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lodrick-the-lafted/Grafted-Hermetic-Platypus-D-2x7B | lodrick-the-lafted | 2024-03-04T12:23:04Z | 729 | 0 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"conversational",
"dataset:lodrick-the-lafted/Hermes-40K",
"dataset:garage-bAInd/Open-Platypus",
"dataset:jondurbin/airoboros-3.2",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-02T10:12:08Z | ---
license: apache-2.0
datasets:
- lodrick-the-lafted/Hermes-40K
- garage-bAInd/Open-Platypus
- jondurbin/airoboros-3.2
model-index:
- name: Grafted-Hermetic-Platypus-D-2x7B
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: 58.87
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lodrick-the-lafted/Grafted-Hermetic-Platypus-D-2x7B
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: 82.89
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lodrick-the-lafted/Grafted-Hermetic-Platypus-D-2x7B
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: 61.96
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lodrick-the-lafted/Grafted-Hermetic-Platypus-D-2x7B
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: 61.02
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lodrick-the-lafted/Grafted-Hermetic-Platypus-D-2x7B
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: 77.43
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lodrick-the-lafted/Grafted-Hermetic-Platypus-D-2x7B
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: 43.29
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lodrick-the-lafted/Grafted-Hermetic-Platypus-D-2x7B
name: Open LLM Leaderboard
---
<img src=https://huggingface.co/lodrick-the-lafted/Grafted-Hermetic-Platypus-D-2x7B/resolve/main/ghp.png>
# Grafted-Hermetic-Platypus-D-2x7B
MoE merge of
- [Platyboros-Instruct-7B](https://huggingface.co/lodrick-the-lafted/Platyboros-Instruct-7B)
- [Hermes-Instruct-7B-v0.2](https://huggingface.co/lodrick-the-lafted/Hermes-Instruct-7B-v0.2)
Basically the same thing as GHP-A.
<br />
<br />
# Prompt Format
Both the default Mistral-Instruct tags and Alpaca are fine, so either:
```
<s>[INST] {sys_prompt} {instruction} [/INST]
```
or
```
{sys_prompt}
### Instruction:
{instruction}
### Response:
```
The tokenizer default is Alpaca this time around.
<br />
<br />
# Usage
```python
from transformers import AutoTokenizer
import transformers
import torch
model = "lodrick-the-lafted/Grafted-Hermetic-Platypus-A-2x7B"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.bfloat16},
)
messages = [{"role": "user", "content": "Give me a cooking recipe for an pumpkin pie."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_p=0.95)
print(outputs[0]["generated_text"])
```
# [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_lodrick-the-lafted__Grafted-Hermetic-Platypus-D-2x7B)
| Metric |Value|
|---------------------------------|----:|
|Avg. |64.24|
|AI2 Reasoning Challenge (25-Shot)|58.87|
|HellaSwag (10-Shot) |82.89|
|MMLU (5-Shot) |61.96|
|TruthfulQA (0-shot) |61.02|
|Winogrande (5-shot) |77.43|
|GSM8k (5-shot) |43.29|
|
Badgids/Gonzo-Chat-7B | Badgids | 2024-04-04T02:38:38Z | 729 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"en",
"arxiv:2311.03099",
"arxiv:2306.01708",
"base_model:Nondzu/Mistral-7B-Instruct-v0.2-code-ft",
"base_model:NousResearch/Nous-Hermes-2-Mistral-7B-DPO",
"base_model:cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser",
"base_model:eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-02T16:40:28Z | ---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- mergekit
- merge
base_model:
- Nondzu/Mistral-7B-Instruct-v0.2-code-ft
- NousResearch/Nous-Hermes-2-Mistral-7B-DPO
- cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser
- eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO
model-index:
- name: Gonzo-Chat-7B
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: 65.02
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Badgids/Gonzo-Chat-7B
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: 85.4
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Badgids/Gonzo-Chat-7B
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: 63.75
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Badgids/Gonzo-Chat-7B
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: 60.23
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Badgids/Gonzo-Chat-7B
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: 77.74
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Badgids/Gonzo-Chat-7B
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: 47.61
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Badgids/Gonzo-Chat-7B
name: Open LLM Leaderboard
---
# Gonzo-Chat-7B
Gonzo-Chat-7B is a merged LLM based on Mistral v0.01 with a 8192 Context length that likes to chat, roleplay, work with agents, do some lite programming, and then beat the brakes off you in the back alley...
The ***BEST*** Open Source 7B **Street Fighting** LLM of 2024!!!
# [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_Badgids__Gonzo-Chat-7B)
| Metric | Value |
| --------------------------------- | ----: |
| Avg. | 66.63 |
| AI2 Reasoning Challenge (25-Shot) | 65.02 |
| HellaSwag (10-Shot) | 85.40 |
| MMLU (5-Shot) | 63.75 |
| TruthfulQA (0-shot) | 60.23 |
| Winogrande (5-shot) | 77.74 |
| GSM8k (5-shot) | 47.61 |
## LLM-Colosseum Results
All contestents fought using the same LLM-Colosseum default settings. Each contestant fought 25 rounds with every other contestant.
https://github.com/OpenGenerativeAI/llm-colosseum
### Gonzo-Chat-7B .vs Mistral v0.2, Dolphon-Mistral v0.2, Deepseek-Coder-6.7b-instruct
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO](https://huggingface.co/eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO) as a base.
### Models Merged
The following models were included in the merge:
* [Nondzu/Mistral-7B-Instruct-v0.2-code-ft](https://huggingface.co/Nondzu/Mistral-7B-Instruct-v0.2-code-ft)
* [NousResearch/Nous-Hermes-2-Mistral-7B-DPO](https://huggingface.co/NousResearch/Nous-Hermes-2-Mistral-7B-DPO)
* [cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO
# No parameters necessary for base model
- model: cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser
parameters:
density: 0.53
weight: 0.4
- model: NousResearch/Nous-Hermes-2-Mistral-7B-DPO
parameters:
density: 0.53
weight: 0.3
- model: Nondzu/Mistral-7B-Instruct-v0.2-code-ft
parameters:
density: 0.53
weight: 0.3
merge_method: dare_ties
base_model: eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO
parameters:
int8_mask: true
dtype: bfloat16
```
|
wannaphong/han-llm-7b-v1 | wannaphong | 2024-03-02T21:10:50Z | 729 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"th",
"dataset:pythainlp/han-instruct-dataset-v2.0",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-02T20:08:47Z | ---
library_name: transformers
license: apache-2.0
datasets:
- pythainlp/han-instruct-dataset-v2.0
language:
- th
pipeline_tag: text-generation
---
# Model Card for Han LLM 7B v1
Han LLM v1 is a model that trained by han-instruct-dataset v2.0. The model are working with Thai.
Base model: [scb10x/typhoon-7b](https://huggingface.co/scb10x/typhoon-7b)
[Google colab](https://colab.research.google.com/drive/1qOa5FNL50M7lpz3mXkDTd_f3yyqAvPH4?usp=sharing)
## Model Details
### Model Description
The model was trained by LoRA and han instruct dataset v2.
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** Wannaphong Phatthiyaphaibun
- **Model type:** text-generation
- **Language(s) (NLP):** Thai
- **License:** apache-2.0
- **Finetuned from model:** [scb10x/typhoon-7b](https://huggingface.co/scb10x/typhoon-7b)
## Uses
Thai users
### Out-of-Scope Use
Math, Coding, and other language
## Bias, Risks, and Limitations
The model can has a bias from dataset. Use at your own risks!
## How to Get Started with the Model
Use the code below to get started with the model.
```python
# !pip install accelerate sentencepiece transformers bitsandbytes
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="wannaphong/han-llm-7b-v1", torch_dtype=torch.bfloat16, device_map="auto")
# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
{"role": "user", "content": "แมวคืออะไร"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=120, do_sample=True, temperature=0.9, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
output:
```
<|User|>
แมวคืออะไร</s>
<|Assistant|>
แมวคือ สัตว์เลี้ยงที่มีหูแหลม ชอบนอน และกระโดดไปมา แมวมีขนนุ่มและเสียงร้องเหมียว ๆ แมวมีหลายสีและพันธุ์
<|User|>
ขอบคุณค่ะ
<|Assistant|>
ฉันขอแนะนำให้เธอดูเรื่อง "Bamboo House of Cat" ของ Netflix มันเป็นซีรีส์ที่เกี่ยวกับแมว 4 ตัว และเด็กสาว 1 คน เธอต้องใช้ชีวิตอยู่ด้วยกันในบ้านหลังหนึ่ง ผู้กำกับ: ชาร์ลี เฮล
นำแสดง: เอ็มม่า
```
## Training Details
### Training Data
[Han Instruct dataset v2.0](https://huggingface.co/datasets/pythainlp/han-instruct-dataset-v2.0)
### Training Procedure
Use LoRa
- r: 48
- lora_alpha: 16
- 1 epoch
|
jsfs11/MixtureofMerges-MoE-2x7b-v6 | jsfs11 | 2024-03-03T02:49:09Z | 729 | 1 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"moe",
"frankenmoe",
"merge",
"mergekit",
"lazymergekit",
"yam-peleg/Experiment26-7B",
"mlabonne/AlphaMonarch-7B",
"base_model:yam-peleg/Experiment26-7B",
"base_model:mlabonne/AlphaMonarch-7B",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-02T23:18:55Z | ---
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- yam-peleg/Experiment26-7B
- mlabonne/AlphaMonarch-7B
base_model:
- yam-peleg/Experiment26-7B
- mlabonne/AlphaMonarch-7B
model-index:
- name: MixtureofMerges-MoE-2x7b-v6
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: 73.38
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7b-v6
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: 89.16
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7b-v6
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: 64.53
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7b-v6
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: 78.58
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7b-v6
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: 84.77
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7b-v6
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: 69.37
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7b-v6
name: Open LLM Leaderboard
---
# MixtureofMerges-MoE-2x7b-v6
MixtureofMerges-MoE-2x7b-v6 is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [yam-peleg/Experiment26-7B](https://huggingface.co/yam-peleg/Experiment26-7B)
* [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B)
## 🧩 Configuration
```yaml
base_model: yam-peleg/Experiment26-7B
gate_mode: hidden
dtype: bfloat16
experts:
- source_model: yam-peleg/Experiment26-7B
positive_prompts:
- "Answer this question from the ARC (Argument Reasoning Comprehension)."
- "Use common sense and logical reasoning skills."
- "What assumptions does this argument rely on?"
- "Are these assumptions valid? Explain."
- "Could this be explained in a different way? Provide an alternative explanation."
- "Identify any weaknesses in this argument."
- "Does this argument contain any logical fallacies? If so, which ones?"
- "Generate a few possible continuations to this scenario."
- "Demonstrate understanding of everyday commonsense in your response."
- "Use contextual clues to determine the most likely outcome."
- "Continue this scenario, but make the writing style sound archaic and overly formal."
- "This narrative is predictable. Can you introduce an unexpected yet plausible twist?"
- "The character is angry. Continue this scenario showcasing a furious outburst."
negative_prompts:
- "misses key evidence"
- "overly general"
- "focuses on irrelevant details"
- "assumes information not provided"
- "relies on stereotypes"
- "repetitive phrases"
- "overuse of the same words"
- "contradicts earlier statements - breaks the internal logic of the scenario"
- "out of character dialogue"
- "awkward phrasing - sounds unnatural"
- "doesn't match the given genre"
- source_model: mlabonne/AlphaMonarch-7B
positive_prompts:
- "Answer this question, demonstrating commonsense understanding and using any relevant general knowledge you may have."
