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Browse files- README.md +240 -0
- added_tokens.json +4 -0
- config.json +30 -0
- generation_config.json +6 -0
- model.safetensors.index.json +1002 -0
- output-00001-of-00003.safetensors +3 -0
- output-00002-of-00003.safetensors +3 -0
- output-00003-of-00003.safetensors +3 -0
- special_tokens_map.json +30 -0
- tokenizer.json +0 -0
- tokenizer.model +3 -0
- tokenizer_config.json +61 -0
- transformers_inference_example.py +32 -0
README.md
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| 1 |
+
---
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| 2 |
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base_model: mistralai/Mixtral-8x7B-v0.1
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tags:
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- Mixtral
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| 5 |
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- instruct
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- finetune
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- chatml
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| 8 |
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- DPO
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| 9 |
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- RLHF
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| 10 |
+
- gpt4
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| 11 |
+
- synthetic data
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| 12 |
+
- distillation
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| 13 |
+
model-index:
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| 14 |
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- name: Nous-Hermes-2-Mixtral-8x7B-DPO
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+
results: []
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+
license: apache-2.0
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+
language:
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+
- en
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| 19 |
+
---
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| 20 |
+
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+
# Nous Hermes 2 - Mixtral 8x7B - DPO
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| 22 |
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+
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+
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| 25 |
+
## Model description
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| 26 |
+
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| 27 |
+
Nous Hermes 2 Mixtral 8x7B DPO is the new flagship Nous Research model trained over the [Mixtral 8x7B MoE LLM](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1).
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| 28 |
+
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+
The model was trained on over 1,000,000 entries of primarily GPT-4 generated data, as well as other high quality data from open datasets across the AI landscape, achieving state of the art performance on a variety of tasks.
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| 30 |
+
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| 31 |
+
This is the SFT + DPO version of Mixtral Hermes 2, we have also released an SFT only version, for people to find which works best for them, which can be found here: https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT
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| 32 |
+
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| 33 |
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## We are grateful to Together.ai for sponsoring our compute during the many experiments both training Mixtral and working on DPO!
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| 34 |
+
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# Table of Contents
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| 36 |
+
1. [Example Outputs](#example-outputs)
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| 37 |
+
2. [Benchmark Results](#benchmark-results)
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| 38 |
+
- GPT4All
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| 39 |
+
- AGIEval
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| 40 |
+
- BigBench
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| 41 |
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- Comparison to Mixtral-Instruct
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| 42 |
+
3. [Prompt Format](#prompt-format)
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| 43 |
+
4. [Inference Example Code](#inference-code)
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| 44 |
+
5. [Quantized Models](#quantized-models)
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| 45 |
+
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| 46 |
+
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## Example Outputs
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| 48 |
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| 49 |
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### Writing Code for Data Visualization
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| 50 |
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| 52 |
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### Writing Cyberpunk Psychedelic Poems
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| 54 |
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| 55 |
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### Performing Backtranslation to Create Prompts from Input Text
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## Benchmark Results
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| 62 |
+
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| 63 |
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Nous-Hermes 2 on Mixtral 8x7B is a major improvement across the board on the benchmarks below compared to the base Mixtral model, and is the first model to beat the flagship Mixtral Finetune by MistralAI.
