294698534069,
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+ "sparsity_structure": "0:0"
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+ },
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+ "tie_word_embeddings": true,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.39.3",
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+ "use_cache": true,
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+ "vocab_size": 32000
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+ }
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+ sparsity: 0.5
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+ sequential_update: true
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+ targets: ['re:model.layers.\d*$']
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