Delete llama2.c-stories110M-pruned50

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llama2.c-stories110M-pruned50/README.md

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----
-base_model: Xenova/llama2.c-stories110M
-inference: true
-model_type: llama
-quantized_by: mgoin
-tags:
-- nm-vllm
-- sparse
----
-
-## llama2.c-stories110M-pruned50
-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.
-
-This model was pruned with [SparseGPT](https://arxiv.org/abs/2301.00774), using [SparseML](https://github.com/neuralmagic/sparseml).
-
-## Inference
-Install [NM-vLLM](https://github.com/neuralmagic/nm-vllm) for fast inference and low memory-usage: 
-```bash
-pip install nm-vllm[sparse]
-```
-Run in a Python pipeline for local inference:
-```python
-from vllm import LLM, SamplingParams
-
-model = LLM("nm-testing/llama2.c-stories110M-pruned50", sparsity="sparse_w16a16")
-prompt = "Hello my name is"
-
-sampling_params = SamplingParams(max_tokens=100, temperature=0)
-outputs = model.generate(prompt, sampling_params=sampling_params)
-print(outputs[0].outputs[0].text)
-```
-
-## Prompt template
-
-N/A
-
-## Sparsification
-For details on how this model was sparsified, see the `recipe.yaml` in this repo and follow the instructions below.
-
-Install [SparseML](https://github.com/neuralmagic/sparseml):
-```bash
-git clone https://github.com/neuralmagic/sparseml
-pip install -e "sparseml[transformers]"
-```
-
-Replace the recipe as you like and run this one-shot compression script to apply SparseGPT:
-```python
-import sparseml.transformers
-
-original_model_name = "Xenova/llama2.c-stories110M"
-calibration_dataset = "open_platypus"
-output_directory = "output/"
-
-recipe = """
-test_stage:
-  obcq_modifiers:
-    SparseGPTModifier:
-      sparsity: 0.5
-      sequential_update: true
-      targets: ['re:model.layers.\d*$']
-"""
-
-# Apply SparseGPT to the model
-sparseml.transformers.oneshot(
-    model=original_model_name,
-    dataset=calibration_dataset,
-    recipe=recipe,
-    output_dir=output_directory,
-)
-```
-
-## Slack
-
-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)

llama2.c-stories110M-pruned50/config.json

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-{
-  "_name_or_path": "/home/damian/.cache/huggingface/hub/models--neuralmagic--llama2.c-stories110M-pruned50/snapshots/ae99c0a865dc99a60311e3fd3fc145eb339cff77",
-  "architectures": [
-    "LlamaForCausalLM"
-  ],
-  "attention_bias": false,
-  "attention_dropout": 0.0,
-  "bos_token_id": 1,
-  "eos_token_id": 2,
-  "hidden_act": "silu",
-  "hidden_size": 768,
-  "initializer_range": 0.02,
-  "intermediate_size": 2048,
-  "max_position_embeddings": 1024,
-  "model_type": "llama",
-  "num_attention_heads": 12,
-  "num_hidden_layers": 12,
-  "num_key_value_heads": 12,
-  "pretraining_tp": 1,
-  "rms_norm_eps": 1e-05,
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-  "rope_theta": 10000.0,
-  "sparsity_config": {
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-    "global_sparsity": 38.77294698534069,
-    "sparsity_structure": "0:0"
-  },
-  "tie_word_embeddings": true,
-  "torch_dtype": "float32",
-  "transformers_version": "4.39.3",
-  "use_cache": true,
-  "vocab_size": 32000
-}

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llama2.c-stories110M-pruned50/recipe.yaml

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-test_stage:
-  obcq_modifiers:
-    SparseGPTModifier:
-      sparsity: 0.5
-      sequential_update: true
-      targets: ['re:model.layers.\d*$']

llama2.c-stories110M-pruned50/special_tokens_map.json

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llama2.c-stories110M-pruned50/tokenizer_config.json

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