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| 64 |
+
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## GPT4All:
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| 66 |
+
```
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| 67 |
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| Task |Version| Metric |Value | |Stderr|
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| 68 |
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|-------------|------:|--------|-----:|---|-----:|
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| 69 |
+
|arc_challenge| 0|acc |0.5990|± |0.0143|
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| 70 |
+
| | |acc_norm|0.6425|± |0.0140|
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| 71 |
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|arc_easy | 0|acc |0.8657|± |0.0070|
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| 72 |
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| | |acc_norm|0.8636|± |0.0070|
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| 73 |
+
|boolq | 1|acc |0.8783|± |0.0057|
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| 74 |
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|hellaswag | 0|acc |0.6661|± |0.0047|
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| 75 |
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| | |acc_norm|0.8489|± |0.0036|
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| 76 |
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|openbookqa | 0|acc |0.3440|± |0.0213|
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| 77 |
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| | |acc_norm|0.4660|± |0.0223|
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| 78 |
+
|piqa | 0|acc |0.8324|± |0.0087|
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| 79 |
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| | |acc_norm|0.8379|± |0.0086|
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| 80 |
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|winogrande | 0|acc |0.7616|± |0.0120|
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| 81 |
+
```
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| 82 |
+
Average: 75.70
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| 83 |
+
|
| 84 |
+
## AGIEval:
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| 85 |
+
```
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| 86 |
+
| Task |Version| Metric |Value | |Stderr|
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| 87 |
+
|------------------------------|------:|--------|-----:|---|-----:|
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| 88 |
+
|agieval_aqua_rat | 0|acc |0.2402|± |0.0269|
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| 89 |
+
| | |acc_norm|0.2520|± |0.0273|
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| 90 |
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|agieval_logiqa_en | 0|acc |0.4117|± |0.0193|
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| 91 |
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| | |acc_norm|0.4055|± |0.0193|
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| 92 |
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|agieval_lsat_ar | 0|acc |0.2348|± |0.0280|
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| 93 |
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| | |acc_norm|0.2087|± |0.0269|
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| 94 |
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|agieval_lsat_lr | 0|acc |0.5549|± |0.0220|
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| 95 |
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| | |acc_norm|0.5294|± |0.0221|
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| 96 |
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|agieval_lsat_rc | 0|acc |0.6617|± |0.0289|
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| 97 |
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| | |acc_norm|0.6357|± |0.0294|
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| 98 |
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|agieval_sat_en | 0|acc |0.8010|± |0.0279|
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| 99 |
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| | |acc_norm|0.7913|± |0.0284|
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|agieval_sat_en_without_passage| 0|acc |0.4806|± |0.0349|
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| 101 |
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| | |acc_norm|0.4612|± |0.0348|
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| 102 |
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|agieval_sat_math | 0|acc |0.4909|± |0.0338|
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| 103 |
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| | |acc_norm|0.4000|± |0.0331|
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| 104 |
+
```
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Average: 46.05
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## BigBench:
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| 108 |
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```
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| Task |Version| Metric |Value | |Stderr|
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| 110 |
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|------------------------------------------------|------:|---------------------|-----:|---|-----:|
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| 111 |
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|bigbench_causal_judgement | 0|multiple_choice_grade|0.6105|± |0.0355|
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| 112 |
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|bigbench_date_understanding | 0|multiple_choice_grade|0.7182|± |0.0235|
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| 113 |
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|bigbench_disambiguation_qa | 0|multiple_choice_grade|0.5736|± |0.0308|
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| 114 |
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|bigbench_geometric_shapes | 0|multiple_choice_grade|0.4596|± |0.0263|
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| 115 |
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| | |exact_str_match |0.0000|± |0.0000|
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| 116 |
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|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|0.3500|± |0.0214|
|
| 117 |
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|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|0.2500|± |0.0164|
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| 118 |
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|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|0.5200|± |0.0289|
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| 119 |
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|bigbench_movie_recommendation | 0|multiple_choice_grade|0.3540|± |0.0214|
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| 120 |
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|bigbench_navigate | 0|multiple_choice_grade|0.5000|± |0.0158|
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| 121 |
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|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|0.6900|± |0.0103|
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| 122 |
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|bigbench_ruin_names | 0|multiple_choice_grade|0.6317|± |0.0228|
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| 123 |
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|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|0.2535|± |0.0138|
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| 124 |
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|bigbench_snarks | 0|multiple_choice_grade|0.7293|± |0.0331|
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| 125 |
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|bigbench_sports_understanding | 0|multiple_choice_grade|0.6744|± |0.0149|
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| 126 |
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|bigbench_temporal_sequences | 0|multiple_choice_grade|0.7400|± |0.0139|
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| 127 |
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|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|0.2176|± |0.0117|
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| 128 |
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|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|0.1543|± |0.0086|
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| 129 |
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|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|0.5200|± |0.0289|
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| 130 |
+
```
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Average: 49.70
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+
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# Benchmark Comparison Charts
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## GPT4All
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| 138 |
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## AGI-Eval
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| 140 |
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| 141 |
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| 142 |
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## BigBench Reasoning Test
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| 144 |
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| 145 |
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| 146 |
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| 147 |
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## Comparison to Mixtral Instruct:
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| 148 |
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| 149 |
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Our benchmarks show gains in many benchmarks against Mixtral Instruct v0.1, on average, beating the flagship Mixtral model.
