TIGER-Lab/TIGERScore-13B
1
0
Fork 0
Code Issues Pull Requests Actions Projects Releases Wiki Activity

初始化项目，由ModelHub XC社区提供模型

Browse Source 
Model: TIGER-Lab/TIGERScore-13B
Source: Original Platform
This commit is contained in:
ModelHub XC
2026-04-10 23:34:13 +08:00
commit c842cdba24
18 changed files with 2881 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

35
.gitattributes vendored Normal file
View File

@@ -0,0 +1,35 @@
*.7z filter=lfs diff=lfs merge=lfs -text
*.arrow filter=lfs diff=lfs merge=lfs -text
*.bin filter=lfs diff=lfs merge=lfs -text
*.bz2 filter=lfs diff=lfs merge=lfs -text
*.ckpt filter=lfs diff=lfs merge=lfs -text
*.ftz filter=lfs diff=lfs merge=lfs -text
*.gz filter=lfs diff=lfs merge=lfs -text
*.h5 filter=lfs diff=lfs merge=lfs -text
*.joblib filter=lfs diff=lfs merge=lfs -text
*.lfs.* filter=lfs diff=lfs merge=lfs -text
*.mlmodel filter=lfs diff=lfs merge=lfs -text
*.model filter=lfs diff=lfs merge=lfs -text
*.msgpack filter=lfs diff=lfs merge=lfs -text
*.npy filter=lfs diff=lfs merge=lfs -text
*.npz filter=lfs diff=lfs merge=lfs -text
*.onnx filter=lfs diff=lfs merge=lfs -text
*.ot filter=lfs diff=lfs merge=lfs -text
*.parquet filter=lfs diff=lfs merge=lfs -text
*.pb filter=lfs diff=lfs merge=lfs -text
*.pickle filter=lfs diff=lfs merge=lfs -text
*.pkl filter=lfs diff=lfs merge=lfs -text
*.pt filter=lfs diff=lfs merge=lfs -text
*.pth filter=lfs diff=lfs merge=lfs -text
*.rar filter=lfs diff=lfs merge=lfs -text
*.safetensors filter=lfs diff=lfs merge=lfs -text
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
*.tar.* filter=lfs diff=lfs merge=lfs -text
*.tar filter=lfs diff=lfs merge=lfs -text
*.tflite filter=lfs diff=lfs merge=lfs -text
*.tgz filter=lfs diff=lfs merge=lfs -text
*.wasm filter=lfs diff=lfs merge=lfs -text
*.xz filter=lfs diff=lfs merge=lfs -text
*.zip filter=lfs diff=lfs merge=lfs -text
*.zst filter=lfs diff=lfs merge=lfs -text
*tfevents* filter=lfs diff=lfs merge=lfs -text

320
README.md Normal file
View File

@@ -0,0 +1,320 @@
---
language:
- en
- zh
- ru
- cs
license: mit
tags:
- text evaluation
- metric
- llm metric
- llama
- tigerscore
datasets:
- TIGER-Lab/MetricInstruct
metrics:
- pearsonr
- spearmanr
pipeline_tag: text2text-generation
model-index:
- name: TIGERScore-13B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 59.04
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TIGER-Lab/TIGERScore-13B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 82.79
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TIGER-Lab/TIGERScore-13B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 55.07
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TIGER-Lab/TIGERScore-13B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 40.38
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TIGER-Lab/TIGERScore-13B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 74.74
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TIGER-Lab/TIGERScore-13B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 28.73
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TIGER-Lab/TIGERScore-13B
name: Open LLM Leaderboard
---
## TIGERScore
[Project Page](https://tiger-ai-lab.github.io/TIGERScore/) | [Paper](https://arxiv.org/abs/2310.00752) | [Code](https://github.com/TIGER-AI-Lab/TIGERScore) | [🤗Demo](https://huggingface.co/spaces/TIGER-Lab/TIGERScore) |
[🤗TIGERScore-7B](https://huggingface.co/TIGER-Lab/TIGERScore-7B-V1.2) | [🤗TIGERScore-13B](https://huggingface.co/TIGER-Lab/TIGERScore-13B-V1.2)
## Introduction
We present TIGERScore, a **T**rained metric that follows **I**nstruction **G**uidance to perform **E**xplainable, and **R**eference-free evaluation over a wide spectrum of text generation tasks. Our metric is based on LLaMA-2, trained on our meticulously curated instruction-tuning dataset [MetricInstruct](https://huggingface.co/datasets/TIGER-Lab/MetricInstruct) which covers 6 text generation tasks and 23 text generation datasets.
Existing automatic metrics are lagging and suffer from issues like 1) **Dependency on references**, 2) **Limited to specific domains**, 3) **Lack of attribution**. Contrary to them, TIGERScore is designed to be driven by natural language instruction and provide detailed error analysis to pinpoint the mistakes in the generated text.
