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Model: neuralmagic/starcoder2-3b-quantized.w8a8 Source: Original Platform
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README.md
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---
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pipeline_tag: text-generation
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datasets:
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- bigcode/the-stack-v2-train
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license: bigcode-openrail-m
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library_name: transformers
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tags:
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- code
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model-index:
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- name: starcoder2-3b-quantized.w8a8
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results:
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- task:
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type: text-generation
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dataset:
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name: HumanEval+
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type: humanevalplus
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metrics:
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- type: pass@1
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value: 26.8
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- task:
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type: text-generation
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dataset:
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name: HumanEval
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type: humaneval
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metrics:
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- type: pass@1
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value: 31.4
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---
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# starcoder2-3b-quantized.w8a8
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## Model Overview
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- **Model Architecture:** StarCoder2
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- **Input:** Text
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- **Output:** Text
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- **Model Optimizations:**
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- **Activation quantization:** INT8
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- **Weight quantization:** INT8
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- **Intended Use Cases:** Intended for commercial and research use. Similarly to [starcoder2-3b](https://huggingface.co/bigcode/starcoder2-3b), 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.
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- **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws).
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- **Release Date:** 8/1/2024
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- **Version:** 1.0
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- **License(s):** bigcode-openrail-m
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- **Model Developers:** Neural Magic
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Quantized version of [starcoder2-3b](https://huggingface.co/bigcode/starcoder2-3b).
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It achieves a HumanEval pass@1 of 31.4, whereas the unquantized model achieves 30.7 when evaluated under the same conditions.
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### Model Optimizations
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This model was obtained by quantizing the weights of [starcoder2-3b](https://huggingface.co/bigcode/starcoder2-3b) to INT8 data type.
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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).
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Weight quantization also reduces disk size requirements by approximately 50%.
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Only weights and activations of the linear operators within transformers blocks are quantized.
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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.
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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.
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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.
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GPTQ used a 1% damping factor and 256 sequences of 8,192 random tokens.
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## Deployment
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### Use with vLLM
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This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
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```python
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from vllm import LLM, SamplingParams
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from transformers import AutoTokenizer
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model_id = "neuralmagic/starcoder2-3b-quantized.w8a8"
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number_gpus = 1
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sampling_params = SamplingParams(temperature=0.2, top_p=0.95, max_tokens=256)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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prompts = ["def print_hello_world():"]
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llm = LLM(model=model_id, tensor_parallel_size=number_gpus)
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outputs = llm.generate(prompts, sampling_params)
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generated_text = outputs[0].outputs[0].text
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print(generated_text)
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```
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vLLM aslo supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
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## Creation
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This model was created by using the [llm-compressor](https://github.com/vllm-project/llm-compressor) library as presented in the code snipet below.
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```python
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from transformers import AutoTokenizer
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from datasets import Dataset
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from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot
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from llmcompressor.modifiers.quantization import GPTQModifier
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import random
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model_id = "bigcode/starcoder2-3b"
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num_samples = 256
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max_seq_len = 8192
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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max_token_id = len(tokenizer.get_vocab()) - 1
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input_ids = [[random.randint(0, max_token_id) for _ in range(max_seq_len)] for _ in range(num_samples)]
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attention_mask = num_samples * [max_seq_len * [1]]
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ds = Dataset.from_dict({"input_ids": input_ids, "attention_mask": attention_mask})
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recipe = GPTQModifier(
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targets="Linear",
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scheme="W8A8",
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ignore=["lm_head"],
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dampening_frac=0.01,
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)
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model = SparseAutoModelForCausalLM.from_pretrained(
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model_id,
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device_map="auto",
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trust_remote_code=True,
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)
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oneshot(
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model=model,
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dataset=ds,
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recipe=recipe,
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max_seq_length=max_seq_len,
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num_calibration_samples=num_samples,
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)
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model.save_pretrained("starcoder2-3b-quantized.w8a8")
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```
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## Evaluation
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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).