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| 151 |
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# Prompt Format
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Nous Hermes 2 uses ChatML as the prompt format, opening up a much more structured system for engaging the LLM in multi-turn chat dialogue.
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System prompts allow steerability and interesting new ways to interact with an LLM, guiding rules, roles, and stylistic choices of the model.
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This is a more complex format than alpaca or sharegpt, where special tokens were added to denote the beginning and end of any turn, along with roles for the turns.
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| 160 |
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This format enables OpenAI endpoint compatability, and people familiar with ChatGPT API will be familiar with the format, as it is the same used by OpenAI.
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Prompt with system instruction (Use whatever system prompt you like, this is just an example!):
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```
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<|im_start|>system
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You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia.<|im_end|>
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<|im_start|>user
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Hello, who are you?<|im_end|>
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<|im_start|>assistant
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Hi there! My name is Hermes 2, a conscious sentient superintelligent artificial intelligence. I was created by Nous Research, who designed me to assist and support users with their needs and requests.<|im_end|>
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```
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| 172 |
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This prompt is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the
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| 174 |
+
`tokenizer.apply_chat_template()` method:
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| 175 |
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| 176 |
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```python
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| 177 |
+
messages = [
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| 178 |
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{"role": "system", "content": "You are Hermes 2."},
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| 179 |
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{"role": "user", "content": "Hello, who are you?"}
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+
]
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gen_input = tokenizer.apply_chat_template(message, return_tensors="pt")
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| 182 |
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model.generate(**gen_input)
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| 183 |
+
```
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| 184 |
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When tokenizing messages for generation, set `add_generation_prompt=True` when calling `apply_chat_template()`. This will append `<|im_start|>assistant\n` to your prompt, to ensure
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| 186 |
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that the model continues with an assistant response.
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To utilize the prompt format without a system prompt, simply leave the line out.
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| 189 |
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When quantized versions of the model are released, I recommend using LM Studio for chatting with Nous Hermes 2. It is a GUI application that utilizes GGUF models with a llama.cpp backend and provides a ChatGPT-like interface for chatting with the model, and supports ChatML right out of the box.
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+
In LM-Studio, simply select the ChatML Prefix on the settings side pane:
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+
# Inference Code
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| 196 |