Specifically, TIGERScore takes an instruction, an associated input context along with a hypothesis output that might contain errors. Then, TIGERScore will evaluate this hypothesis output and list several errors, each consisting of the error location, aspect, explanation and penalty scores (score reduced, starting from 0). The sum of the reduced scores is taken as the overall rating of this output.
As a reference-free metric, its correlation can even surpass the best existing reference-based metrics. We believe TIGERScore demonstrates the possibility of building universal explainable metrics to evaluate any text generation task.
## Training Data
The models are trained on the 🤗 [MetricInstruct Dataset](https://huggingface.co/datasets/TIGER-Lab/MetricInstruct), which covers 6 text generation tasks and 22 text generation datasets. Check out the dataset card for more details.
## Training Procedure
The models are fine-tuned with the MetricInstruct dataset using the original Llama-2 model as base models. The training procedure varies for different models based on their sizes. Check out our paper for more details.
## Evaluation
Experiments show that TIGERScore surpasses existing baseline metrics in correlation with human ratings on all 6 held-in tasks and 1 held-out task, achiving the highest overall performance. We hope the emergence of TIGERScore can promote the research in the LLM community as a powerful, interpretable, and easy-to-use metric.
### Kendall Results
| Tasks⟶ | Summarization | Translation | Data2Text | Long-form QA | MathQA | Instruction Following | Story-Gen | Average |
|----------------------------------------|-----------|-----------|-----------------|-----------|-----------|-----------|-----------|-----------|
| | | | GPT-based | Metrics | | | | |
| GPT-3.5-turbo (few-shot) | **30.45** | 32.3 | 30.38 | 20.91 | **58.57** | 17.73 | 3.26 | 27.65 |
| GPT-4 (zero-shot) | 29.32 | **35.38** | **32.26** | **35.85** | 46.63 | **49.5** | **25.69** | **36.38** |
| | | | Reference-based | Metrics | | | | |
| BLEU | 8.71 | 14.5 | 23.13 | 7.73 | 17.25 | 35.92 | -0.89 | 15.19 |
| ROUGE-2f | 10.67 | 13.19 | 24.74 | 11.73 | 18.07 | 34.59 | 1.78 | 16.4 |
| InstructScore | 20.86 | 40.44 | 30.21 | 15.64 | -3.87 | 13.87 | 13.5 | 18.66 |
| GPTScore-ref | 10.8 | 18.74 | 27.47 | 22.13 | 14.86 | 25.4 | 12.78 | 18.88 |
| BARTScore-cnn (hypo-ref) | 10 | 21.06 | 27.04 | 20.67 | **19.07** | 24.7 | 18.58 | 20.16 |
| BARTScore-para (hypo-ref) | 10.41 | 24.9 | 28.42 | 20.24 | 14.1 | 26.13 | 12.11 | 19.47 |
| BERTScore | 17.39 | 31.57 | 30.74 | 17.7 | 9.41 | 35.61 | 2 | 20.63 |
| BLEURT | 12.69 | 36.12 | **34.48** | 23.11 | 2.88 | 27.94 | 19.18 | 22.34 |
| UniEval (summ) | **35.89** | 16.08 | 28.56 | **29.32** | 16.15 | 11.93 | **31.22** | 24.17 |
| COMET-22 | 25.01 | **42.79** | 23.43 | 24.66 | -4.52 | **36.17** | 27.52 | **25.01** |
| | | | Reference-free |Metrics | | | | |
| BARTScore-para (src-hypo) | 29.12 | 7.01 | 22.32 | 18.8 | -2.21 | 4.26 | 14.15 | 13.35 |
| BARTScore-cnn (src-hypo) | 26.63 | 9.4 | 23.69 | 28.93 | 1.23 | 19.09 | 23.29 | 18.89 |
| Llama-2-13b-chat-0-shot | 25.22 | 11.79 | 23.45 | 15.96 | 1.08 | 19.5 | 21.52 | 16.93 |
| COMETKiwi | 11.87 | 36.37 | 19.08 | 12.23 | -9.38 | 26.46 | 12.78 | 15.63 |
| GPTScore-src | 28.2 | 6.5 | 19.81 | 27.64 | 11.64 | 20.04 | 16.36 | 18.6 |
| TigerScore-7B | 28.79 | 33.65 | 32.44 | 33.93 | 19.98 | 38.13 | 29.72 | 30.95 |
| TigerScore-13B | **31.29** | **36.5** | **36.43** | **33.17** | **21.58** | **41.84** | **35.33** | **33.73** |
| ∆ (ours - best reference-free) | +2 | +0 | +13 | +4 | +10 | +15 | +14 | +15 |
| ∆ (ours - best reference-based) | -4 | -6 | +2 | +4 | +2 | +5 | +4 | +8 |
### Pearson Results
| Tasks⟶ | Summarization | Translation | Data2Text | Long-form QA | MathQA | Instruction Following | Story-Gen | Average |
