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Model: neuralmagic/Nous-Hermes-2-SOLAR-10.7B-pruned2.4
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---
base_model: NousResearch/Nous-Hermes-2-SOLAR-10.7B
inference: True
model_type: Llama
tags:
- nm-vllm
- sparse
---
## Nous-Hermes-2-SOLAR-10.7B-pruned2.4
This repo contains model files for [Nous-Hermes-2-SOLAR-10.7B](https://huggingface.co/NousResearch/Nous-Hermes-2-SOLAR-10.7B) 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/Nous-Hermes-2-SOLAR-10.7B-pruned2.4", sparsity="semi_structured_sparse_w16a16")
prompt = "How to make banana bread?"
formatted_prompt = f"<|im_start|>User:{prompt}\n<|im_start|>assistant:\n"
sampling_params = SamplingParams(max_tokens=100)
outputs = model.generate(formatted_prompt, sampling_params=sampling_params)
print(outputs[0].outputs[0].text)
"""
To make banana bread, you will need to follow these steps:
1. Gather the ingredients needed for the recipe: flour, sugar, eggs, baking powder, and banana bread.
2. Preparing the bread by kneading the flour, butter, and sugar in a bowl until it becomes soft.
3. Then, knead the bread with the flour eggs and sugar then bake them in a microwave oven.
4. Once done baking a
"""
```
## Prompt template
```
### User:
{prompt}
### Assistant:"
```
## 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 = "NousResearch/Nous-Hermes-2-SOLAR-10.7B"
calibration_dataset = "open_platypus"
output_directory = "output/"
recipe = """
test_stage:
obcq_modifiers:
SparseGPTModifier:
sparsity: 0.5
sequential_update: true
mask_structure: '2:4'
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)

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{
"_name_or_path": "NousResearch/Nous-Hermes-2-SOLAR-10.7B",
"architectures": [
"LlamaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 1,
"eos_token_id": 32000,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 4096,
"model_type": "llama",
"num_attention_heads": 32,
"num_hidden_layers": 48,
"num_key_value_heads": 8,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_scaling": null,
"rope_theta": 10000.0,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "1.7.0.20240130",
"use_cache": false,
"vocab_size": 32002
}

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"_from_model_config": true,
"bos_token_id": 1,
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"transformers_version": "1.7.0.20240130",
"use_cache": false
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recipe.yaml Normal file
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test_stage:
obcq_modifiers:
SparseGPTModifier:
sparsity: 0.5
block_size: 128
sequential_update: true
quantize: false
percdamp: 0.01
mask_structure: '2:4'
targets: ['re:model.layers.\d*$']

30
special_tokens_map.json Normal file
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{
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"single_word": false
},
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"rstrip": false,
"single_word": false
}
}

91140
tokenizer.json Normal file

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BIN
tokenizer.model (Stored with Git LFS) Normal file

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61
tokenizer_config.json Normal file
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{
"add_bos_token": true,
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"added_tokens_decoder": {
"0": {
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"special": true
},
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"special": true
},
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},
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}
},
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"bos_token": "<s>",
"chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
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}