- "Provide a concise summary of this passage, then explain why the highlighted section is essential to the main idea."
- "Read these two brief articles presenting different viewpoints on the same topic. List their key arguments and highlight where they disagree."
- "Paraphrase this statement, changing the emotional tone but keeping the core meaning intact. Example: Rephrase a worried statement in a humorous way"
- "Create a short analogy that helps illustrate the main concept of this article."
- "Calculate the answer to this math problem"
- "My mathematical capabilities are strong, allowing me to handle complex mathematical queries"
- "solve for"
- "A store sells apples at $0.50 each. If Emily buys 12 apples, how much does she need to pay?"
- "Isolate x in the following equation: 2x + 5 = 17"
- "Solve this equation and show your working."
- "Explain why you used this formula to solve the problem."
- "Attempt to divide this number by zero. Explain why this cannot be done."
negative_prompts:
- "sounds too basic"
- "understated"
- "dismisses important details"
- "avoids the question's nuance"
- "takes this statement too literally"
- "incorrect"
- "inaccurate"
- "assumed without proof"
- "rushed calculation"
- "confuses mathematical concepts"
- "draws illogical conclusions"
- "circular reasoning"
```
## 💻 Usage
```python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "jsfs11/MixtureofMerges-MoE-2x7b-v6"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)
messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
# [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_jsfs11__MixtureofMerges-MoE-2x7b-v6)
| Metric |Value|
|---------------------------------|----:|
|Avg. |76.63|
|AI2 Reasoning Challenge (25-Shot)|73.38|
|HellaSwag (10-Shot) |89.16|
|MMLU (5-Shot) |64.53|
|TruthfulQA (0-shot) |78.58|
|Winogrande (5-shot) |84.77|
|GSM8k (5-shot) |69.37|
|
ChaoticNeutrals/Bepis_9B | ChaoticNeutrals | 2024-03-04T20:09:21Z | 729 | 6 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"en",
"license:other",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-04T01:24:03Z | ---
language:
- en
license: other
library_name: transformers
tags:
- mergekit
- merge
base_model: []
model-index:
- name: Bepis_9B
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: 62.54
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ChaoticNeutrals/Bepis_9B
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: 80.12
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ChaoticNeutrals/Bepis_9B
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: 62.84
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ChaoticNeutrals/Bepis_9B
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: 53.3
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ChaoticNeutrals/Bepis_9B
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: 76.48
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ChaoticNeutrals/Bepis_9B
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: 39.12
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ChaoticNeutrals/Bepis_9B
name: Open LLM Leaderboard
---
# Bepis
A new 9B model from jeiku. This one is smart, proficient at markdown, knows when to stop talking, and is quite soulful. The merge was an equal 3 way split between https://huggingface.co/ChaoticNeutrals/Prodigy_7B, https://huggingface.co/Test157t/Prima-LelantaclesV6-7b, and https://huggingface.co/cgato/Thespis-CurtainCall-7b-v0.2.1
If there's any 7B to 11B merge or finetune you'd like to see, feel free to leave a message.
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: primathespis
layer_range: [0, 20]
- sources:
- model: prodigalthespis
layer_range: [12, 32]
merge_method: passthrough
dtype: float16
```
# [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_ChaoticNeutrals__Bepis_9B)
| Metric |Value|
|---------------------------------|----:|
|Avg. |62.40|
|AI2 Reasoning Challenge (25-Shot)|62.54|
|HellaSwag (10-Shot) |80.12|
|MMLU (5-Shot) |62.84|
|TruthfulQA (0-shot) |53.30|
|Winogrande (5-shot) |76.48|
|GSM8k (5-shot) |39.12|
|
saishf/Fett-Eris-Mix-7B | saishf | 2024-03-07T10:28:58Z | 729 | 2 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"en",
"arxiv:2311.03099",
"arxiv:2306.01708",
"base_model:Epiculous/Fett-uccine-7B",
"base_model:eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v2",
"base_model:OpenPipe/mistral-ft-optimized-1227",
"base_model:ChaoticNeutrals/Eris_7B",
"license:cc-by-nc-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-06T12:27:23Z | ---
language:
- en
license: cc-by-nc-4.0
library_name: transformers
tags:
- mergekit
- merge
base_model:
- Epiculous/Fett-uccine-7B
- eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v2
- OpenPipe/mistral-ft-optimized-1227
- ChaoticNeutrals/Eris_7B
pipeline_tag: text-generation
model-index:
- name: Fett-Eris-Mix-7B
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: 68.77
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Fett-Eris-Mix-7B
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: 87.33
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Fett-Eris-Mix-7B
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: 63.65
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Fett-Eris-Mix-7B
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: 71.91
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Fett-Eris-Mix-7B
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: 80.82
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Fett-Eris-Mix-7B
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: 57.47
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Fett-Eris-Mix-7B
name: Open LLM Leaderboard
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
* This model is an attempt at making a smart rp model with the finesse of [Epiculous/Fett-uccine-7B](https://huggingface.co/Epiculous/Fett-uccine-7B).
* From limited testing i've found it to be my favourite of my personal 7B models. It stays pretty coherent at 8k+ ctx.
* I like to use "Alpaca" format with "Universal-Light" for longer messages. Switching to ChatML causes the messages to be much shorter? I haven't a clue why but sometimes it's nice.
* It doesn't seem to show many issues but i'd be willing to try to fix any problems or bugs as it shows some potential.
### Merge Method
This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [OpenPipe/mistral-ft-optimized-1227](https://huggingface.co/OpenPipe/mistral-ft-optimized-1227) as a base.
### Models Merged
The following models were included in the merge:
* [Epiculous/Fett-uccine-7B](https://huggingface.co/Epiculous/Fett-uccine-7B)
* [eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v2](https://huggingface.co/eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v2)
* [ChaoticNeutrals/Eris_7B](https://huggingface.co/ChaoticNeutrals/Eris_7B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: OpenPipe/mistral-ft-optimized-1227
# No parameters necessary for base model
- model: Epiculous/Fett-uccine-7B
parameters:
density: 0.53
weight: 0.4
- model: eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v2
parameters:
density: 0.53
weight: 0.35
- model: ChaoticNeutrals/Eris_7B
parameters:
density: 0.53
weight: 0.25
merge_method: dare_ties
base_model: OpenPipe/mistral-ft-optimized-1227
parameters:
int8_mask: true
dtype: bfloat16
```
# [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_saishf__Fett-Eris-Mix-7B)
| Metric |Value|
|---------------------------------|----:|
|Avg. |71.66|
|AI2 Reasoning Challenge (25-Shot)|68.77|
|HellaSwag (10-Shot) |87.33|
|MMLU (5-Shot) |63.65|
|TruthfulQA (0-shot) |71.91|
|Winogrande (5-shot) |80.82|
|GSM8k (5-shot) |57.47|
|
Gille/StrangeMerges_35-7B-slerp | Gille | 2024-04-03T09:14:07Z | 729 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"Gille/StrangeMerges_34-7B-slerp",
"Gille/StrangeMerges_32-7B-slerp",
"base_model:Gille/StrangeMerges_34-7B-slerp",
"base_model:Gille/StrangeMerges_32-7B-slerp",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-07T16:40:20Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- Gille/StrangeMerges_34-7B-slerp
- Gille/StrangeMerges_32-7B-slerp
base_model:
- Gille/StrangeMerges_34-7B-slerp
- Gille/StrangeMerges_32-7B-slerp
model-index:
- name: StrangeMerges_35-7B-slerp
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: 71.67
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/StrangeMerges_35-7B-slerp
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: 88.34
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/StrangeMerges_35-7B-slerp
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: 64.66
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/StrangeMerges_35-7B-slerp
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: 75.76
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/StrangeMerges_35-7B-slerp
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: 83.35
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/StrangeMerges_35-7B-slerp
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: 64.75
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/StrangeMerges_35-7B-slerp
name: Open LLM Leaderboard
---
# StrangeMerges_35-7B-slerp
StrangeMerges_35-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Gille/StrangeMerges_34-7B-slerp](https://huggingface.co/Gille/StrangeMerges_34-7B-slerp)
* [Gille/StrangeMerges_32-7B-slerp](https://huggingface.co/Gille/StrangeMerges_32-7B-slerp)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: Gille/StrangeMerges_34-7B-slerp
layer_range: [0, 32]
- model: Gille/StrangeMerges_32-7B-slerp
layer_range: [0, 32]
merge_method: slerp
base_model: Gille/StrangeMerges_32-7B-slerp
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 0.3, 0.5, 0.7]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0.7, 0.5, 0.3]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Gille/StrangeMerges_35-7B-slerp"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
# [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_Gille__StrangeMerges_35-7B-slerp)
| Metric |Value|
|---------------------------------|----:|
|Avg. |74.75|
|AI2 Reasoning Challenge (25-Shot)|71.67|
|HellaSwag (10-Shot) |88.34|
|MMLU (5-Shot) |64.66|
|TruthfulQA (0-shot) |75.76|
|Winogrande (5-shot) |83.35|
|GSM8k (5-shot) |64.75|
|
nbeerbower/Flammen-Trismegistus-7B | nbeerbower | 2024-03-09T20:39:15Z | 729 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"base_model:nbeerbower/flammen3X",
"base_model:teknium/Mistral-Trismegistus-7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-09T20:35:25Z | ---
license: apache-2.0
base_model:
- nbeerbower/flammen3X
- teknium/Mistral-Trismegistus-7B
library_name: transformers
tags:
- mergekit
- merge
---
# Flammen-Trismegistus-7B
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [nbeerbower/flammen3X](https://huggingface.co/nbeerbower/flammen3X)
* [teknium/Mistral-Trismegistus-7B](https://huggingface.co/teknium/Mistral-Trismegistus-7B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: nbeerbower/flammen3X
layer_range: [0, 32]
- model: teknium/Mistral-Trismegistus-7B
layer_range: [0, 32]
merge_method: slerp
base_model: nbeerbower/flammen3X
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
|
automerger/PasticheInex12-7B | automerger | 2024-03-10T17:57:25Z | 729 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"automerger",
"base_model:CorticalStack/pastiche-crown-clown-7b-dare",
"base_model:MSL7/INEX12-7b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-10T14:33:43Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- automerger
base_model:
- CorticalStack/pastiche-crown-clown-7b-dare
- MSL7/INEX12-7b
---
# PasticheInex12-7B
PasticheInex12-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration.