|-------------------------------|-----------|-----------|-----------------|-----------|-----------|-----------|-----------|-----------|
| | | | GPT-based | Metrics | | | | |
| GPT-3.5-turbo (few-shot) | **45.53** | **43.77** | **47.76** | 29.84 | **61.26** | 15.36 | 7.8 | 35.9 |
| GPT-4 (zero-shot) | 40.75 | 33.92 | 46.83 | **49.3** | 54.98 | **60.45** | **37.74** | **46.28** |
| | | | Reference-based | Metrics | | | | |
| BLEU | 11.66 | 17.47 | 34.29 | 18.21 | 18.12 | 29.47 | -0.64 | 18.37 |
| ROUGE-2f | 16.03 | 16.26 | 35.85 | 19.66 | 20.69 | 33.49 | 2.88 | 20.69 |
| InstructScore | 27.4 | 51.55 | 47.28 | 20.59 | 0.36 | 20.98 | 12.81 | 25.85 |
| GPTScore-ref | 13.47 | 21.05 | 48.7 | 33.4 | 18.22 | 29.66 | 18.94 | 26.2 |
| BARTScore-cnn (hypo-ref) | 16.67 | 23.56 | 45.08 | 32.78 | **23.09** | 26.57 | 27.61 | 27.91 |
| BARTScore-para (hypo-ref) | 19.73 | 29.04 | 47.89 | 32.7 | 17.33 | 30.2 | 17.76 | 27.81 |
| BERTScore | 26.26 | 37.65 | 48.22 | 26.39 | 11.19 | 45.58 | 4.08 | 28.48 |
| BLEURT | 17.27 | 43 | **54.32** | 34.26 | 3.98 | 39.15 | 27.89 | 31.41 |
| UniEval (summ) | **53.22** | 23.11 | 51.14 | **36.95** | 17.69 | 30.87 | **44.88** | 36.84 |
| COMET-22 | 35.32 | **58.46** | 43.82 | 36.79 | -5.58 | **49.68** | 40.12 | **36.94** |
| | | | Reference-free | Metrics | | | | |
| BARTScore-para (src-hypo) | 43.11 | 6.96 | 37.82 | 29.86 | -0.41 | 19.37 | 19.99 | 22.38 |
| BARTScore-cnn (src-hypo) | 39.72 | 9.53 | 45.43 | 41.48 | 3.28 | 34.97 | 33.51 | 29.7 |
| Llama-2-13b-chat-0-shot | 29.59 | 9.09 | 41.32 | 21.67 | 2.8 | 22.71 | 21.13 | 21.19 |
| COMETKiwi | 14.22 | **50.91** | 23.63 | 22.59 | -13.35 | 34.46 | 19.12 | 21.65 |
| GPTScore-src | 41.71 | 6.82 | 41.19 | 39.79 | 13.99 | 27.59 | 23.22 | 27.76 |
| TigerScore-7B | 43.95 | 37.7 | 49.13 | **46.1** | 21.77 | 38.26 | 39.9 | 39.54 |
| TigerScore-13B | **44.21** | 41.54 | **52.87** | 44.76 | **24.41** | **47.52** | **47.66** | **43.28** |
| ∆ (ours - best reference-free) | +1 | -9 | +7 | +5 | +10 | +20 | +14 | +13 |
| ∆ (ours - best reference-based) | -9 | -17 | -2 | +9 | +1 | -2 | +3 | +6 |
### Spearman Results
| Tasks⟶ | Summarization | Translation | Data2Text | Long-form QA | MathQA | Instruction Following | Story-Gen | Average |
|-------------------------------------------|----------------|----------------|----------------|-----------------|----------------|----------------|----------------|----------------|
| | | | GPT-based | Metrics | | | | |
| GPT-3.5-turbo (few-shot) | **38.50** | 40.53 | 40.20 | 29.33 | **66.46** | 23.20 | 4.77 | 34.71 |
| GPT-4 (zero-shot) | 36.46 | **43.87** | **44.04** | **48.95** | 51.71 | **58.53** | **32.48** | **45.15** |
| | | | Reference-based | Metrics | | | | |
| BLEU | 11.98 | 19.73 | 33.29 | 11.38 | 21.12 | **46.61** | -1.17 | 20.42 |
| ROUGE-2f | 14.53 | 17.83 | 35.49 | 16.83 | 22.12 | 44.56 | 2.34 | 21.96 |
| InstructScore | 26.33 | 47.30 | 43.93 | 21.62 | -4.15 | 16.19 | 16.13 | 23.91 |
| GPTScore-ref | 14.73 | 24.95 | 39.42 | 31.60 | 18.20 | 33.14 | 18.24 | 25.75 |
| BARTScore-cnn(hypo-ref) | 13.64 | 28.53 | 36.12 | 29.57 | **23.35** | 32.49 | 26.64 | 27.19 |
| BARTScore-para (hypo-ref) | 17.18 | 33.72 | 40.79 | 28.94 | 17.27 | 34.47 | 17.43 | 27.11 |
| BERTScore | 23.67 | 42.41 | 43.75 | 25.60 | 11.53 | 45.77 | 2.88 | 27.95 |
| BLEURT | 17.30 | 48.41 | **48.76** | 33.26 | 3.53 | 36.46 | 27.52 | 30.75 |
| UniEval(summ) | **47.52** | 21.90 | 38.38 | **41.83** | 19.78 | 16.02 | **44.46** | 32.84 |
| COMET-22 | 33.75 | **56.35** | 33.92 | 35.28 | -5.53 | 46.13 | 39.20 | **34.16** |
| | | | Reference-free | Metrics | | | | |
| BARTScore-para (src-hypo) | **38.68** | 9.60 | 32.26 | 26.86 | -2.70 | 5.92 | 20.55 | 18.74 |