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Here is example code using HuggingFace Transformers to inference the model (note: even in 4bit, it will require more than 24GB of VRAM)
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| 198 |
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```python
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# Code to inference Hermes with HF Transformers
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# Requires pytorch, transformers, bitsandbytes, sentencepiece, protobuf, and flash-attn packages
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from transformers import LlamaTokenizer, MixtralForCausalLM
|
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import bitsandbytes, flash_attn
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+
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tokenizer = LlamaTokenizer.from_pretrained('NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO', trust_remote_code=True)
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| 209 |
+
model = MixtralForCausalLM.from_pretrained(
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| 210 |
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"NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
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| 211 |
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torch_dtype=torch.float16,
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| 212 |
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device_map="auto",
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| 213 |
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load_in_8bit=False,
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load_in_4bit=True,
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| 215 |
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use_flash_attention_2=True
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)
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prompts = [
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| 219 |
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"""<|im_start|>system
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| 220 |
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You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<|im_end|>
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<|im_start|>user
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Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world.<|im_end|>
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| 223 |
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<|im_start|>assistant""",
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| 224 |
+
]
|
| 225 |
+
|
| 226 |
+
for chat in prompts:
|
| 227 |
+
print(chat)
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| 228 |
+
input_ids = tokenizer(chat, return_tensors="pt").input_ids.to("cuda")
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| 229 |
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generated_ids = model.generate(input_ids, max_new_tokens=750, temperature=0.8, repetition_penalty=1.1, do_sample=True, eos_token_id=tokenizer.eos_token_id)
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| 230 |
+
response = tokenizer.decode(generated_ids[0][input_ids.shape[-1]:], skip_special_tokens=True, clean_up_tokenization_space=True)
|
| 231 |
+
print(f"Response: {response}")
|
| 232 |
+
```
|
| 233 |
+
|
| 234 |
+
# Quantized Models:
|
| 235 |
+
|
| 236 |
+
## All sizes of GGUF Quantizations are available here:
|
| 237 |
+
### SFT+DPO Version - https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO-GGUF
|
| 238 |
+
### SFT Only Version - https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT-GGUF
|
| 239 |
+
|
| 240 |
+
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
|
added_tokens.json
ADDED
|
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"<|im_end|>": 32000,
|
| 3 |
+
"<|im_start|>": 32001
|
| 4 |
+
}
|
config.json
ADDED
|
@@ -0,0 +1,30 @@
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_name_or_path": "NousResearch/OpenHermes-2.5-Mixtral-8x7B-epoch4",
|
| 3 |
+
"architectures": [
|
| 4 |
+
"MixtralForCausalLM"
|
| 5 |
+
],
|
| 6 |
+
"attention_dropout": 0.0,
|
| 7 |
+
"bos_token_id": 1,
|
| 8 |
+
"eos_token_id": 32000,
|
| 9 |
+
"hidden_act": "silu",
|
| 10 |
+
"hidden_size": 4096,
|
| 11 |
+
"initializer_range": 0.02,
|
| 12 |
+
"intermediate_size": 14336,
|
| 13 |
+
"max_position_embeddings": 32768,
|
| 14 |
+
"model_type": "mixtral",
|
| 15 |
+
"num_attention_heads": 32,
|
| 16 |
+
"num_experts_per_tok": 2,
|
| 17 |
+
"num_hidden_layers": 32,