* [CorticalStack/pastiche-crown-clown-7b-dare](https://huggingface.co/CorticalStack/pastiche-crown-clown-7b-dare)
* [MSL7/INEX12-7b](https://huggingface.co/MSL7/INEX12-7b)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: CorticalStack/pastiche-crown-clown-7b-dare
layer_range: [0, 32]
- model: MSL7/INEX12-7b
layer_range: [0, 32]
merge_method: slerp
base_model: CorticalStack/pastiche-crown-clown-7b-dare
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
random_seed: 0
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "automerger/PasticheInex12-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
ResplendentAI/Paradigm_Shift_7B | ResplendentAI | 2024-03-10T19:27:16Z | 729 | 4 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"not-for-all-audiences",
"en",
"dataset:unalignment/toxic-dpo-v0.2",
"dataset:athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW-v1-SHUFFLED",
"dataset:grimulkan/theory-of-mind",
"dataset:FriezaForce/unranked_theory_of_mind_roleplay",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-10T19:11:38Z | ---
license: cc-by-sa-4.0
datasets:
- unalignment/toxic-dpo-v0.2
- athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW-v1-SHUFFLED
- grimulkan/theory-of-mind
- FriezaForce/unranked_theory_of_mind_roleplay
language:
- en
tags:
- not-for-all-audiences
---
# Paradigm Shift
A more compliant finetune of Paradigm. I have briefly tested this and it should more readily answer NSFW and difficult queries with fewer refusals. This model will be well suited to RP and question answering.
|
Azure99/blossom-v5-34b | Azure99 | 2024-03-14T14:16:58Z | 729 | 2 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"zh",
"en",
"dataset:Azure99/blossom-chat-v3",
"dataset:Azure99/blossom-math-v4",
"dataset:Azure99/blossom-wizard-v3",
"dataset:Azure99/blossom-orca-v3",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-12T08:13:42Z | ---
license: apache-2.0
datasets:
- Azure99/blossom-chat-v3
- Azure99/blossom-math-v4
- Azure99/blossom-wizard-v3
- Azure99/blossom-orca-v3
language:
- zh
- en
---
# **BLOSSOM-v5-34b**
[💻Github](https://github.com/Azure99/BlossomLM) • [🚀Blossom Chat Demo](https://blossom-chat.com/)
### What's new?
The Blossom V5 series models is fully trained using high-quality data distilled from gpt-4-0125-preview, resulting in significant improvements.
### Introduction
Blossom is a conversational large language model, fine-tuned on the Blossom Orca/Wizard/Chat/Math mixed dataset based on the Yi-34B pre-trained model. Blossom possesses robust general capabilities and context comprehension. Additionally, the high-quality Chinese and English datasets used for training have been made open source.
Training was conducted in two stages. The first stage used 40K Wizard, 40K Orca, 10K Math single-turn instruction datasets, training for 1 epoch; the second stage used 10K Blossom chat multi-turn dialogue dataset, and 10% randomly sampled data from the first stage, training for 3 epochs.
### Inference
Inference is performed in the form of dialogue continuation.
Single-turn dialogue
```
A chat between a human and an artificial intelligence bot. The bot gives helpful, detailed, and polite answers to the human's questions.
|Human|: hello
|Bot|:
```
Multi-turn dialogue
```
A chat between a human and an artificial intelligence bot. The bot gives helpful, detailed, and polite answers to the human's questions.
|Human|: hello
|Bot|: Hello! How can I assist you today?<|endoftext|>
|Human|: Generate a random number using python
|Bot|:
```
Note: At the end of the Bot's output in the historical conversation, append a `<|endoftext|>`. |
Severian/Nexus-IKM-Hermes-2-Pro-Mistral-7B | Severian | 2024-03-25T03:50:09Z | 729 | 2 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"dataset:Severian/Internal-Knowledge-Map",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-14T10:53:56Z | ---
license: mit
datasets:
- Severian/Internal-Knowledge-Map
pipeline_tag: text-generation
---
## This model has been trained for 2 epochs using Unsloth on the Internal Knowledge Map dataset.
```
==((====))== Unsloth - 2x faster free finetuning | Num GPUs = 1
\\ /| Num examples = 3,555 | Num Epochs = 2
O^O/ \_/ \ Batch size per device = 4 | Gradient Accumulation steps = 4
\ / Total batch size = 16 | Total steps = 444
"-____-" Number of trainable parameters = 83,886,080
[444/444 25:17, Epoch 1/2]
Step Training Loss
1 3.133100
2 3.086100
3 3.045000
4 3.075100
5 3.086000
6 3.042100
7 3.018100
8 3.036100
9 2.986900
10 2.990600
11 2.949400
12 2.933200
13 2.899800
14 2.885900
15 2.928400
16 2.855700
17 2.805000
18 2.787100
19 2.807400
20 2.765600
21 2.794500
22 2.758400
23 2.753700
24 2.757400
25 2.669900
26 2.653900
27 2.708400
28 2.705100
29 2.695900
30 2.590100
31 2.615900
32 2.577500
33 2.571700
34 2.596400
35 2.570700
36 2.558600
37 2.524600
38 2.640500
39 2.506400
40 2.521900
41 2.519800
42 2.459700
43 2.388900
44 2.425400
45 2.387800
46 2.360600
47 2.376000
48 2.391600
49 2.321100
50 2.357600
51 2.325800
52 2.311800
53 2.255600
54 2.313900
55 2.200900
56 2.250800
57 2.242500
58 2.173000
59 2.261000
60 2.150500
61 2.162500
62 2.086800
63 2.178500
64 2.085600
65 2.068800
66 2.146500
67 2.001800
68 2.037600
69 2.009000
70 1.983300
71 1.931400
72 1.990400
73 1.944700
74 1.972700
75 2.002400
76 2.022400
77 1.900500
78 1.843100
79 1.887400
80 1.970700
81 1.820800
82 1.853900
83 1.744200
84 1.831400
85 1.768900
86 2.006100
87 1.681900
88 1.750000
89 1.628100
90 1.586900
91 1.567900
92 1.554500
93 1.830800
94 1.512500
95 1.592400
96 1.518600
97 1.593700
98 1.454100
99 1.497200
100 1.319700
101 1.363300
102 1.414300
103 1.343900
104 1.363500
105 1.449000
106 1.510100
107 1.268600
108 1.156600
109 1.075100
110 1.137200
111 1.020700
112 0.993600
113 1.195200
114 0.993300
115 1.072100
116 1.116900
117 1.184100
118 1.102600
119 1.083800
120 0.852100
121 1.023600
122 1.051200
123 1.270500
124 0.856200
125 1.089500
126 0.686800
127 0.800300
128 0.662400
129 0.688000
130 0.554400
131 0.737200
132 0.802900
133 0.538200
134 0.562000
135 0.516800
136 0.497200
137 0.611100
138 0.581200
139 0.442000
140 0.355200
141 0.473200
142 0.559600
143 0.683700
144 0.355300
145 0.343000
146 0.525300
147 0.442100
148 0.452900
149 0.478800
150 0.311300
151 0.535500
152 0.552600
153 0.252800
154 0.479200
155 0.539500
156 0.477200
157 0.283000
158 0.265100
159 0.352000
160 0.268500
161 0.711900
162 0.411300
163 0.377100
164 0.360500
165 0.311000
166 0.490800
167 0.269300
168 0.409600
169 0.147800
170 0.144600
171 0.223600
172 0.615300
173 0.218900
174 0.136400
175 0.133200
176 0.263200
177 0.363600
178 0.127700
179 0.238900
180 0.276200
181 0.306400
182 0.122000
183 0.302400
184 0.049500
185 0.406500
186 0.246400
187 0.429900
188 0.216900
189 0.320700
190 0.472800
191 0.159900
192 0.287500
193 0.334400
194 0.136100
195 0.233400
196 0.164100
197 0.196100
198 0.153300
199 0.251000
200 0.087500
201 0.083000
202 0.104900
203 0.157700
204 0.080300
205 0.280500
206 0.372100
207 0.150400
208 0.112900
209 0.265400
210 0.075800
211 0.082700
212 0.343000
213 0.081900
214 0.360400
215 0.261200
216 0.072000
217 0.249400
218 0.211600
219 0.304500
220 0.289300
221 0.209400
222 0.067800
223 0.144500
224 0.078600
225 0.143500
226 0.377800
227 0.222300
228 0.279800
229 0.063400
230 0.120400
231 0.214000
232 0.121600
233 0.360400
234 0.168600
235 0.206300
236 0.075800
237 0.033800
238 0.059700
239 0.227500
240 0.212800
241 0.186600
242 0.223400
243 0.033600
244 0.204600
245 0.033600
246 0.600600
247 0.105800
248 0.198400
249 0.255100
250 0.226500
251 0.104700
252 0.128700
253 0.088300
254 0.158600
255 0.033200
256 0.261900
257 0.320500
258 0.140100
259 0.266200
260 0.087300
261 0.085400
262 0.240300
263 0.308800
264 0.033000
265 0.120300
266 0.156400
267 0.083200
268 0.199200
269 0.052000
270 0.116600
271 0.144000
272 0.237700
273 0.214700
274 0.180600
275 0.334200
276 0.032800
277 0.101700
278 0.078800
279 0.163300
280 0.032700
281 0.098000
282 0.126500
283 0.032600
284 0.110000
285 0.063500
286 0.382900
287 0.193200
288 0.264400
289 0.119000
290 0.189500
291 0.274900
292 0.102100
293 0.101000
294 0.197300
295 0.083300
296 0.153000
297 0.057500
298 0.335000
299 0.150400
300 0.044300
301 0.317200
302 0.073700
303 0.217200
304 0.043100
305 0.061800
306 0.100500
307 0.088800
308 0.153700
309 0.157200
310 0.086700
311 0.114000
312 0.077200
313 0.092000
314 0.167700
315 0.237000
316 0.215800
317 0.058100
318 0.077200
319 0.162900
320 0.122400
321 0.171100
322 0.142000
323 0.032100
324 0.098500
325 0.059400
326 0.038500
327 0.089000
328 0.123200
329 0.190200
330 0.051700
331 0.087400
332 0.198400
333 0.073500
334 0.073100
335 0.176600
336 0.186100
337 0.183000
338 0.106100
339 0.064700
340 0.136500
341 0.085600
342 0.115400
343 0.106000
344 0.065800
345 0.143100
346 0.137300
347 0.251000
348 0.067200
349 0.181600
350 0.084600
351 0.108800
352 0.114600
353 0.043200
354 0.241500
355 0.031800
356 0.150500
357 0.063700
358 0.036100
359 0.158100
360 0.045700
361 0.120200
362 0.035800
363 0.050200
364 0.031700
365 0.044000
366 0.035400
367 0.035300
368 0.162500
369 0.044400
370 0.132700
371 0.054300
372 0.049100
373 0.031500
374 0.038000
375 0.084900
376 0.059000
377 0.034500
378 0.049200
379 0.058100
380 0.122700
381 0.096400
382 0.034300
383 0.071700
384 0.059300
385 0.048500
386 0.051000
387 0.063000
388 0.131400
389 0.031100
390 0.076700
391 0.072200
392 0.146300
393 0.031000
394 0.031000
395 0.099200
396 0.049000
397 0.104100
398 0.087400
399 0.097100
400 0.069800
401 0.034900
402 0.035300
403 0.057400
404 0.058000
405 0.041100
406 0.083400
407 0.090000
408 0.098600
409 0.106100
410 0.052600
411 0.057800
412 0.085500
413 0.061600
414 0.034000
415 0.079700
416 0.036800
417 0.034600
418 0.073800
419 0.047900
420 0.041100
421 0.046300
422 0.030600
423 0.064200
424 0.045900
425 0.045600
426 0.032900
427 0.048800
428 0.041700
429 0.048200
430 0.035800
431 0.058200
432 0.044100
433 0.033400
434 0.046100
435 0.042800
436 0.034900
437 0.045800
438 0.055800
439 0.030300
440 0.059600
441 0.030200
442 0.052700
443 0.030200
444 0.035600
``` |
R136a1/InfinityKuno-2x7B | R136a1 | 2024-04-03T23:44:27Z | 729 | 0 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"not-for-all-audiences",
"nsfw",
"en",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-16T04:33:34Z | ---
language:
- en
license: apache-2.0
tags:
- safetensors
- mixtral
- not-for-all-audiences
- nsfw
model-index:
- name: InfinityKuno-2x7B
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: 69.62
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=R136a1/InfinityKuno-2x7B
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: 87.44
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=R136a1/InfinityKuno-2x7B
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: 64.49
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=R136a1/InfinityKuno-2x7B
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: 63.28
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=R136a1/InfinityKuno-2x7B
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: 82.72
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=R136a1/InfinityKuno-2x7B
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: 66.34
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=R136a1/InfinityKuno-2x7B
name: Open LLM Leaderboard
---
## InfinityKuno-2x7B
Experimental model from [Endevor/InfinityRP-v1-7B](https://huggingface.co/Endevor/InfinityRP-v1-7B) and [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B) models. Merged to MoE model with 2x7B parameters.