| BARTScore-cnn (src-hypo) | 35.50 | 12.83 | 34.33 | 40.96 | 1.50 | 25.43 | 33.48 | 26.29 |
| Llama-2-13b-chat-0-shot | 28.53 | 14.38 | 29.24 | 19.91 | 1.08 | 21.37 | 26.78 | 20.18 |
| COMETKiwi | 16.27 | **48.48** | 27.90 | 18.05 | -11.48 | 34.86 | 18.47 | 21.79 |
| GPTScore-src | 37.41 | 8.90 | 28.82 | 39.48 | 14.25 | 26.46 | 23.91 | 25.61 |
| TIGERScore-7B (ours) | 35.11 | 41.50 | 42.39 | **47.11** | 21.23 | 43.57 | 39.26 | 38.60 |
| TIGERScore-13B (ours) | 36.81 | 44.99 | **45.88** | 46.22 | **23.32** | **47.03** | **46.36** | **41.52** |
| Δ (ours - best reference-free) | -2 | -3 | +12 | +5 | +9 | +14 | +13 | +16 |
| ∆ (ours - best reference-based) | -9 | -11 | -3 | +5 | -0 | +0 | +2 | +7 |
## Usage
TIGERScore can be easily loaded in 2 lines of codes, and provides a friendly scoring interface function.
To use TIGERScore, first install `tigerscore` with
```bash
pip install git+https://github.com/TIGER-AI-Lab/TIGERScore.git
```
Then load the tigerscore model variates according to you needs.
```python
# set up scorer
from tigerscore import TIGERScorer
scorer = TIGERScorer(model_name="TIGER-Lab/TIGERScore-13B") # on GPU
# scorer = TIGERScorer(model_name="TIGER-Lab/TIGERScore-13B", quantized=True) # 4 bit quantization on GPU
# scorer = TIGERScorer(model_name="TIGER-Lab/TIGERScore-13B", use_vllm=True) # VLLM on GPU, Recommended for faster evaluation (0.2s per instance)
# scorer = TIGERScorer(model_name="TIGER-Lab/TIGERScore-13B-GGUF", use_llamacpp=True) # 4 bit quantization on CPU
```
After loading, you can easily get errors of the provided **hypothesis output** given the **instruction** and **input context**
```python
# example
instruction = "Write an apology letter."
input_context = "Reason: You canceled a plan at the last minute due to illness."
hypo_output = "Hey [Recipient],\n\nI'm really sorry for ditching our plan. I suddenly got an opportunity for a vacation so I took it. I know this might have messed up your plans and I regret that.\n\nDespite being under the weather, I would rather go for an adventure. I hope you can understand my perspective and I hope this incident doesn't change anything between us.\n\nWe can reschedule our plan for another time. Sorry again for the trouble.\n\nPeace out,\n[Your Name]\n\n---"
results = scorer.score([instruction], [hypo_output], [input_context])
print(results)
```
Results are a list of errors with detailed explanations and reasonable penalty scores:
```json
[
{
"num_errors": 2,
"score": -7.0,
"errors": {
"error_0": {
"location": " \"I suddenly got an opportunity for a vacation so I took it.\"",
"aspect": " Misunderstanding context",
"explanation": " The error lies in the context of the reason for cancelling the plan. The original reason was due to illness, but in the incorrect output, it is stated that the cancellation was due to a vacation opportunity, which is a misunderstanding of the context. The correction would be to stick to the original reason for cancelling.",
"severity": "Major",
"score_reduction": "5.0"
},
"error_1": {
"location": " \"I hope you can understand my perspective and I hope this incident doesn't change anything between us.\"",
"aspect": " Inappropriate tone",
"explanation": " The tone of this sentence is too casual and lacks regret or apology. It's important to maintain a formal and regretful tone in an apology letter. The sentence could be corrected to something like \"I hope you can find it in your heart to forgive me and let this incident not strain our relationship.\"",
"severity": "Minor",
"score_reduction": "2.0"
}
},
"raw_output": " The model-generated output contains 2 errors, with a total score reduction of 7.0.\nError location 1: ..."
}
]
```
Check more usage at our [Github Usage Doc](https://github.com/TIGER-AI-Lab/TIGERScore#usage). Have Fun!
