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---
pipeline_tag: text-generation
datasets:
- bigcode/the-stack-v2-train
license: bigcode-openrail-m
library_name: transformers
tags:
- code
model-index:
- name: starcoder2-7b-quantized.w8a8
results:
- task:
type: text-generation
dataset:
name: HumanEval+
type: humanevalplus
metrics:
- type: pass@1
value: 29.3
- task:
type: text-generation
dataset:
name: HumanEval
type: humaneval
metrics:
- type: pass@1
value: 33.9
---
# starcoder2-7b-quantized.w8a8
## Model Overview
- **Model Architecture:** StarCoder2
- **Input:** Text
- **Output:** Text
- **Model Optimizations:**
- **Activation quantization:** INT8
- **Weight quantization:** INT8
- **Intended Use Cases:** Intended for commercial and research use. Similarly to [starcoder2-7b](https://huggingface.co/bigcode/starcoder2-7b), this model is intended for code generation and is _not_ an instruction model. Commands like "Write a function that computes the square root." do not work well.
- **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws).
- **Release Date:** 8/1/2024
- **Version:** 1.0
- **License(s):** bigcode-openrail-m
- **Model Developers:** Neural Magic
Quantized version of [starcoder2-7b](https://huggingface.co/bigcode/starcoder2-7b).
It achieves a HumanEval pass@1 of 33.9, whereas the unquantized model achieves 34.9 when evaluated under the same conditions.
### Model Optimizations
This model was obtained by quantizing the weights of [starcoder2-7b](https://huggingface.co/bigcode/starcoder2-7b) to INT8 data type.
This optimization reduces the number of bits used to represent weights and activations from 16 to 8, reducing GPU memory requirements (by approximately 50%) and increasing matrix-multiply compute throughput (by approximately 2x).
Weight quantization also reduces disk size requirements by approximately 50%.
Only weights and activations of the linear operators within transformers blocks are quantized.
Weights are quantized with a symmetric static per-channel scheme, where a fixed linear scaling factor is applied between INT8 and floating point representations for each output channel dimension.
Activations are quantized with a symmetric dynamic per-token scheme, computing a linear scaling factor at runtime for each token between INT8 and floating point representations.
The [GPTQ](https://arxiv.org/abs/2210.17323) algorithm is applied for quantization, as implemented in the [llm-compressor](https://github.com/vllm-project/llm-compressor) library.
GPTQ used a 1% damping factor and 256 sequences of 8,192 random tokens.
## Deployment
### Use with vLLM
This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
```python
from vllm import LLM, SamplingParams
from transformers import AutoTokenizer
model_id = "neuralmagic/starcoder2-7b-quantized.w8a8"
number_gpus = 1
sampling_params = SamplingParams(temperature=0.2, top_p=0.95, max_tokens=256)
tokenizer = AutoTokenizer.from_pretrained(model_id)
prompts = ["def print_hello_world():"]
llm = LLM(model=model_id, tensor_parallel_size=number_gpus)
outputs = llm.generate(prompts, sampling_params)
generated_text = outputs[0].outputs[0].text
print(generated_text)
```
vLLM aslo supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
## Creation
This model was created by using the [llm-compressor](https://github.com/vllm-project/llm-compressor) library as presented in the code snipet below.
```python
from transformers import AutoTokenizer
from datasets import Dataset
from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot
from llmcompressor.modifiers.quantization import GPTQModifier
import random
model_id = "bigcode/starcoder2-7b"
num_samples = 256
max_seq_len = 8192
tokenizer = AutoTokenizer.from_pretrained(model_id)
max_token_id = len(tokenizer.get_vocab()) - 1
input_ids = [[random.randint(0, max_token_id) for _ in range(max_seq_len)] for _ in range(num_samples)]
attention_mask = num_samples * [max_seq_len * [1]]
ds = Dataset.from_dict({"input_ids": input_ids, "attention_mask": attention_mask})
recipe = GPTQModifier(
targets="Linear",
scheme="W8A8",
ignore=["lm_head"],
dampening_frac=0.01,
)
model = SparseAutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
trust_remote_code=True,
)
oneshot(
model=model,
dataset=ds,
recipe=recipe,
max_seq_length=max_seq_len,
num_calibration_samples=num_samples,
)
model.save_pretrained("starcoder2-7b-quantized.w8a8")
```
## Evaluation
The model was evaluated on the [HumanEval](https://arxiv.org/abs/2107.03374) and [HumanEval+](https://arxiv.org/abs/2305.01210) benchmarks, using the generation configuration from [Big Code Models Leaderboard](https://huggingface.co/spaces/bigcode/bigcode-models-leaderboard).
We used Neural Magic's fork of [evalplus](https://github.com/neuralmagic/evalplus) and the [vLLM](https://docs.vllm.ai/en/stable/) engine, using the following commands:
```
python codegen/generate.py \
--model neuralmagic/starcoder2-7b-quantized.w8a8 \
--bs 16 \
--temperature 0.2 \
--n_samples 50 \
--dataset humaneval \
-- root "."
python3 evalplus/sanitize.py humaneval/neuralmagic--starcoder2-7b-quantized.w8a8_vllm_temp_0.2
evalplus.evaluate --dataset humaneval --samples humaneval/neuralmagic--starcoder2-7b-quantized.w8a8_vllm_temp_0.2-sanitized
```
### Accuracy
<table>
<tr>
<td><strong>Benchmark</strong>
</td>
<td><strong>starcoder2-7b</strong>
</td>
<td><strong>starcoder2-7b-quantized.w8a8 (this model)</strong>
</td>
<td><strong>Recovery</strong>
</td>
</tr>
<tr>
<td>HumanEval pass@1
</td>
<td>34.9
</td>
<td>33.9
</td>
<td>97.1%
</td>
</tr>
<tr>
<td>HumanEval pass@10
</td>
<td>50.7
</td>
<td>50.9
</td>
<td>100.4%
</td>
</tr>
<tr>
<td>HumanEval+ pass@1
</td>
<td>30.0
</td>
<td>29.3
</td>
<td>97.7%
</td>
</tr>
<tr>
<td>HumanEval+ pass@10
</td>
<td>43.0
</td>
<td>43.6
</td>
<td>101.4%
</td>
</tr>
<tr>
</table>

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"quant_method": "compressed-tensors",
"quantization_status": "compressed",
"sparsity_config": {}
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8
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64
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98410
tokenizer.json Normal file
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357
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"clean_up_tokenization_spaces": true,
"eos_token": "<|endoftext|>",
"model_max_length": 1000000000000000019884624838656,
"pad_token": "<|endoftext|>",
"tokenizer_class": "GPT2Tokenizer",
"unk_token": "<|endoftext|>",
"vocab_size": 49152
}

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