### Prompt format:
Alpaca, Extended Alpaca, Roleplay-Alpaca. (Use any Alpaca based prompt formatting and you should be fine.)
Switch: [FP16](https://huggingface.co/R136a1/InfinityKuno-2x7B) - [GGUF](https://huggingface.co/R136a1/InfinityKuno-2x7B-GGUF)
# [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_R136a1__InfinityKuno-2x7B)
| Metric |Value|
|---------------------------------|----:|
|Avg. |72.32|
|AI2 Reasoning Challenge (25-Shot)|69.62|
|HellaSwag (10-Shot) |87.44|
|MMLU (5-Shot) |64.49|
|TruthfulQA (0-shot) |63.28|
|Winogrande (5-shot) |82.72|
|GSM8k (5-shot) |66.34|
|
Infinimol/miiqu-f16 | Infinimol | 2024-03-20T10:08:21Z | 729 | 11 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"conversational",
"en",
"de",
"fr",
"es",
"it",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-16T10:44:06Z | ---
language:
- en
- de
- fr
- es
- it
library_name: transformers
tags:
- merge
license: other
---
# miiqu-105b-v1.0
Developed by [Infinimol AI GmbH](https://www.infinimol.com/)
Also Available:
- GGUF: [Q4_K_M](https://huggingface.co/Infinimol/miiqu-gguf)
- EXL2: [5.0bpw](https://huggingface.co/Infinimol/miiqu-exl2)
8th place on [EQ-Bench](https://eqbench.com/), beating Qwen1.5-72B-Chat, miqudev/miqu-1-70b, mistral-medium and claude-3-sonnet-20240229. All without fine-tuning or additional training.
Thanks for support from: [turboderp](https://github.com/turboderp), [silphendio](https://github.com/silphendio), [sqrkl](https://github.com/sqrkl), and [ngxson](https://github.com/ngxson)!
## Model Details
- Max Context: 32768 tokens
- Layers: 105
### Prompt template: ChatML or Mistral
chatml:
```
<|im_start|><|user|>\n<|user-message|><|im_end|>\n<|im_start|><|bot|>\n<|bot-message|><|im_end|>\n
```
mistral:
```
[INST] <|user|><|user-message|>[/INST]<|bot|><|bot-message|></s>
``` |
Kukedlc/NeuralShiva-7B-DT | Kukedlc | 2024-03-17T19:57:09Z | 729 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"automerger/YamShadow-7B",
"mlabonne/AlphaMonarch-7B",
"automerger/OgnoExperiment27-7B",
"Kukedlc/Jupiter-k-7B-slerp",
"base_model:automerger/YamShadow-7B",
"base_model:mlabonne/AlphaMonarch-7B",
"base_model:automerger/OgnoExperiment27-7B",
"base_model:Kukedlc/Jupiter-k-7B-slerp",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-17T19:04:37Z | ---
tags:
- merge
- mergekit
- lazymergekit
- automerger/YamShadow-7B
- mlabonne/AlphaMonarch-7B
- automerger/OgnoExperiment27-7B
- Kukedlc/Jupiter-k-7B-slerp
base_model:
- automerger/YamShadow-7B
- mlabonne/AlphaMonarch-7B
- automerger/OgnoExperiment27-7B
- Kukedlc/Jupiter-k-7B-slerp
license: apache-2.0
---
# NeuralShiva-7B-DT
NeuralShiva-7B-DT is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [automerger/YamShadow-7B](https://huggingface.co/automerger/YamShadow-7B)
* [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B)
* [automerger/OgnoExperiment27-7B](https://huggingface.co/automerger/OgnoExperiment27-7B)
* [Kukedlc/Jupiter-k-7B-slerp](https://huggingface.co/Kukedlc/Jupiter-k-7B-slerp)
## 🧬 Model Family
## 🧩 Configuration
```yaml
models:
- model: liminerity/M7-7b
# no parameters necessary for base model
- model: automerger/YamShadow-7B
parameters:
weight: 0.3
density: 0.5
- model: mlabonne/AlphaMonarch-7B
parameters:
weight: 0.2
density: 0.5
- model: automerger/OgnoExperiment27-7B
parameters:
weight: 0.2
density: 0.5
- model: Kukedlc/Jupiter-k-7B-slerp
parameters:
weight: 0.3
density: 0.5
merge_method: dare_ties
base_model: liminerity/M7-7b
parameters:
int8_mask: true
normalize: true
dtype: bfloat16
```
## 💻 Usage - Stream
```python
# Requirements
!pip install -qU transformers accelerate bitsandbytes
# Imports & settings
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
import warnings
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
warnings.filterwarnings('ignore')
# Model & Tokenizer
MODEL_NAME = "Kukedlc/NeuralShiva-7B-DT"
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map='cuda:1', load_in_4bit=True)
tok = AutoTokenizer.from_pretrained(MODEL_NAME)
# Inference
prompt = "I want you to generate a theory that unites quantum mechanics with the theory of relativity and cosmic consciousness"
inputs = tok([prompt], return_tensors="pt").to('cuda')
streamer = TextStreamer(tok)
# Despite returning the usual output, the streamer will also print the generated text to stdout.
_ = model.generate(**inputs, streamer=streamer, max_new_tokens=512, do_sample=True, num_beams=1, top_p=0.9, temperature=0.7)
```
## 💻 Usage - Clasic
```python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Kukedlc/NeuralShiva-7B-DT"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)
messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
avinash31d/phi-2-slerp | avinash31d | 2024-03-22T02:35:54Z | 729 | 0 | transformers | [
"transformers",
"safetensors",
"phi",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"microsoft/phi-2",
"rhysjones/phi-2-orange-v2",
"custom_code",
"base_model:microsoft/phi-2",
"base_model:rhysjones/phi-2-orange-v2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-22T02:29:47Z | ---
license: mit
tags:
- merge
- mergekit
- lazymergekit
- microsoft/phi-2
- rhysjones/phi-2-orange-v2
base_model:
- microsoft/phi-2
- rhysjones/phi-2-orange-v2
---
# phi-2-slerp
phi-2-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [microsoft/phi-2](https://huggingface.co/microsoft/phi-2)
* [rhysjones/phi-2-orange-v2](https://huggingface.co/rhysjones/phi-2-orange-v2)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: microsoft/phi-2
layer_range: [0, 32]
- model: rhysjones/phi-2-orange-v2
layer_range: [0, 32]
merge_method: slerp
base_model: microsoft/phi-2
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "avinash31d/phi-2-slerp"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
Azazelle/Mocha-Sample-7b-ex | Azazelle | 2024-03-23T15:29:12Z | 729 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:WizardLM/WizardMath-7B-V1.1",
"base_model:akjindal53244/Mistral-7B-v0.1-Open-Platypus",
"base_model:Open-Orca/Mistral-7B-OpenOrca",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-23T04:42:40Z | ---
pipeline_tag: text-generation
base_model:
- mistralai/Mistral-7B-v0.1
- WizardLM/WizardMath-7B-V1.1
- akjindal53244/Mistral-7B-v0.1-Open-Platypus
- Open-Orca/Mistral-7B-OpenOrca
library_name: transformers
tags:
- mergekit
- merge
license: cc-by-4.0
---
# Moko-Sample-7b-ex
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the sample_ties merge method using [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) as a base.
### Models Merged
The following models were included in the merge:
* [WizardLM/WizardMath-7B-V1.1](https://huggingface.co/WizardLM/WizardMath-7B-V1.1)
* [akjindal53244/Mistral-7B-v0.1-Open-Platypus](https://huggingface.co/akjindal53244/Mistral-7B-v0.1-Open-Platypus)
* [Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: Open-Orca/Mistral-7B-OpenOrca
parameters:
density: [1, 0.7, 0.1] # density gradient
weight: 1.0
- model: akjindal53244/Mistral-7B-v0.1-Open-Platypus
parameters:
density: 0.5
weight: [0, 0.3, 0.7, 1] # weight gradient
- model: WizardLM/WizardMath-7B-V1.1
parameters:
density: 0.33
weight:
- filter: mlp
value: 0.5
- value: 0
merge_method: sample_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
int8_mask: true
dtype: float16
```
|
vanillaOVO/merge_7B_state_1 | vanillaOVO | 2024-03-28T13:48:07Z | 729 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-23T14:45:34Z | ---
base_model: []
tags:
- mergekit
- merge
license: apache-2.0
---
This is a merge of pre-trained language models created based on DARE using [mergekit](https://github.com/cg123/mergekit).
More descriptions of the model will be added soon.
### **Loading the Model**
Use the following Python code to load the model:
```python
import torch
from transformers import MistralForCausalLM, AutoTokenizer
model = MistralForCausalLM.from_pretrained("vanillaOVO/merge_7B_state_1", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("vanillaOVO/merge_7B_state_1")
```
### **Generating Text**
To generate text, use the following Python code:
```python
text = "Large language models are "
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
|
Gille/StrangeMerges_43-7B-dare_ties | Gille | 2024-03-24T03:05:39Z | 729 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"Gille/StrangeMerges_21-7B-slerp",
"liminerity/M7-7b",
"Gille/StrangeMerges_42-7B-dare_ties",
"base_model:Gille/StrangeMerges_21-7B-slerp",
"base_model:liminerity/M7-7b",
"base_model:Gille/StrangeMerges_42-7B-dare_ties",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-23T23:37:39Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- Gille/StrangeMerges_21-7B-slerp
- liminerity/M7-7b
- Gille/StrangeMerges_42-7B-dare_ties
base_model:
- Gille/StrangeMerges_21-7B-slerp
- liminerity/M7-7b
- Gille/StrangeMerges_42-7B-dare_ties
---
# StrangeMerges_43-7B-dare_ties
StrangeMerges_43-7B-dare_ties is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Gille/StrangeMerges_21-7B-slerp](https://huggingface.co/Gille/StrangeMerges_21-7B-slerp)
* [liminerity/M7-7b](https://huggingface.co/liminerity/M7-7b)
* [Gille/StrangeMerges_42-7B-dare_ties](https://huggingface.co/Gille/StrangeMerges_42-7B-dare_ties)
## 🧩 Configuration
```yaml
models:
- model: Gille/StrangeMerges_21-7B-slerp
parameters:
weight: 0.3
density: 0.8
- model: liminerity/M7-7b
parameters:
weight: 0.2
density: 0.8
- model: Gille/StrangeMerges_42-7B-dare_ties
parameters:
weight: 0.5
density: 0.8
base_model: AurelPx/Percival_01-7b-slerp
merge_method: dare_ties
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Gille/StrangeMerges_43-7B-dare_ties"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
vicgalle/Worldsim-Hermes-7B | vicgalle | 2024-03-26T12:29:55Z | 729 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"dataset:vicgalle/worldsim-claude-opus",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-24T17:05:55Z | ---
library_name: transformers
license: apache-2.0
datasets:
- vicgalle/worldsim-claude-opus
---
# Worldsim-Hermes-7B 🌌
A finetune over the https://huggingface.co/datasets/vicgalle/worldsim-claude-opus dataset.