## Citation
If you find our work useful, please cite our paper:
```
@article{jiang2023TIGERScore,
title={TIGERScore: Towards Building Explainable Metric for All Text Generation Tasks},
author={Dongfu Jiang, Yishan Li, Ge Zhang, Wenhao Huang, Bill Yuchen Lin, Wenhu Chen},
journal={arXiv preprint arXiv:2310.00752},
year={2023}
}
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_TIGER-Lab__TIGERScore-13B)
| Metric |Value|
|---------------------------------|----:|
|Avg. |56.79|
|AI2 Reasoning Challenge (25-Shot)|59.04|
|HellaSwag (10-Shot) |82.79|
|MMLU (5-Shot) |55.07|
|TruthfulQA (0-shot) |40.38|
|Winogrande (5-shot) |74.74|
|GSM8k (5-shot) |28.73|

3
added_tokens.json Normal file
View File

@@ -0,0 +1,3 @@
{
"[PAD]": 32000
}

27
config.json Normal file
View File

@@ -0,0 +1,27 @@
{
"_name_or_path": "/ML-A100/models/Llama-2-13b-hf",
"architectures": [
"LlamaForCausalLM"
],
"attention_bias": false,
"bos_token_id": 1,
"eos_token_id": 2,
"hidden_act": "silu",
"hidden_size": 5120,
"initializer_range": 0.02,
"intermediate_size": 13824,
"max_position_embeddings": 4096,
"model_type": "llama",
"num_attention_heads": 40,
"num_hidden_layers": 40,
"num_key_value_heads": 40,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_scaling": null,
"rope_theta": 10000.0,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.35.1",
"use_cache": true,
"vocab_size": 32001
}

1
configuration.json Normal file
View File

@@ -0,0 +1 @@
{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

10
generation_config.json Normal file
View File

@@ -0,0 +1,10 @@
{
"bos_token_id": 1,
"do_sample": true,
"eos_token_id": 2,
"max_length": 4096,
"pad_token_id": 0,
"temperature": 0.6,
"top_p": 0.9,
"transformers_version": "4.35.1"
}

3
model-00001-of-00006.safetensors Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:a6d8b53803d945c25ee4c102fe01fe5a0c8d3d4dedbed67678cb0db00164c5b5
size 4978276040

3
model-00002-of-00006.safetensors Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:6c091def96de2022fe77bdc713df664018ca6a586b01e5506a33d9953a45b39f
size 4970422232

3
model-00003-of-00006.safetensors Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:8eb03620e493ab9e819b898eff12aef0a128b26982b0c4d557ad528df518e7ea
size 4970422256

3
model-00004-of-00006.safetensors Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:c385a8cf536eb88603598a5b1602e31323fe53ed2abb4c720e50ca5129b6bf9f
size 4933701504

3
model-00005-of-00006.safetensors Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:b6a524eeee2916df42aa936889bd7034c6f75fb1ae8b8d9ab85d9a4117a69b56
size 4933722216

3
model-00006-of-00006.safetensors Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:4a4855e8b0b538bca923c6ae5b927e177b2196500f7c47f55f93af25865a912a
size 1245247160

370
model.safetensors.index.json Normal file
View File

@@ -0,0 +1,370 @@
{
"metadata": {
"total_size": 26031749120
},
"weight_map": {
"lm_head.weight": "model-00006-of-00006.safetensors",
"model.embed_tokens.weight": "model-00001-of-00006.safetensors",
"model.layers.0.input_layernorm.weight": "model-00001-of-00006.safetensors",
"model.layers.0.mlp.down_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.0.mlp.gate_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.0.mlp.up_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.0.post_attention_layernorm.weight": "model-00001-of-00006.safetensors",
"model.layers.0.self_attn.k_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.0.self_attn.o_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.0.self_attn.q_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.0.self_attn.v_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.1.input_layernorm.weight": "model-00001-of-00006.safetensors",
"model.layers.1.mlp.down_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.1.mlp.gate_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.1.mlp.up_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.1.post_attention_layernorm.weight": "model-00001-of-00006.safetensors",
"model.layers.1.self_attn.k_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.1.self_attn.o_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.1.self_attn.q_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.1.self_attn.v_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.10.input_layernorm.weight": "model-00002-of-00006.safetensors",
"model.layers.10.mlp.down_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.10.mlp.gate_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.10.mlp.up_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.10.post_attention_layernorm.weight": "model-00002-of-00006.safetensors",
"model.layers.10.self_attn.k_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.10.self_attn.o_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.10.self_attn.q_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.10.self_attn.v_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.11.input_layernorm.weight": "model-00002-of-00006.safetensors",
"model.layers.11.mlp.down_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.11.mlp.gate_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.11.mlp.up_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.11.post_attention_layernorm.weight": "model-00002-of-00006.safetensors",
"model.layers.11.self_attn.k_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.11.self_attn.o_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.11.self_attn.q_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.11.self_attn.v_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.12.input_layernorm.weight": "model-00002-of-00006.safetensors",
"model.layers.12.mlp.down_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.12.mlp.gate_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.12.mlp.up_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.12.post_attention_layernorm.weight": "model-00002-of-00006.safetensors",
"model.layers.12.self_attn.k_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.12.self_attn.o_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.12.self_attn.q_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.12.self_attn.v_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.13.input_layernorm.weight": "model-00002-of-00006.safetensors",
"model.layers.13.mlp.down_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.13.mlp.gate_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.13.mlp.up_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.13.post_attention_layernorm.weight": "model-00002-of-00006.safetensors",
"model.layers.13.self_attn.k_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.13.self_attn.o_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.13.self_attn.q_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.13.self_attn.v_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.14.input_layernorm.weight": "model-00002-of-00006.safetensors",
"model.layers.14.mlp.down_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.14.mlp.gate_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.14.mlp.up_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.14.post_attention_layernorm.weight": "model-00002-of-00006.safetensors",
"model.layers.14.self_attn.k_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.14.self_attn.o_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.14.self_attn.q_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.14.self_attn.v_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.15.input_layernorm.weight": "model-00003-of-00006.safetensors",