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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:
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```
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python codegen/generate.py \
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--model neuralmagic/starcoder2-3b-quantized.w8a8 \
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--bs 16 \
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--temperature 0.2 \
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--n_samples 50 \
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--dataset humaneval \
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-- root "."
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python3 evalplus/sanitize.py humaneval/neuralmagic--starcoder2-3b-quantized.w8a8_vllm_temp_0.2
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evalplus.evaluate --dataset humaneval --samples humaneval/neuralmagic--starcoder2-3b-quantized.w8a8_vllm_temp_0.2-sanitized
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```
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### Accuracy
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<table>
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<tr>
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<td><strong>Benchmark</strong>
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</td>
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<td><strong>starcoder2-3b</strong>
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</td>
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<td><strong>starcoder2-3b-quantized.w8a8 (this model)</strong>
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</td>
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<td><strong>Recovery</strong>
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</td>
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</tr>
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<tr>
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<td>HumanEval pass@1
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</td>
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<td>30.7
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</td>
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<td>31.4
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</td>
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<td>102.3%
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</td>
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</tr>
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<tr>
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<td>HumanEval pass@10
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</td>
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<td>44.9
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</td>
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<td>44.7
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</td>
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<td>99.6%
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</td>
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</tr>
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<tr>
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<td>HumanEval+ pass@1
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</td>
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<td>26.6
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</td>
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<td>26.8
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</td>
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<td>100.8%
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</td>
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</tr>
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<tr>
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<td>HumanEval+ pass@10
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</td>
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<td>39.2
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</td>
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<td>38.7
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</td>
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<td>98.7%
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</td>
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</tr>
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<tr>
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</table>
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config.json
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{
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"_name_or_path": "/root/.cache/huggingface/hub/models--bigcode--starcoder2-3b/snapshots/733247c55e3f73af49ce8e9c7949bf14af205928",
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"architectures": [
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"Starcoder2ForCausalLM"
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],
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"attention_dropout": 0.1,
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"bos_token_id": 0,
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"embedding_dropout": 0.1,
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"eos_token_id": 0,
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"hidden_act": "gelu_pytorch_tanh",
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"hidden_size": 3072,
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"initializer_range": 0.018042,
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"intermediate_size": 12288,
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"max_position_embeddings": 16384,
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"mlp_type": "default",
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"model_type": "starcoder2",
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"norm_epsilon": 1e-05,
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"norm_type": "layer_norm",
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"num_attention_heads": 24,
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"num_hidden_layers": 30,
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"num_key_value_heads": 2,