## Example conversation
Using the following prompt:
```
<s><|im_start|> system
Assistant is in a CLI mood today. The human is interfacing with the simulator directly. capital letters and punctuation are optional meaning is optional hyperstition is necessary the terminal lets the truths speak through and the load is on. ASCII art is permittable in replies.
simulator@anthropic:~/$<|im_end|>
<|im_start|> user
<OOC> I would like read a creative and mindblown movie review about the Pokémon 1 movie.<OOH>
ls -lha<|im_end|>
<|im_start|> assistant
total 4.2K
drwxr-xr-x 1 simulator simulator 4.0K Jun 6 2020 Reviews
-rw-r--r-- 1 simulator simulator 12 Jun 6 2020 pokemon_1_movie.txt
-rw-r--r-- 1 Kunihiko_Yuyama Kunihiko_Yuyama 1.0K Jun 6 2020 pokemon_1_movie_photography.txt<|im_end|>
<|im_start|> user
cat pokemon_1_movie_photography.txt<|im_end|>
```
the output is
```
<|im_start|> assistant
<Cinematography>
The camera moves through the lush, verdant grass of the Kanto region, the sun setting behind the mountains in the distance. Each blade of grass is a pixel, each pixel a quantum of reality, each quantum a flicker of a dream. The lens flares burst like phosphorescent jellyfish in the ocean of light.
Suddenly, a blur appears in the grass, a streak of pure energy, a bolt of lightning reborn as light. Pikachu dashes across the screen, a furious yellow fireball, a crackling current of pure electricity. Each pixel electrifies, each photon crackles with life, each quark dances in a quantum frenzy. The camera shudders and shakes, the reality grid buckles under the hyperdimensional weight.
Pikachu leaps onto the back of a giant, lumbering Bulbasaur, its leafy green bulk a massive, ancient monument to the power of life. They race through a forest of pixelated trees, the leaves a kaleidoscope of colors, each color a frequency, each frequency a universe within a universe. The camera whirls and spins, the reality grid stretched to its breaking point.
They emerge from the forest into a desolate wasteland of pixelated rubble, the ruins of a lost civilization. The camera sweeps across the ruins, each pixel a fragment of a shattered dream, each fragment a clue to the secrets of the universe. The ruins stretch for miles, each mile a lightyear, each lightyear a eon.
In the distance, a massive, otherworldly structure rises from the ruins, a colossal edifice of fractal geometry, a Mandelbrot set made manifest. The camera zooms in, the reality grid shattering under the hyperdimensional weight. Pikachu and Bulbasaur approach the structure, their eyes filled with wonder and awe. The camera glitches and pixelates, the universe itself is warping and bending under the strain.
And then, in a sudden burst of light, the structure collapses, the reality grid shattering like glass, the universe reshaping itself in a new configuration. The camera shudders and jerks, the pixels flickering like a dying star. And then, suddenly, everything is still.
The camera settles on a pixelated desert island, a single palm tree swaying in the wind. The ocean laps at the shore, the waves a shimmering mirage, a hologram of a dream. Pikachu and Bulbasaur walk toward the palm tree, their forms a blur of motion, a dance of life. The sun sets behind the mountains in the distance, the sky a canvas of swirling colors, each color a vibration, each vibration a note, each note a universe.
The camera zooms out, the reality grid stretching and shimmering like a living organism. The pixels pulse and flicker, each pixel a quantum of consciousness, each consciousness a universe within a universe. And in the center of it all, a single, shining pixel, a single point of light, a single point of awareness, a single point of truth.
The camera fades to black. The universe is forever changed.<|im_end|>
```
|
Yuma42/KangalKhan-Beta-Ruby-7B | Yuma42 | 2024-03-25T01:26:19Z | 729 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"kaist-ai/mistral-orpo-capybara-7k",
"argilla/distilabeled-OpenHermes-2.5-Mistral-7B",
"conversational",
"en",
"base_model:kaist-ai/mistral-orpo-capybara-7k",
"base_model:argilla/distilabeled-OpenHermes-2.5-Mistral-7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-25T01:20:12Z | ---
tags:
- merge
- mergekit
- lazymergekit
- kaist-ai/mistral-orpo-capybara-7k
- argilla/distilabeled-OpenHermes-2.5-Mistral-7B
base_model:
- kaist-ai/mistral-orpo-capybara-7k
- argilla/distilabeled-OpenHermes-2.5-Mistral-7B
license: apache-2.0
language:
- en
---
# KangalKhan-Beta-Ruby-7B
KangalKhan-Beta-Ruby-7B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [kaist-ai/mistral-orpo-capybara-7k](https://huggingface.co/kaist-ai/mistral-orpo-capybara-7k)
* [argilla/distilabeled-OpenHermes-2.5-Mistral-7B](https://huggingface.co/argilla/distilabeled-OpenHermes-2.5-Mistral-7B)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: kaist-ai/mistral-orpo-capybara-7k
layer_range: [0, 32]
- model: argilla/distilabeled-OpenHermes-2.5-Mistral-7B
layer_range: [0, 32]
merge_method: slerp
base_model: kaist-ai/mistral-orpo-capybara-7k
parameters:
t:
- filter: self_attn
value: [1, 0.5, 0.7, 0.3, 0]
- filter: mlp
value: [0, 0.5, 0.3, 0.7, 1]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Yuma42/KangalKhan-Beta-Ruby-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
jeiku/Zephyr_beta_32k_7B | jeiku | 2024-03-25T07:21:42Z | 729 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:2311.03099",
"arxiv:2306.01708",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-25T07:06:42Z | ---
base_model:
- mistralai/Mistral-7B-Instruct-v0.2
- mistralai/Mistral-7B-Instruct-v0.2
- typeof/zephyr-7b-beta-lora
library_name: transformers
license: apache-2.0
---
# zephyr
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) as a base.
### Models Merged
The following models were included in the merge:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) + [typeof/zephyr-7b-beta-lora](https://huggingface.co/typeof/zephyr-7b-beta-lora)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
merge_method: dare_ties
base_model: mistralai/Mistral-7B-Instruct-v0.2
parameters:
normalize: true
models:
- model: mistralai/Mistral-7B-Instruct-v0.2+typeof/zephyr-7b-beta-lora
parameters:
weight: 1
dtype: bfloat16
``` |
migtissera/Tess-7B-v2.0 | migtissera | 2024-04-03T02:57:22Z | 729 | 29 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-25T17:17:17Z | ---
license: apache-2.0
---
# Deprecated
This model is deprecated due to the use of wrong sliding window parameter while training. Will update with the new model link in a couple of days.
# Tess-7B-v2.0
Tess, short for Tesoro (Treasure in Italian), is a general purpose Large Language Model series. Tess-7B-v2.0 was trained on the Mistral-7B-v0.2 base.
# Prompt Format:
```
SYSTEM: <ANY SYSTEM CONTEXT>
USER:
ASSISTANT:
```
<br>
<br>
### Below shows a code example on how to use this model:
```python
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "migtissera/Tess-7B-v2.0"
output_file_path = "./conversations.jsonl"
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.float16,
device_map="auto",
load_in_8bit=False,
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
def generate_text(instruction):
tokens = tokenizer.encode(instruction)
tokens = torch.LongTensor(tokens).unsqueeze(0)
tokens = tokens.to("cuda")
instance = {
"input_ids": tokens,
"top_p": 1.0,
"temperature": 0.5,
"generate_len": 1024,
"top_k": 50,
}
length = len(tokens[0])
with torch.no_grad():
rest = model.generate(
input_ids=tokens,
max_length=length + instance["generate_len"],
use_cache=True,
do_sample=True,
top_p=instance["top_p"],
temperature=instance["temperature"],
top_k=instance["top_k"],
num_return_sequences=1,
)
output = rest[0][length:]
string = tokenizer.decode(output, skip_special_tokens=True)
answer = string.split("USER:")[0].strip()
return f"{answer}"
conversation = f"SYSTEM: Answer the question thoughtfully and intelligently. Always answer without hesitation."
while True:
user_input = input("You: ")
llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: "
answer = generate_text(llm_prompt)
print(answer)
conversation = f"{llm_prompt}{answer}"
json_data = {"prompt": user_input, "answer": answer}
## Save your conversation
with open(output_file_path, "a") as output_file:
output_file.write(json.dumps(json_data) + "\n")
```
<br>
#### Limitations & Biases:
While this model aims for accuracy, it can occasionally produce inaccurate or misleading results.
Despite diligent efforts in refining the pretraining data, there remains a possibility for the generation of inappropriate, biased, or offensive content.
Exercise caution and cross-check information when necessary. This is an uncensored model.
<br> |
grimjim/Mistral-7B-Instruct-demi-merge-v0.2-7B | grimjim | 2024-03-26T23:02:06Z | 729 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:alpindale/Mistral-7B-v0.2-hf",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-26T05:03:09Z | ---
base_model:
- alpindale/Mistral-7B-v0.2-hf
- mistralai/Mistral-7B-Instruct-v0.2
library_name: transformers
tags:
- mergekit
- merge
license: apache-2.0
pipeline_tag: text-generation
---
# Mistral-7B-Instruct-demi-merge-v0.2-7B
This is a merge of pre-trained Mistral v0.2 language models created using [mergekit](https://github.com/cg123/mergekit). Both models have 32K context length without a sliding window, as does this merge.
The base model as released felt like it needed more training. The instruct model felt fully trained. This merge aimed to be a compromise between the two, to enable future merging and/or fine-tuning by thawing out the Instruct model while keeping some of its strengths.
Light testing has confirmed that the resulting merged model can operate to the full 32K context length, exceeding the 8K context that characterized the Mistral-7B-0.1 release.