"model.layers.15.mlp.down_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.15.mlp.gate_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.15.mlp.up_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.15.post_attention_layernorm.weight": "model-00003-of-00006.safetensors",
"model.layers.15.self_attn.k_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.15.self_attn.o_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.15.self_attn.q_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.15.self_attn.v_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.16.input_layernorm.weight": "model-00003-of-00006.safetensors",
"model.layers.16.mlp.down_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.16.mlp.gate_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.16.mlp.up_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.16.post_attention_layernorm.weight": "model-00003-of-00006.safetensors",
"model.layers.16.self_attn.k_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.16.self_attn.o_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.16.self_attn.q_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.16.self_attn.v_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.17.input_layernorm.weight": "model-00003-of-00006.safetensors",
"model.layers.17.mlp.down_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.17.mlp.gate_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.17.mlp.up_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.17.post_attention_layernorm.weight": "model-00003-of-00006.safetensors",
"model.layers.17.self_attn.k_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.17.self_attn.o_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.17.self_attn.q_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.17.self_attn.v_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.18.input_layernorm.weight": "model-00003-of-00006.safetensors",
"model.layers.18.mlp.down_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.18.mlp.gate_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.18.mlp.up_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.18.post_attention_layernorm.weight": "model-00003-of-00006.safetensors",
"model.layers.18.self_attn.k_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.18.self_attn.o_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.18.self_attn.q_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.18.self_attn.v_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.19.input_layernorm.weight": "model-00003-of-00006.safetensors",
"model.layers.19.mlp.down_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.19.mlp.gate_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.19.mlp.up_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.19.post_attention_layernorm.weight": "model-00003-of-00006.safetensors",
"model.layers.19.self_attn.k_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.19.self_attn.o_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.19.self_attn.q_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.19.self_attn.v_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.2.input_layernorm.weight": "model-00001-of-00006.safetensors",
"model.layers.2.mlp.down_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.2.mlp.gate_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.2.mlp.up_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.2.post_attention_layernorm.weight": "model-00001-of-00006.safetensors",
"model.layers.2.self_attn.k_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.2.self_attn.o_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.2.self_attn.q_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.2.self_attn.v_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.20.input_layernorm.weight": "model-00003-of-00006.safetensors",
"model.layers.20.mlp.down_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.20.mlp.gate_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.20.mlp.up_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.20.post_attention_layernorm.weight": "model-00003-of-00006.safetensors",
"model.layers.20.self_attn.k_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.20.self_attn.o_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.20.self_attn.q_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.20.self_attn.v_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.21.input_layernorm.weight": "model-00003-of-00006.safetensors",
"model.layers.21.mlp.down_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.21.mlp.gate_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.21.mlp.up_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.21.post_attention_layernorm.weight": "model-00003-of-00006.safetensors",
"model.layers.21.self_attn.k_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.21.self_attn.o_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.21.self_attn.q_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.21.self_attn.v_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.22.input_layernorm.weight": "model-00003-of-00006.safetensors",
"model.layers.22.mlp.down_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.22.mlp.gate_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.22.mlp.up_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.22.post_attention_layernorm.weight": "model-00003-of-00006.safetensors",
"model.layers.22.self_attn.k_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.22.self_attn.o_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.22.self_attn.q_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.22.self_attn.v_proj.weight": "model-00003-of-00006.safetensors",
"model.layers.23.input_layernorm.weight": "model-00004-of-00006.safetensors",
"model.layers.23.mlp.down_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.23.mlp.gate_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.23.mlp.up_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.23.post_attention_layernorm.weight": "model-00004-of-00006.safetensors",
"model.layers.23.self_attn.k_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.23.self_attn.o_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.23.self_attn.q_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.23.self_attn.v_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.24.input_layernorm.weight": "model-00004-of-00006.safetensors",
"model.layers.24.mlp.down_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.24.mlp.gate_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.24.mlp.up_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.24.post_attention_layernorm.weight": "model-00004-of-00006.safetensors",
"model.layers.24.self_attn.k_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.24.self_attn.o_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.24.self_attn.q_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.24.self_attn.v_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.25.input_layernorm.weight": "model-00004-of-00006.safetensors",
"model.layers.25.mlp.down_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.25.mlp.gate_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.25.mlp.up_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.25.post_attention_layernorm.weight": "model-00004-of-00006.safetensors",
"model.layers.25.self_attn.k_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.25.self_attn.o_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.25.self_attn.q_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.25.self_attn.v_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.26.input_layernorm.weight": "model-00004-of-00006.safetensors",