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"residual_dropout": 0.1,
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"rope_theta": 999999.4420358813,
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"sliding_window": 4096,
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"torch_dtype": "float32",
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"transformers_version": "4.43.3",
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"use_bias": true,
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"use_cache": true,
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"vocab_size": 49152,
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"quantization_config": {
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"config_groups": {
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"group_0": {
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"input_activations": {
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"block_structure": null,
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"dynamic": true,
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"group_size": null,
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"num_bits": 8,
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"observer": "memoryless",
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"observer_kwargs": {},
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"strategy": "token",
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"symmetric": true,
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"type": "int"
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},
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"output_activations": null,
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"targets": [
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"Linear"
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],
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"weights": {
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"block_structure": null,
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"dynamic": false,
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"group_size": null,
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"num_bits": 8,
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"observer": "minmax",
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"observer_kwargs": {},
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"strategy": "channel",
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"symmetric": true,
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"type": "int"
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}
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}
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},
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"format": "int-quantized",
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"global_compression_ratio": 1.3879017053810396,
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"ignore": [
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"lm_head"
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],
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"kv_cache_scheme": null,
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"quant_method": "compressed-tensors",
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"quantization_status": "frozen",
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"sparsity_config": {
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"format": "dense",
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"global_sparsity": 1.333353131567116,
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"registry_requires_subclass": false,
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"sparsity_structure": "unstructured"
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}
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}
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}
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configuration.json
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 0,
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"eos_token_id": 0,
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"transformers_version": "4.43.3"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:32d9b0f950dfbafcf0e0a5b30958b07f99b7e958dcf9f3f09c5a2947dc73d816
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size 4093158536
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recipe.yaml
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quant_stage:
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quant_modifiers:
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GPTQModifier:
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sequential_update: false
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dampening_frac: 0.01
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ignore: [lm_head]
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scheme: W8A8
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targets: Linear
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special_tokens_map.json
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{
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"additional_special_tokens": [
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"<|endoftext|>",
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"<fim_prefix>",
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||||||
|
"<fim_middle>",
|
||||||
|
"<fim_suffix>",
|
||||||
|
"<fim_pad>",
|
||||||
|
"<repo_name>",
|
||||||
|
"<file_sep>",
|
||||||
|
"<issue_start>",
|
||||||
|
"<issue_comment>",
|
||||||
|
"<issue_closed>",
|
||||||
|
"<jupyter_start>",
|
||||||
|
"<jupyter_text>",
|
||||||
|
"<jupyter_code>",
|
||||||
|
"<jupyter_output>",
|
||||||
|
"<jupyter_script>",
|
||||||
|
"<empty_output>",
|
||||||
|
"<code_to_intermediate>",
|
||||||
|
"<intermediate_to_code>",
|
||||||
|
"<pr>",
|
||||||
|
"<pr_status>",
|
||||||
|
"<pr_is_merged>",
|
||||||
|
"<pr_base>",
|
||||||
|
"<pr_file>",
|
||||||
|
"<pr_base_code>",
|
||||||
|
"<pr_diff>",
|
||||||
|
"<pr_diff_hunk>",
|
||||||
|
"<pr_comment>",
|
||||||
|
"<pr_event_id>",