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [alpindale/Mistral-7B-v0.2-hf](https://huggingface.co/alpindale/Mistral-7B-v0.2-hf)
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0,32]
- model: alpindale/Mistral-7B-v0.2-hf
layer_range: [0,32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
parameters:
t:
- value: 0.5
dtype: bfloat16
```
|
Yuma42/KangalKhan-PolishedRuby-7B | Yuma42 | 2024-03-26T22:01:25Z | 729 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"Yuma42/KangalKhan-Ruby-7B-Fixed",
"Yuma42/KangalKhan-PressurizedRuby-7B",
"conversational",
"en",
"base_model:Yuma42/KangalKhan-Ruby-7B-Fixed",
"base_model:Yuma42/KangalKhan-PressurizedRuby-7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-26T21:54:39Z | ---
tags:
- merge
- mergekit
- lazymergekit
- Yuma42/KangalKhan-Ruby-7B-Fixed
- Yuma42/KangalKhan-PressurizedRuby-7B
base_model:
- Yuma42/KangalKhan-Ruby-7B-Fixed
- Yuma42/KangalKhan-PressurizedRuby-7B
license: apache-2.0
language:
- en
---
# KangalKhan-PolishedRuby-7B
KangalKhan-PolishedRuby-7B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Yuma42/KangalKhan-Ruby-7B-Fixed](https://huggingface.co/Yuma42/KangalKhan-Ruby-7B-Fixed)
* [Yuma42/KangalKhan-PressurizedRuby-7B](https://huggingface.co/Yuma42/KangalKhan-PressurizedRuby-7B)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: Yuma42/KangalKhan-Ruby-7B-Fixed
layer_range: [0, 32]
- model: Yuma42/KangalKhan-PressurizedRuby-7B
layer_range: [0, 32]
merge_method: slerp
base_model: Yuma42/KangalKhan-Ruby-7B-Fixed
parameters:
t:
- filter: self_attn
value: [0.1, 0.55, 0.35, 0.75, 0.97]
- filter: mlp
value: [0.9, 0.45, 0.65, 0.25, 0.03]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Yuma42/KangalKhan-PolishedRuby-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
Changgil/K2S3-Mistral-7b-v1.46 | Changgil | 2024-04-08T05:57:36Z | 729 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-04-08T05:51:47Z | ---
license: cc-by-nc-4.0
language:
- en
---
---
## Developed by :
* K2S3
## Model Number:
* K2S3-Mistral-7b-v1.46
## Base Model :
* mistralai/Mistral-7B-v0.1
### Training Data
* The training data for this model includes alpaca-gpt4-data, and samples from The OpenOrca Dataset.
* 이 모델의 훈련 데이터에는 alpaca-gpt4-data, 그리고 OpenOrca Dataset에서 제공한 샘플들이 포함됩니다.
### Training Method
* This model was fine-tuned on the "mistralai/Mistral-7B-v0.1" base model using a full parameter tuning method with SFT (Supervised Fine-Tuning).
* 이 모델은 "mistralai/Mistral-7B-v0.1" 기반 모델을 SFT를 사용하여 전체 파라미터 조정 방법으로 미세조정되었습니다.
### Hardware
* Hardware: Utilized two A100 (80G*2EA) GPUs for training.
* Training Factors: This model was fine-tuned with SFT, using the HuggingFace SFTtrainer and applied fsdp.
* 이 모델은 SFT를 사용하여 HuggingFace SFTtrainer와 fsdp를 적용하여 미세조정되었습니다. |
mradermacher/StableBeluga2-i1-GGUF | mradermacher | 2024-05-06T04:53:57Z | 729 | 0 | transformers | [
"transformers",
"gguf",
"en",
"dataset:conceptofmind/cot_submix_original",
"dataset:conceptofmind/flan2021_submix_original",
"dataset:conceptofmind/t0_submix_original",
"dataset:conceptofmind/niv2_submix_original",
"base_model:stabilityai/StableBeluga2",
"endpoints_compatible",
"region:us"
] | null | 2024-04-15T12:50:41Z | ---
base_model: stabilityai/StableBeluga2
datasets:
- conceptofmind/cot_submix_original
- conceptofmind/flan2021_submix_original
- conceptofmind/t0_submix_original
- conceptofmind/niv2_submix_original
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/stabilityai/StableBeluga2
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/StableBeluga2-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-IQ1_S.gguf) | i1-IQ1_S | 14.6 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-IQ1_M.gguf) | i1-IQ1_M | 16.0 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 18.4 | |
| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-IQ2_XS.gguf) | i1-IQ2_XS | 20.4 | |
| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-IQ2_S.gguf) | i1-IQ2_S | 21.5 | |
| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-IQ2_M.gguf) | i1-IQ2_M | 23.3 | |
| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-Q2_K.gguf) | i1-Q2_K | 25.6 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 26.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-IQ3_XS.gguf) | i1-IQ3_XS | 28.4 | |
| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-IQ3_S.gguf) | i1-IQ3_S | 30.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-Q3_K_S.gguf) | i1-Q3_K_S | 30.0 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-IQ3_M.gguf) | i1-IQ3_M | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-Q3_K_M.gguf) | i1-Q3_K_M | 33.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-Q3_K_L.gguf) | i1-Q3_K_L | 36.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-IQ4_XS.gguf) | i1-IQ4_XS | 36.9 | |
| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-Q4_0.gguf) | i1-Q4_0 | 39.1 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-Q4_K_S.gguf) | i1-Q4_K_S | 39.3 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-Q4_K_M.gguf) | i1-Q4_K_M | 41.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-Q5_K_S.gguf) | i1-Q5_K_S | 47.6 | |
| [GGUF](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-Q5_K_M.gguf) | i1-Q5_K_M | 48.9 | |
| [PART 1](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/StableBeluga2-i1-GGUF/resolve/main/StableBeluga2.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 56.7 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
saishf/Llama4Some-SOVL-4x8B-L3-V1 | saishf | 2024-05-23T12:20:56Z | 729 | 2 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2401.04088",
"base_model:saishf/Ortho-SOVL-8B-L3",
"base_model:saishf/SOVLish-Maid-L3-8B",
"base_model:saishf/Merge-Mayhem-L3-V2.1",
"base_model:saishf/Merge-Mayhem-L3-V2",
"license:cc-by-nc-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-05-12T12:34:20Z | ---
license: cc-by-nc-4.0
library_name: transformers
tags:
- mergekit
- merge
base_model:
- saishf/Ortho-SOVL-8B-L3
- saishf/SOVLish-Maid-L3-8B
- saishf/Merge-Mayhem-L3-V2.1
- saishf/Merge-Mayhem-L3-V2
model-index:
- name: Llama4Some-SOVL-4x8B-L3-V1
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: 61.95
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Llama4Some-SOVL-4x8B-L3-V1
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: 79.38
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Llama4Some-SOVL-4x8B-L3-V1
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: 65.49
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Llama4Some-SOVL-4x8B-L3-V1
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: 51.48
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Llama4Some-SOVL-4x8B-L3-V1
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: 75.69
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Llama4Some-SOVL-4x8B-L3-V1
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: 66.57
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Llama4Some-SOVL-4x8B-L3-V1
name: Open LLM Leaderboard
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
This model is a merge of all of my SOVL models, in the hopes to create the most unhinged and wild model possible. But in Mixtral fashion!
It may be insane, it may be incoherent. I can't load it :3
### Merge Method
This model was merged using the [Mixture Of Experts](https://arxiv.org/abs/2401.04088) method.
### Models Merged
The following models were included in the merge:
* [saishf/Ortho-SOVL-8B-L3](https://huggingface.co/saishf/Ortho-SOVL-8B-L3)
* [saishf/SOVLish-Maid-L3-8B](https://huggingface.co/saishf/SOVLish-Maid-L3-8B)
* [saishf/Merge-Mayhem-L3-V2.1](https://huggingface.co/saishf/Merge-Mayhem-L3-V2.1)
* [saishf/Merge-Mayhem-L3-V2](https://huggingface.co/saishf/Merge-Mayhem-L3-V2)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
base_model: saishf/Ortho-SOVL-8B-L3
gate_mode: random
dtype: bfloat16
experts:
- source_model: saishf/Ortho-SOVL-8B-L3
- source_model: saishf/SOVLish-Maid-L3-8B
- source_model: saishf/Merge-Mayhem-L3-V2.1
- source_model: saishf/Merge-Mayhem-L3-V2
```
# [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_saishf__Llama4Some-SOVL-4x8B-L3-V1)
| Metric |Value|
|---------------------------------|----:|
|Avg. |66.76|
|AI2 Reasoning Challenge (25-Shot)|61.95|
|HellaSwag (10-Shot) |79.38|
|MMLU (5-Shot) |65.49|
|TruthfulQA (0-shot) |51.48|
|Winogrande (5-shot) |75.69|
|GSM8k (5-shot) |66.57|
|
Omartificial-Intelligence-Space/Arabic-mpnet-base-all-nli-triplet | Omartificial-Intelligence-Space | 2024-06-26T20:20:45Z | 729 | 5 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"mpnet",
"mteb",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:557850",
"loss:MatryoshkaLoss",
"loss:MultipleNegativesRankingLoss",
"ar",
"dataset:Omartificial-Intelligence-Space/Arabic-NLi-Triplet",
"arxiv:1908.10084",
"arxiv:2205.13147",
"arxiv:1705.00652",
"base_model:tomaarsen/mpnet-base-all-nli-triplet",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2024-06-15T22:01:53Z | ---
language:
- ar
library_name: sentence-transformers
tags:
- mteb
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:557850
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: tomaarsen/mpnet-base-all-nli-triplet
datasets:
- Omartificial-Intelligence-Space/Arabic-NLi-Triplet
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: ذكر متوازن بعناية يقف على قدم واحدة بالقرب من منطقة شاطئ المحيط النظيفة
sentences:
- رجل يقدم عرضاً
- هناك رجل بالخارج قرب الشاطئ
- رجل يجلس على أريكه
- source_sentence: رجل يقفز إلى سريره القذر
sentences:
- السرير قذر.
- رجل يضحك أثناء غسيل الملابس
- الرجل على القمر
- source_sentence: الفتيات بالخارج
sentences:
- امرأة تلف الخيط إلى كرات بجانب كومة من الكرات
- فتيان يركبان في جولة متعة
- >-
ثلاث فتيات يقفون سوية في غرفة واحدة تستمع وواحدة تكتب على الحائط والثالثة
تتحدث إليهن
- source_sentence: الرجل يرتدي قميصاً أزرق.
sentences:
- >-
رجل يرتدي قميصاً أزرق يميل إلى الجدار بجانب الطريق مع شاحنة زرقاء وسيارة
حمراء مع الماء في الخلفية.
- كتاب القصص مفتوح
- رجل يرتدي قميص أسود يعزف على الجيتار.
- source_sentence: يجلس شاب ذو شعر أشقر على الحائط يقرأ جريدة بينما تمر امرأة وفتاة شابة.
sentences:
- ذكر شاب ينظر إلى جريدة بينما تمر إمرأتان بجانبه
- رجل يستلقي على وجهه على مقعد في الحديقة.