"model.layers.26.mlp.down_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.26.mlp.gate_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.26.mlp.up_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.26.post_attention_layernorm.weight": "model-00004-of-00006.safetensors",
"model.layers.26.self_attn.k_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.26.self_attn.o_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.26.self_attn.q_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.26.self_attn.v_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.27.input_layernorm.weight": "model-00004-of-00006.safetensors",
"model.layers.27.mlp.down_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.27.mlp.gate_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.27.mlp.up_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.27.post_attention_layernorm.weight": "model-00004-of-00006.safetensors",
"model.layers.27.self_attn.k_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.27.self_attn.o_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.27.self_attn.q_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.27.self_attn.v_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.28.input_layernorm.weight": "model-00004-of-00006.safetensors",
"model.layers.28.mlp.down_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.28.mlp.gate_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.28.mlp.up_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.28.post_attention_layernorm.weight": "model-00004-of-00006.safetensors",
"model.layers.28.self_attn.k_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.28.self_attn.o_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.28.self_attn.q_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.28.self_attn.v_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.29.input_layernorm.weight": "model-00004-of-00006.safetensors",
"model.layers.29.mlp.down_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.29.mlp.gate_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.29.mlp.up_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.29.post_attention_layernorm.weight": "model-00004-of-00006.safetensors",
"model.layers.29.self_attn.k_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.29.self_attn.o_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.29.self_attn.q_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.29.self_attn.v_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.3.input_layernorm.weight": "model-00001-of-00006.safetensors",
"model.layers.3.mlp.down_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.3.mlp.gate_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.3.mlp.up_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.3.post_attention_layernorm.weight": "model-00001-of-00006.safetensors",
"model.layers.3.self_attn.k_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.3.self_attn.o_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.3.self_attn.q_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.3.self_attn.v_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.30.input_layernorm.weight": "model-00005-of-00006.safetensors",
"model.layers.30.mlp.down_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.30.mlp.gate_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.30.mlp.up_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.30.post_attention_layernorm.weight": "model-00005-of-00006.safetensors",
"model.layers.30.self_attn.k_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.30.self_attn.o_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.30.self_attn.q_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.30.self_attn.v_proj.weight": "model-00004-of-00006.safetensors",
"model.layers.31.input_layernorm.weight": "model-00005-of-00006.safetensors",
"model.layers.31.mlp.down_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.31.mlp.gate_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.31.mlp.up_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.31.post_attention_layernorm.weight": "model-00005-of-00006.safetensors",
"model.layers.31.self_attn.k_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.31.self_attn.o_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.31.self_attn.q_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.31.self_attn.v_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.32.input_layernorm.weight": "model-00005-of-00006.safetensors",
"model.layers.32.mlp.down_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.32.mlp.gate_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.32.mlp.up_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.32.post_attention_layernorm.weight": "model-00005-of-00006.safetensors",
"model.layers.32.self_attn.k_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.32.self_attn.o_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.32.self_attn.q_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.32.self_attn.v_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.33.input_layernorm.weight": "model-00005-of-00006.safetensors",
"model.layers.33.mlp.down_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.33.mlp.gate_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.33.mlp.up_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.33.post_attention_layernorm.weight": "model-00005-of-00006.safetensors",
"model.layers.33.self_attn.k_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.33.self_attn.o_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.33.self_attn.q_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.33.self_attn.v_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.34.input_layernorm.weight": "model-00005-of-00006.safetensors",
"model.layers.34.mlp.down_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.34.mlp.gate_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.34.mlp.up_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.34.post_attention_layernorm.weight": "model-00005-of-00006.safetensors",
"model.layers.34.self_attn.k_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.34.self_attn.o_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.34.self_attn.q_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.34.self_attn.v_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.35.input_layernorm.weight": "model-00005-of-00006.safetensors",
"model.layers.35.mlp.down_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.35.mlp.gate_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.35.mlp.up_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.35.post_attention_layernorm.weight": "model-00005-of-00006.safetensors",
"model.layers.35.self_attn.k_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.35.self_attn.o_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.35.self_attn.q_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.35.self_attn.v_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.36.input_layernorm.weight": "model-00005-of-00006.safetensors",
"model.layers.36.mlp.down_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.36.mlp.gate_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.36.mlp.up_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.36.post_attention_layernorm.weight": "model-00005-of-00006.safetensors",
