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Model: RedHatAI/Llama-2-7b-ultrachat200k-pruned_50
Source: Original Platform

.gitattributes

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README.md

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+---
+base_model: neuralmagic/Llama-2-7b-pruned50-retrained
+inference: true
+model_type: llama
+pipeline_tag: text-generation
+datasets:
+  - cerebras/SlimPajama-627B
+  - HuggingFaceH4/ultrachat_200k
+tags:
+- sparse
+- chat
+---
+
+# Llama-2-7b-pruned50-retrained-ultrachat
+
+This repo contains a [50% sparse Llama 2 7B](https://huggingface.co/neuralmagic/Llama-2-7b-pruned50-retrained) finetuned for chat tasks using the [UltraChat 200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) dataset.
+
+Official model weights from [Enabling High-Sparsity Foundational Llama Models with Efficient Pretraining and Deployment](https://arxiv.org/abs/2405.03594).
+
+**Authors**: Neural Magic, Cerebras
+
+## Usage
+
+Below we share some code snippets on how to get quickly started with running the model.
+
+### Sparse Transfer
+
+By leveraging a pre-sparsified model's structure, you can efficiently fine-tune on new data, leading to reduced hyperparameter tuning, training times, and computational costs. Learn about this process [here](https://neuralmagic.github.io/docs-v2/get-started/transfer).
+
+### Running the model
+
+This model may be run with the transformers library. For accelerated inference with sparsity, deploy with [nm-vllm](https://github.com/neuralmagic/nm-vllm) or [deepsparse](https://github.com/neuralmagic/deepsparse).
+
+```python
+# pip install transformers accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("neuralmagic/Llama-2-7b-pruned50-retrained-ultrachat")
+model = AutoModelForCausalLM.from_pretrained("neuralmagic/Llama-2-7b-pruned50-retrained-ultrachat", device_map="auto")
+
+input_text = "Write me a poem about Machine Learning."
+input_ids = tokenizer.apply_chat_template(input_text, add_generation_prompt=True, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**input_ids)
+print(tokenizer.decode(outputs[0]))
+```
+
+## Evaluation Benchmark Results
+
+Model evaluation metrics and results.
+
+| Benchmark                                      | Metric        | Llama-2-7b-ultrachat  | Llama-2-7b-pruned50-retrained-ultrachat |
+|------------------------------------------------|---------------|-------------|-------------------------------|
+| [MMLU](https://arxiv.org/abs/2009.03300)       | 5-shot        | 46.1%       | 41.4%                         |
+| [HellaSwag](https://arxiv.org/abs/1905.07830)  | 0-shot        | 75.9%       | 73.5%                         |
+| [WinoGrande](https://arxiv.org/abs/1907.10641) | 5-shot        | 72.6%       | 67.8%                         |
+| [ARC-c](https://arxiv.org/abs/1911.01547)      | 25-shot       | 52.8%       | 49.0%                         |
+| [TruthfulQA](https://arxiv.org/abs/2109.07958) | 5-shot        | 44.8%       | 39.5%                         |
+| [GSM8K](https://arxiv.org/abs/2110.14168)      | 5-shot        | 12.4%       | 8.0%                          |
+| [AlpacaEval](https://arxiv.org/abs/2107.03374) ([Llama-2-70b-chat-hf](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) evaluator) | Win rate  | 57.6% | 60.1% |
+| [AlpacaEval](https://arxiv.org/abs/2107.03374) (GPT-4 Turbo evaluator) | Win rate  | 60.6% | 59.0% |
+
+
+## Model Training Details
+
+This model was obtained by sparse-tranfer of the sparse foundational model [Llama-2-7b-pruned50-retrained](https://huggingface.co/neuralmagic/Llama-2-7b-pruned50-retrained) on the [ultrachat_200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) dataset.
+Training was perfomerd for 2 epochs and used the [SquareHead](https://arxiv.org/abs/2310.06927) knowledge distillation with [Llama-2-7b-ultrachat](https://huggingface.co/neuralmagic/Llama-2-7b-ultrachat) as teacher.
+
+## Help
+
+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)

arc_challenge_25shot_bs16_bf16.json

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+  "versions": {
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+  },
+  "config": {
+    "model": "sparseml",
+    "model_args": "pretrained=/network/alexandre/research/cerebras/llama2_7B_sparse50_45B_retrained/ultrachat200k/llama2_7B_45B_sparse50_LR2e-4_GC2_E2/training,dtype=bfloat16",
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+}

config.json

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+{
+  "_name_or_path": "neuralmagic/Llama-2-7b-pruned50-retrained-ultrachat",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 4096,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": false,
+  "tokenizer_class": "LlamaTokenizerFast",
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.40.0",
+  "use_cache": true,
+  "vocab_size": 32000
+}

configuration.json

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+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.40.0"
+}

gsm8k_5shot_bs16_bf16.json

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+  "results": {
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+      "acc_stderr": 0.007455924338676284
+    }
+  },
+  "versions": {
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+  "config": {
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+    "model_args": "pretrained=/network/alexandre/research/cerebras/llama2_7B_sparse50_45B_retrained/ultrachat200k/llama2_7B_45B_sparse50_LR2e-4_GC2_E2/training,dtype=bfloat16",
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+}

hellaswag_10shot_bs16_bf16.json

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+    "description_dict": {}
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+}

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truthfulqa_mc_0shot_bs16_bf16.json

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winogrande_5shot_bs16_bf16.json

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