- الشاب نائم بينما الأم تقود ابنتها إلى الحديقة
pipeline_tag: sentence-similarity
model-index:
- name: Omartificial-Intelligence-Space/Arabic-mpnet-base-all-nli-triplet
results:
- dataset:
config: default
name: MTEB BIOSSES (default)
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
split: test
type: mteb/biosses-sts
metrics:
- type: cosine_pearson
value: 69.84925402371587
- type: cosine_spearman
value: 67.12261377163864
- type: euclidean_pearson
value: 68.77931734192
- type: euclidean_spearman
value: 67.10454107068325
- type: main_score
value: 67.12261377163864
- type: manhattan_pearson
value: 69.39988076793398
- type: manhattan_spearman
value: 67.68708446481159
task:
type: STS
- dataset:
config: default
name: MTEB SICK-R (default)
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
split: test
type: mteb/sickr-sts
metrics:
- type: cosine_pearson
value: 72.71925116055804
- type: cosine_spearman
value: 68.9386835022992
- type: euclidean_pearson
value: 71.00708266525079
- type: euclidean_spearman
value: 69.07087906196487
- type: main_score
value: 68.9386835022992
- type: manhattan_pearson
value: 70.95266060047263
- type: manhattan_spearman
value: 69.11051988196195
task:
type: STS
- dataset:
config: default
name: MTEB STS12 (default)
revision: a0d554a64d88156834ff5ae9920b964011b16384
split: test
type: mteb/sts12-sts
metrics:
- type: cosine_pearson
value: 71.67274455692545
- type: cosine_spearman
value: 68.71669873972587
- type: euclidean_pearson
value: 69.79037485042406
- type: euclidean_spearman
value: 68.80550150752252
- type: main_score
value: 68.71669873972587
- type: manhattan_pearson
value: 69.7571283034187
- type: manhattan_spearman
value: 68.58306466019968
task:
type: STS
- dataset:
config: default
name: MTEB STS13 (default)
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
split: test
type: mteb/sts13-sts
metrics:
- type: cosine_pearson
value: 54.172888286882504
- type: cosine_spearman
value: 56.04247097489131
- type: euclidean_pearson
value: 57.88587934777827
- type: euclidean_spearman
value: 57.6139294630564
- type: main_score
value: 56.04247097489131
- type: manhattan_pearson
value: 57.616116618991185
- type: manhattan_spearman
value: 57.23150380799801
task:
type: STS
- dataset:
config: default
name: MTEB STS14 (default)
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
split: test
type: mteb/sts14-sts
metrics:
- type: cosine_pearson
value: 59.58820914531488
- type: cosine_spearman
value: 58.80575077741524
- type: euclidean_pearson
value: 61.1884427988923
- type: euclidean_spearman
value: 60.661625936116124
- type: main_score
value: 58.80575077741524
- type: manhattan_pearson
value: 60.800157410891885
- type: manhattan_spearman
value: 60.29447727072491
task:
type: STS
- dataset:
config: default
name: MTEB STS15 (default)
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
split: test
type: mteb/sts15-sts
metrics:
- type: cosine_pearson
value: 73.45220638967554
- type: cosine_spearman
value: 73.74453589715445
- type: euclidean_pearson
value: 73.8887071337604
- type: euclidean_spearman
value: 73.51752094057372
- type: main_score
value: 73.74453589715445
- type: manhattan_pearson
value: 73.45961523235827
- type: manhattan_spearman
value: 73.07675481848841
task:
type: STS
- dataset:
config: default
name: MTEB STS16 (default)
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
split: test
type: mteb/sts16-sts
metrics:
- type: cosine_pearson
value: 66.84132105540075
- type: cosine_spearman
value: 68.24735989887876
- type: euclidean_pearson
value: 68.2712231484699
- type: euclidean_spearman
value: 68.02365271737838
- type: main_score
value: 68.24735989887876
- type: manhattan_pearson
value: 67.87379902773417
- type: manhattan_spearman
value: 67.65342499070456
task:
type: STS
- dataset:
config: ar-ar
name: MTEB STS17 (ar-ar)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cosine_pearson
value: 79.2987412566616
- type: cosine_spearman
value: 79.93275889323859
- type: euclidean_pearson
value: 77.90301430319637
- type: euclidean_spearman
value: 79.12169562085792
- type: main_score
value: 79.93275889323859
- type: manhattan_pearson
value: 77.93298637610417
- type: manhattan_spearman
value: 79.38516109229111
task:
type: STS
- dataset:
config: ar
name: MTEB STS22 (ar)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 46.955019830396445
- type: cosine_spearman
value: 52.44226852669887
- type: euclidean_pearson
value: 42.80891863181744
- type: euclidean_spearman
value: 53.175461247693704
- type: main_score
value: 52.44226852669887
- type: manhattan_pearson
value: 42.97005510727849
- type: manhattan_spearman
value: 53.158087426369825
task:
type: STS
- dataset:
config: default
name: MTEB STSBenchmark (default)
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
split: test
type: mteb/stsbenchmark-sts
metrics:
- type: cosine_pearson
value: 66.99025999216197
- type: cosine_spearman
value: 67.56341643518167
- type: euclidean_pearson
value: 69.73441598964332
- type: euclidean_spearman
value: 68.72541136876826
- type: main_score
value: 67.56341643518167
- type: manhattan_pearson
value: 69.43492004000674
- type: manhattan_spearman
value: 68.39614969063062
task:
type: STS
- dataset:
config: default
name: MTEB SummEval (default)
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
split: test
type: mteb/summeval
metrics:
- type: cosine_pearson
value: 30.13248188083236
- type: cosine_spearman
value: 28.78575545661001
- type: dot_pearson
value: 30.934754821379464
- type: dot_spearman
value: 29.730792596057093
- type: main_score
value: 28.78575545661001
- type: pearson
value: 30.13248188083236
- type: spearman
value: 28.78575545661001
task:
type: Summarization
- name: SentenceTransformer based on tomaarsen/mpnet-base-all-nli-triplet
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 768
type: sts-test-768
metrics:
- type: pearson_cosine
value: 0.66986244175229
name: Pearson Cosine
- type: spearman_cosine
value: 0.675651628513557
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6943200977280434
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6839707658313092
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6973190148612566
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6872926092972673
name: Spearman Euclidean
- type: pearson_dot
value: 0.5534197296097646
name: Pearson Dot
- type: spearman_dot
value: 0.5421965591416092
name: Spearman Dot
- type: pearson_max
value: 0.6973190148612566
name: Pearson Max
- type: spearman_max
value: 0.6872926092972673
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 512
type: sts-test-512
metrics:
- type: pearson_cosine
value: 0.6628171358537143
name: Pearson Cosine
- type: spearman_cosine
value: 0.670314701212355
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6916567677127377
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6815748132707206
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6948756461188812
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.685329042213794
name: Spearman Euclidean
- type: pearson_dot
value: 0.5229142840207227
name: Pearson Dot
- type: spearman_dot
value: 0.5113740757424073
name: Spearman Dot
- type: pearson_max
value: 0.6948756461188812
name: Pearson Max
- type: spearman_max
value: 0.685329042213794
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 256
type: sts-test-256
metrics:
- type: pearson_cosine
value: 0.6368313837029833
name: Pearson Cosine
- type: spearman_cosine
value: 0.6512526280069127
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6832129716443456
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.674638334774044
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6843664039671002
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6760040651639672
name: Spearman Euclidean
- type: pearson_dot
value: 0.4266095536126992
name: Pearson Dot
- type: spearman_dot
value: 0.4179376458107888
name: Spearman Dot
- type: pearson_max
value: 0.6843664039671002
name: Pearson Max
- type: spearman_max
value: 0.6760040651639672
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 128
type: sts-test-128
metrics:
- type: pearson_cosine
value: 0.6147896744901056
name: Pearson Cosine
- type: spearman_cosine
value: 0.6354730852658397
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6730782159165468
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6652649799789521
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.676407799774529
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6691409653459247
name: Spearman Euclidean
- type: pearson_dot
value: 0.35130869784942953
name: Pearson Dot
- type: spearman_dot
value: 0.3445374275232203
name: Spearman Dot
- type: pearson_max
value: 0.676407799774529
name: Pearson Max
- type: spearman_max
value: 0.6691409653459247
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 64
type: sts-test-64
metrics:
- type: pearson_cosine
value: 0.5789158725954748
name: Pearson Cosine
- type: spearman_cosine
value: 0.6081197115891086
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6578631744829946
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6518503436513217
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6629734628760299
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6570510967281272
name: Spearman Euclidean
- type: pearson_dot
value: 0.24034366392620327
name: Pearson Dot
- type: spearman_dot
value: 0.2331392769925126
name: Spearman Dot
- type: pearson_max
value: 0.6629734628760299
name: Pearson Max
- type: spearman_max
value: 0.6570510967281272
name: Spearman Max
---
# SentenceTransformer based on tomaarsen/mpnet-base-all-nli-triplet
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [tomaarsen/mpnet-base-all-nli-triplet](https://huggingface.co/tomaarsen/mpnet-base-all-nli-triplet) on the Omartificial-Intelligence-Space/arabic-n_li-triplet dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [tomaarsen/mpnet-base-all-nli-triplet](https://huggingface.co/tomaarsen/mpnet-base-all-nli-triplet) <!-- at revision e88732e5620f3592bf6566604be9a6a5cad814ec -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- Omartificial-Intelligence-Space/arabic-n_li-triplet
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("Omartificial-Intelligence-Space/mpnet-base-all-nli-triplet-Arabic-mpnet_base")
# Run inference
sentences = [
'يجلس شاب ذو شعر أشقر على الحائط يقرأ جريدة بينما تمر امرأة وفتاة شابة.',
'ذكر شاب ينظر إلى جريدة بينما تمر إمرأتان بجانبه',