"model.layers.36.self_attn.k_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.36.self_attn.o_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.36.self_attn.q_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.36.self_attn.v_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.37.input_layernorm.weight": "model-00005-of-00006.safetensors",
"model.layers.37.mlp.down_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.37.mlp.gate_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.37.mlp.up_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.37.post_attention_layernorm.weight": "model-00005-of-00006.safetensors",
"model.layers.37.self_attn.k_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.37.self_attn.o_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.37.self_attn.q_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.37.self_attn.v_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.38.input_layernorm.weight": "model-00006-of-00006.safetensors",
"model.layers.38.mlp.down_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.38.mlp.gate_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.38.mlp.up_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.38.post_attention_layernorm.weight": "model-00006-of-00006.safetensors",
"model.layers.38.self_attn.k_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.38.self_attn.o_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.38.self_attn.q_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.38.self_attn.v_proj.weight": "model-00005-of-00006.safetensors",
"model.layers.39.input_layernorm.weight": "model-00006-of-00006.safetensors",
"model.layers.39.mlp.down_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.39.mlp.gate_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.39.mlp.up_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.39.post_attention_layernorm.weight": "model-00006-of-00006.safetensors",
"model.layers.39.self_attn.k_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.39.self_attn.o_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.39.self_attn.q_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.39.self_attn.v_proj.weight": "model-00006-of-00006.safetensors",
"model.layers.4.input_layernorm.weight": "model-00001-of-00006.safetensors",
"model.layers.4.mlp.down_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.4.mlp.gate_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.4.mlp.up_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.4.post_attention_layernorm.weight": "model-00001-of-00006.safetensors",
"model.layers.4.self_attn.k_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.4.self_attn.o_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.4.self_attn.q_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.4.self_attn.v_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.5.input_layernorm.weight": "model-00001-of-00006.safetensors",
"model.layers.5.mlp.down_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.5.mlp.gate_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.5.mlp.up_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.5.post_attention_layernorm.weight": "model-00001-of-00006.safetensors",
"model.layers.5.self_attn.k_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.5.self_attn.o_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.5.self_attn.q_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.5.self_attn.v_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.6.input_layernorm.weight": "model-00001-of-00006.safetensors",
"model.layers.6.mlp.down_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.6.mlp.gate_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.6.mlp.up_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.6.post_attention_layernorm.weight": "model-00001-of-00006.safetensors",
"model.layers.6.self_attn.k_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.6.self_attn.o_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.6.self_attn.q_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.6.self_attn.v_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.7.input_layernorm.weight": "model-00002-of-00006.safetensors",
"model.layers.7.mlp.down_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.7.mlp.gate_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.7.mlp.up_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.7.post_attention_layernorm.weight": "model-00002-of-00006.safetensors",
"model.layers.7.self_attn.k_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.7.self_attn.o_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.7.self_attn.q_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.7.self_attn.v_proj.weight": "model-00001-of-00006.safetensors",
"model.layers.8.input_layernorm.weight": "model-00002-of-00006.safetensors",
"model.layers.8.mlp.down_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.8.mlp.gate_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.8.mlp.up_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.8.post_attention_layernorm.weight": "model-00002-of-00006.safetensors",
"model.layers.8.self_attn.k_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.8.self_attn.o_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.8.self_attn.q_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.8.self_attn.v_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.9.input_layernorm.weight": "model-00002-of-00006.safetensors",
"model.layers.9.mlp.down_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.9.mlp.gate_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.9.mlp.up_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.9.post_attention_layernorm.weight": "model-00002-of-00006.safetensors",
"model.layers.9.self_attn.k_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.9.self_attn.o_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.9.self_attn.q_proj.weight": "model-00002-of-00006.safetensors",
"model.layers.9.self_attn.v_proj.weight": "model-00002-of-00006.safetensors",
"model.norm.weight": "model-00006-of-00006.safetensors"
}
}

30
special_tokens_map.json Normal file
View File

@@ -0,0 +1,30 @@
{
"bos_token": {
"content": "<s>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"eos_token": {
"content": "</s>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"pad_token": {
"content": "[PAD]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"unk_token": {
"content": "<unk>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
}
}

3
tokenizer.model Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
size 499723

50
tokenizer_config.json Normal file
View File

@@ -0,0 +1,50 @@
{
"add_bos_token": true,
"add_eos_token": false,
"added_tokens_decoder": {
"0": {
"content": "<unk>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"1": {
"content": "<s>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"2": {
"content": "</s>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"32000": {
"content": "[PAD]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
}
},
"bos_token": "<s>",
"clean_up_tokenization_spaces": false,
"eos_token": "</s>",
"legacy": false,
"model_max_length": 1024,
"pad_token": "[PAD]",
"padding_side": "right",
"sp_model_kwargs": {},
"spaces_between_special_tokens": false,
"tokenizer_class": "LlamaTokenizer",
"unk_token": "<unk>",
"use_default_system_prompt": false
}

2011
trainer_state.json Normal file
View File

File diff suppressed because it is too large Load Diff

3
training_args.bin Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:6da0167127a217efe26c78d229b99ad905ad18eecd77581e01cd9a087834755f
size 6456