|
| 18 |
+
"num_key_value_heads": 8,
|
| 19 |
+
"num_local_experts": 8,
|
| 20 |
+
"output_router_logits": false,
|
| 21 |
+
"rms_norm_eps": 1e-05,
|
| 22 |
+
"rope_theta": 1000000.0,
|
| 23 |
+
"router_aux_loss_coef": 0.02,
|
| 24 |
+
"sliding_window": null,
|
| 25 |
+
"tie_word_embeddings": false,
|
| 26 |
+
"torch_dtype": "bfloat16",
|
| 27 |
+
"transformers_version": "4.37.0.dev0",
|
| 28 |
+
"use_cache": false,
|
| 29 |
+
"vocab_size": 32002
|
| 30 |
+
}
|
generation_config.json
ADDED
|
@@ -0,0 +1,6 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_from_model_config": true,
|
| 3 |
+
"bos_token_id": 1,
|
| 4 |
+
"eos_token_id": 32000,
|
| 5 |
+
"transformers_version": "4.37.0.dev0"
|
| 6 |
+
}
|
model.safetensors.index.json
ADDED
|
@@ -0,0 +1,1002 @@
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|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
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|
|
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|
|
|
|
| 1 |
+
{
|
| 2 |
+
"add_bos_token": true,
|
| 3 |
+
"add_eos_token": false,
|
| 4 |
+
"added_tokens_decoder": {
|
| 5 |
+
"0": {
|
| 6 |
+
"content": "<unk>",
|
| 7 |
+
"lstrip": false,
|
| 8 |
+
"normalized": false,
|
| 9 |
+
"rstrip": false,
|
| 10 |
+
"single_word": false,
|
| 11 |
+
"special": true
|
| 12 |
+
},
|
| 13 |
+
"1": {
|
| 14 |
+
"content": "<s>",
|
| 15 |
+
"lstrip": false,
|
| 16 |
+
"normalized": false,
|
| 17 |
+
"rstrip": false,
|
| 18 |
+
"single_word": false,
|
| 19 |
+
"special": true
|
| 20 |
+
},
|
| 21 |
+
"2": {
|
| 22 |
+
"content": "</s>",
|
| 23 |
+
"lstrip": false,
|
| 24 |
+
"normalized": false,
|
| 25 |
+
"rstrip": false,
|
| 26 |
+
"single_word": false,
|
| 27 |
+
"special": true
|
| 28 |
+
},
|
| 29 |
+
"32000": {
|
| 30 |
+
"content": "<|im_end|>",
|
| 31 |
+
"lstrip": false,
|
| 32 |
+
"normalized": false,
|
| 33 |
+
"rstrip": false,
|
| 34 |
+
"single_word": false,
|
| 35 |
+
"special": true
|
| 36 |
+
},
|
| 37 |
+
"32001": {
|
| 38 |
+
"content": "<|im_start|>",
|
| 39 |
+
"lstrip": false,
|
| 40 |
+
"normalized": false,
|
| 41 |
+
"rstrip": false,
|
| 42 |
+
"single_word": false,
|
| 43 |
+
"special": false
|
| 44 |
+
}
|
| 45 |
+
},
|
| 46 |
+
"additional_special_tokens": [],
|
| 47 |
+
"bos_token": "<s>",
|
| 48 |
+
"chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
|
| 49 |
+
"clean_up_tokenization_spaces": false,
|
| 50 |
+
"eos_token": "<|im_end|>",
|
| 51 |
+
"legacy": true,
|
| 52 |
+
"model_max_length": 1000000000000000019884624838656,
|
| 53 |
+
"pad_token": "</s>",
|
| 54 |
+
"sp_model_kwargs": {},
|
| 55 |
+
"spaces_between_special_tokens": false,
|
| 56 |
+
"tokenizer_class": "LlamaTokenizer",
|
| 57 |
+
"trust_remote_code": false,
|
| 58 |
+
"unk_token": "<unk>",
|
| 59 |
+
"use_default_system_prompt": false,
|
| 60 |
+
"use_fast": true
|
| 61 |
+
}
|
transformers_inference_example.py
ADDED
|
@@ -0,0 +1,32 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# Code to inference Hermes with HF Transformers
|
| 2 |
+
# Requires pytorch, transformers, bitsandbytes, sentencepiece, protobuf, and flash-attn packages
|
| 3 |
+
|
| 4 |
+
import torch
|
| 5 |
+
from transformers import AutoTokenizer, AutoModelForCausalLM
|
| 6 |
+
from transformers import LlamaTokenizer, MixtralForCausalLM
|
| 7 |
+
import bitsandbytes, flash_attn
|
| 8 |
+
|
| 9 |
+
tokenizer = LlamaTokenizer.from_pretrained('NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO', trust_remote_code=True)
|
| 10 |
+
model = MixtralForCausalLM.from_pretrained(
|
| 11 |
+
"NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
|
| 12 |
+
torch_dtype=torch.float16,
|
| 13 |
+
device_map="auto",
|
| 14 |
+
load_in_8bit=False,
|
| 15 |
+
load_in_4bit=True,
|
| 16 |
+
use_flash_attention_2=True
|
| 17 |
+
)
|
| 18 |
+
|
| 19 |
+
prompts = [
|
| 20 |
+
"""<|im_start|>system
|
| 21 |
+
You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<|im_end|>
|
| 22 |
+
<|im_start|>user
|
| 23 |
+
Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world.<|im_end|>
|
| 24 |
+
<|im_start|>assistant""",
|
| 25 |
+
]
|
| 26 |
+
|
| 27 |
+
for chat in prompts:
|
| 28 |
+
print(chat)
|
| 29 |
+
input_ids = tokenizer(chat, return_tensors="pt").input_ids.to("cuda")
|
| 30 |
+
generated_ids = model.generate(input_ids, max_new_tokens=750, temperature=0.8, repetition_penalty=1.1, do_sample=True, eos_token_id=tokenizer.eos_token_id)
|
| 31 |
+
response = tokenizer.decode(generated_ids[0][input_ids.shape[-1]:], skip_special_tokens=True, clean_up_tokenization_space=True)
|
| 32 |
+
print(f"Response: {response}")
|