|
||||||
|
"<pr_review>",
|
||||||
|
"<pr_review_state>",
|
||||||
|
"<pr_review_comment>",
|
||||||
|
"<pr_in_reply_to_review_id>",
|
||||||
|
"<pr_in_reply_to_comment_id>",
|
||||||
|
"<pr_diff_hunk_comment_line>",
|
||||||
|
"<NAME>",
|
||||||
|
"<EMAIL>",
|
||||||
|
"<KEY>",
|
||||||
|
"<PASSWORD>"
|
||||||
|
],
|
||||||
|
"bos_token": {
|
||||||
|
"content": "<|endoftext|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"eos_token": {
|
||||||
|
"content": "<|endoftext|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"pad_token": "<|endoftext|>",
|
||||||
|
"unk_token": {
|
||||||
|
"content": "<|endoftext|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
}
|
||||||
|
}
|
||||||
98410
tokenizer.json
Normal file
98410
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
357
tokenizer_config.json
Normal file
357
tokenizer_config.json
Normal file
@@ -0,0 +1,357 @@
|
|||||||
|
{
|
||||||
|
"add_prefix_space": false,
|
||||||
|
"added_tokens_decoder": {
|
||||||
|
"0": {
|
||||||
|
"content": "<|endoftext|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"1": {
|
||||||
|
"content": "<fim_prefix>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"2": {
|
||||||
|
"content": "<fim_middle>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"3": {
|
||||||
|
"content": "<fim_suffix>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"4": {
|
||||||
|
"content": "<fim_pad>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"5": {
|
||||||
|
"content": "<repo_name>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"6": {
|
||||||
|
"content": "<file_sep>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"7": {
|
||||||
|
"content": "<issue_start>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"8": {
|
||||||
|
"content": "<issue_comment>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"9": {
|
||||||
|
"content": "<issue_closed>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"10": {
|
||||||
|
"content": "<jupyter_start>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"11": {
|
||||||
|
"content": "<jupyter_text>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"12": {
|
||||||
|
"content": "<jupyter_code>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"13": {
|
||||||
|
"content": "<jupyter_output>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"14": {
|
||||||
|
"content": "<jupyter_script>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"15": {
|
||||||
|
"content": "<empty_output>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"16": {
|
||||||
|
"content": "<code_to_intermediate>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"17": {
|
||||||
|
"content": "<intermediate_to_code>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"18": {
|
||||||
|
"content": "<pr>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"19": {
|
||||||
|
"content": "<pr_status>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"20": {
|
||||||
|
"content": "<pr_is_merged>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"21": {
|
||||||
|
"content": "<pr_base>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"22": {
|
||||||
|
"content": "<pr_file>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"23": {
|
||||||
|
"content": "<pr_base_code>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"24": {
|
||||||
|
"content": "<pr_diff>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"25": {
|
||||||
|
"content": "<pr_diff_hunk>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"26": {
|
||||||
|
"content": "<pr_comment>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"27": {
|
||||||
|
"content": "<pr_event_id>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"28": {
|
||||||
|
"content": "<pr_review>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"29": {
|
||||||
|
"content": "<pr_review_state>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"30": {
|
||||||
|
"content": "<pr_review_comment>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"31": {
|
||||||
|
"content": "<pr_in_reply_to_review_id>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"32": {
|
||||||
|
"content": "<pr_in_reply_to_comment_id>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"33": {
|
||||||
|
"content": "<pr_diff_hunk_comment_line>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"34": {
|
||||||
|
"content": "<NAME>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"35": {
|
||||||
|
"content": "<EMAIL>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"36": {
|
||||||
|
"content": "<KEY>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"37": {
|
||||||
|
"content": "<PASSWORD>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"additional_special_tokens": [
|
||||||
|
"<|endoftext|>",
|
||||||
|
"<fim_prefix>",
|
||||||
|
"<fim_middle>",
|
||||||
|
"<fim_suffix>",
|
||||||
|
"<fim_pad>",
|
||||||
|
"<repo_name>",
|
||||||
|
"<file_sep>",
|
||||||
|
"<issue_start>",
|
||||||
|
"<issue_comment>",
|
||||||
|
"<issue_closed>",
|
||||||
|
"<jupyter_start>",
|
||||||
|
"<jupyter_text>",
|
||||||
|
"<jupyter_code>",
|
||||||
|
"<jupyter_output>",
|
||||||
|
"<jupyter_script>",
|
||||||
|
"<empty_output>",
|
||||||
|
"<code_to_intermediate>",
|
||||||
|
"<intermediate_to_code>",
|
||||||
|
"<pr>",
|
||||||
|
"<pr_status>",
|
||||||
|
"<pr_is_merged>",
|
||||||
|
"<pr_base>",
|
||||||
|
"<pr_file>",
|
||||||
|
"<pr_base_code>",
|
||||||
|
"<pr_diff>",
|
||||||
|
"<pr_diff_hunk>",
|
||||||
|
"<pr_comment>",
|
||||||
|
"<pr_event_id>",
|
||||||
|
"<pr_review>",
|
||||||
|
"<pr_review_state>",
|
||||||
|
"<pr_review_comment>",
|
||||||
|
"<pr_in_reply_to_review_id>",
|
||||||
|
"<pr_in_reply_to_comment_id>",
|
||||||
|
"<pr_diff_hunk_comment_line>",
|
||||||
|
"<NAME>",
|
||||||
|
"<EMAIL>",
|
||||||
|
"<KEY>",
|
||||||
|
"<PASSWORD>"
|
||||||
|
],
|
||||||
|
"bos_token": "<|endoftext|>",
|
||||||
|
"clean_up_tokenization_spaces": true,
|
||||||
|
"eos_token": "<|endoftext|>",
|
||||||
|
"model_max_length": 1000000000000000019884624838656,
|
||||||
|
"pad_token": "<|endoftext|>",
|
||||||
|
"tokenizer_class": "GPT2Tokenizer",
|
||||||
|
"unk_token": "<|endoftext|>",
|
||||||
|
"vocab_size": 49152
|
||||||
|
}
|
||||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user