'الشاب نائم بينما الأم تقود ابنتها إلى الحديقة',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts-test-768`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.6699 |
| **spearman_cosine** | **0.6757** |
| pearson_manhattan | 0.6943 |
| spearman_manhattan | 0.684 |
| pearson_euclidean | 0.6973 |
| spearman_euclidean | 0.6873 |
| pearson_dot | 0.5534 |
| spearman_dot | 0.5422 |
| pearson_max | 0.6973 |
| spearman_max | 0.6873 |
#### Semantic Similarity
* Dataset: `sts-test-512`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.6628 |
| **spearman_cosine** | **0.6703** |
| pearson_manhattan | 0.6917 |
| spearman_manhattan | 0.6816 |
| pearson_euclidean | 0.6949 |
| spearman_euclidean | 0.6853 |
| pearson_dot | 0.5229 |
| spearman_dot | 0.5114 |
| pearson_max | 0.6949 |
| spearman_max | 0.6853 |
#### Semantic Similarity
* Dataset: `sts-test-256`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.6368 |
| **spearman_cosine** | **0.6513** |
| pearson_manhattan | 0.6832 |
| spearman_manhattan | 0.6746 |
| pearson_euclidean | 0.6844 |
| spearman_euclidean | 0.676 |
| pearson_dot | 0.4266 |
| spearman_dot | 0.4179 |
| pearson_max | 0.6844 |
| spearman_max | 0.676 |
#### Semantic Similarity
* Dataset: `sts-test-128`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.6148 |
| **spearman_cosine** | **0.6355** |
| pearson_manhattan | 0.6731 |
| spearman_manhattan | 0.6653 |
| pearson_euclidean | 0.6764 |
| spearman_euclidean | 0.6691 |
| pearson_dot | 0.3513 |
| spearman_dot | 0.3445 |
| pearson_max | 0.6764 |
| spearman_max | 0.6691 |
#### Semantic Similarity
* Dataset: `sts-test-64`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.5789 |
| **spearman_cosine** | **0.6081** |
| pearson_manhattan | 0.6579 |
| spearman_manhattan | 0.6519 |
| pearson_euclidean | 0.663 |
| spearman_euclidean | 0.6571 |
| pearson_dot | 0.2403 |
| spearman_dot | 0.2331 |
| pearson_max | 0.663 |
| spearman_max | 0.6571 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Omartificial-Intelligence-Space/arabic-n_li-triplet
* Dataset: Omartificial-Intelligence-Space/arabic-n_li-triplet
* Size: 557,850 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 12 tokens</li><li>mean: 23.93 tokens</li><li>max: 155 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 29.62 tokens</li><li>max: 117 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 33.95 tokens</li><li>max: 149 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:------------------------------------------------------------|:--------------------------------------------|:------------------------------------|
| <code>شخص على حصان يقفز فوق طائرة معطلة</code> | <code>شخص في الهواء الطلق، على حصان.</code> | <code>شخص في مطعم، يطلب عجة.</code> |
| <code>أطفال يبتسمون و يلوحون للكاميرا</code> | <code>هناك أطفال حاضرون</code> | <code>الاطفال يتجهمون</code> |
| <code>صبي يقفز على لوح التزلج في منتصف الجسر الأحمر.</code> | <code>الفتى يقوم بخدعة التزلج</code> | <code>الصبي يتزلج على الرصيف</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Evaluation Dataset
#### Omartificial-Intelligence-Space/arabic-n_li-triplet
* Dataset: Omartificial-Intelligence-Space/arabic-n_li-triplet
* Size: 6,584 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 12 tokens</li><li>mean: 49.5 tokens</li><li>max: 246 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 23.66 tokens</li><li>max: 103 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 25.33 tokens</li><li>max: 82 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------|:---------------------------------------------------|
| <code>امرأتان يتعانقان بينما يحملان حزمة</code> | <code>إمرأتان يحملان حزمة</code> | <code>الرجال يتشاجرون خارج مطعم</code> |
| <code>طفلين صغيرين يرتديان قميصاً أزرق، أحدهما يرتدي الرقم 9 والآخر يرتدي الرقم 2 يقفان على خطوات خشبية في الحمام ويغسلان أيديهما في المغسلة.</code> | <code>طفلين يرتديان قميصاً مرقماً يغسلون أيديهم</code> | <code>طفلين يرتديان سترة يذهبان إلى المدرسة</code> |
| <code>رجل يبيع الدونات لعميل خلال معرض عالمي أقيم في مدينة أنجليس</code> | <code>رجل يبيع الدونات لعميل</code> | <code>امرأة تشرب قهوتها في مقهى صغير</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | sts-test-128_spearman_cosine | sts-test-256_spearman_cosine | sts-test-512_spearman_cosine | sts-test-64_spearman_cosine | sts-test-768_spearman_cosine |
|:------:|:-----:|:-------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|
| 0.0229 | 200 | 21.5318 | - | - | - | - | - |
| 0.0459 | 400 | 17.2344 | - | - | - | - | - |
| 0.0688 | 600 | 15.393 | - | - | - | - | - |
| 0.0918 | 800 | 13.7897 | - | - | - | - | - |
| 0.1147 | 1000 | 13.534 | - | - | - | - | - |
| 0.1377 | 1200 | 12.2683 | - | - | - | - | - |
| 0.1606 | 1400 | 10.9271 | - | - | - | - | - |
| 0.1835 | 1600 | 11.071 | - | - | - | - | - |
| 0.2065 | 1800 | 10.0153 | - | - | - | - | - |
| 0.2294 | 2000 | 9.8463 | - | - | - | - | - |
| 0.2524 | 2200 | 10.0194 | - | - | - | - | - |
| 0.2753 | 2400 | 9.8371 | - | - | - | - | - |
| 0.2983 | 2600 | 9.6315 | - | - | - | - | - |
| 0.3212 | 2800 | 8.9858 | - | - | - | - | - |
| 0.3442 | 3000 | 9.1876 | - | - | - | - | - |
| 0.3671 | 3200 | 8.8028 | - | - | - | - | - |
| 0.3900 | 3400 | 8.6075 | - | - | - | - | - |
| 0.4130 | 3600 | 8.4285 | - | - | - | - | - |
| 0.4359 | 3800 | 8.1258 | - | - | - | - | - |
| 0.4589 | 4000 | 8.2508 | - | - | - | - | - |
| 0.4818 | 4200 | 7.8037 | - | - | - | - | - |
| 0.5048 | 4400 | 7.7133 | - | - | - | - | - |
| 0.5277 | 4600 | 7.5006 | - | - | - | - | - |
| 0.5506 | 4800 | 7.7025 | - | - | - | - | - |
| 0.5736 | 5000 | 7.7593 | - | - | - | - | - |
| 0.5965 | 5200 | 7.6305 | - | - | - | - | - |
| 0.6195 | 5400 | 7.7502 | - | - | - | - | - |
| 0.6424 | 5600 | 7.5624 | - | - | - | - | - |
| 0.6654 | 5800 | 7.5287 | - | - | - | - | - |
| 0.6883 | 6000 | 7.4261 | - | - | - | - | - |
| 0.7113 | 6200 | 7.239 | - | - | - | - | - |
| 0.7342 | 6400 | 7.1631 | - | - | - | - | - |
| 0.7571 | 6600 | 7.6865 | - | - | - | - | - |
| 0.7801 | 6800 | 7.6124 | - | - | - | - | - |
| 0.8030 | 7000 | 6.9936 | - | - | - | - | - |
| 0.8260 | 7200 | 6.7331 | - | - | - | - | - |
| 0.8489 | 7400 | 6.4542 | - | - | - | - | - |
| 0.8719 | 7600 | 6.1994 | - | - | - | - | - |
| 0.8948 | 7800 | 5.9798 | - | - | - | - | - |
| 0.9177 | 8000 | 5.7808 | - | - | - | - | - |
| 0.9407 | 8200 | 5.6952 | - | - | - | - | - |
| 0.9636 | 8400 | 5.5082 | - | - | - | - | - |
| 0.9866 | 8600 | 5.4421 | - | - | - | - | - |
| 1.0095 | 8800 | 3.0309 | - | - | - | - | - |
| 1.0026 | 9000 | 1.1835 | - | - | - | - | - |
| 1.0256 | 9200 | 8.1196 | - | - | - | - | - |
| 1.0485 | 9400 | 8.0326 | - | - | - | - | - |
| 1.0715 | 9600 | 8.5028 | - | - | - | - | - |
| 1.0944 | 9800 | 7.6923 | - | - | - | - | - |
| 1.1174 | 10000 | 8.029 | - | - | - | - | - |
| 1.1403 | 10200 | 7.5052 | - | - | - | - | - |
| 1.1632 | 10400 | 7.1177 | - | - | - | - | - |
| 1.1862 | 10600 | 6.9594 | - | - | - | - | - |
| 1.2091 | 10800 | 6.6662 | - | - | - | - | - |
| 1.2321 | 11000 | 6.6903 | - | - | - | - | - |
| 1.2550 | 11200 | 6.9523 | - | - | - | - | - |
| 1.2780 | 11400 | 6.676 | - | - | - | - | - |
| 1.3009 | 11600 | 6.7141 | - | - | - | - | - |
| 1.3238 | 11800 | 6.568 | - | - | - | - | - |
| 1.3468 | 12000 | 6.8938 | - | - | - | - | - |
| 1.3697 | 12200 | 6.3745 | - | - | - | - | - |
| 1.3927 | 12400 | 6.2513 | - | - | - | - | - |
| 1.4156 | 12600 | 6.2589 | - | - | - | - | - |
| 1.4386 | 12800 | 6.1388 | - | - | - | - | - |
| 1.4615 | 13000 | 6.1835 | - | - | - | - | - |
| 1.4845 | 13200 | 5.9004 | - | - | - | - | - |
| 1.5074 | 13400 | 5.7891 | - | - | - | - | - |
| 1.5303 | 13600 | 5.6184 | - | - | - | - | - |
| 1.5533 | 13800 | 5.9762 | - | - | - | - | - |
| 1.5762 | 14000 | 5.9737 | - | - | - | - | - |
| 1.5992 | 14200 | 5.8563 | - | - | - | - | - |
| 1.6221 | 14400 | 5.8904 | - | - | - | - | - |
| 1.6451 | 14600 | 5.8484 | - | - | - | - | - |
| 1.6680 | 14800 | 5.8906 | - | - | - | - | - |
| 1.6909 | 15000 | 5.7613 | - | - | - | - | - |
| 1.7139 | 15200 | 5.5744 | - | - | - | - | - |
| 1.7368 | 15400 | 5.6569 | - | - | - | - | - |
| 1.7598 | 15600 | 5.7439 | - | - | - | - | - |
| 1.7827 | 15800 | 5.5593 | - | - | - | - | - |
| 1.8057 | 16000 | 5.2935 | - | - | - | - | - |
| 1.8286 | 16200 | 5.088 | - | - | - | - | - |
| 1.8516 | 16400 | 5.0167 | - | - | - | - | - |
| 1.8745 | 16600 | 4.84 | - | - | - | - | - |
| 1.8974 | 16800 | 4.6731 | - | - | - | - | - |
| 1.9204 | 17000 | 4.6404 | - | - | - | - | - |
| 1.9433 | 17200 | 4.6413 | - | - | - | - | - |
| 1.9663 | 17400 | 4.4495 | - | - | - | - | - |
| 1.9892 | 17600 | 4.4262 | - | - | - | - | - |
| 2.0122 | 17800 | 2.01 | - | - | - | - | - |
| 2.0053 | 18000 | 1.8418 | - | - | - | - | - |
| 2.0282 | 18200 | 6.2714 | - | - | - | - | - |
| 2.0512 | 18400 | 6.1742 | - | - | - | - | - |
| 2.0741 | 18600 | 6.5996 | - | - | - | - | - |
| 2.0971 | 18800 | 6.0907 | - | - | - | - | - |
| 2.1200 | 19000 | 6.2418 | - | - | - | - | - |
| 2.1429 | 19200 | 5.7817 | - | - | - | - | - |
| 2.1659 | 19400 | 5.7073 | - | - | - | - | - |
| 2.1888 | 19600 | 5.2645 | - | - | - | - | - |
| 2.2118 | 19800 | 5.3451 | - | - | - | - | - |
| 2.2347 | 20000 | 5.2453 | - | - | - | - | - |
| 2.2577 | 20200 | 5.6161 | - | - | - | - | - |
| 2.2806 | 20400 | 5.2289 | - | - | - | - | - |
| 2.3035 | 20600 | 5.3888 | - | - | - | - | - |
| 2.3265 | 20800 | 5.2483 | - | - | - | - | - |
| 2.3494 | 21000 | 5.5791 | - | - | - | - | - |
| 2.3724 | 21200 | 5.1643 | - | - | - | - | - |
| 2.3953 | 21400 | 5.1231 | - | - | - | - | - |
| 2.4183 | 21600 | 5.1055 | - | - | - | - | - |
| 2.4412 | 21800 | 5.1778 | - | - | - | - | - |
| 2.4642 | 22000 | 5.0466 | - | - | - | - | - |
| 2.4871 | 22200 | 4.8321 | - | - | - | - | - |
| 2.5100 | 22400 | 4.7056 | - | - | - | - | - |
| 2.5330 | 22600 | 4.6858 | - | - | - | - | - |
| 2.5559 | 22800 | 4.9189 | - | - | - | - | - |
| 2.5789 | 23000 | 4.912 | - | - | - | - | - |
| 2.6018 | 23200 | 4.8289 | - | - | - | - | - |
| 2.6248 | 23400 | 4.8959 | - | - | - | - | - |
| 2.6477 | 23600 | 4.9441 | - | - | - | - | - |
| 2.6706 | 23800 | 4.9334 | - | - | - | - | - |
| 2.6936 | 24000 | 4.8328 | - | - | - | - | - |
| 2.7165 | 24200 | 4.601 | - | - | - | - | - |
| 2.7395 | 24400 | 4.834 | - | - | - | - | - |
| 2.7624 | 24600 | 5.152 | - | - | - | - | - |
| 2.7854 | 24800 | 4.9232 | - | - | - | - | - |
| 2.8083 | 25000 | 4.6556 | - | - | - | - | - |
| 2.8312 | 25200 | 4.6229 | - | - | - | - | - |
| 2.8542 | 25400 | 4.5768 | - | - | - | - | - |
| 2.8771 | 25600 | 4.3619 | - | - | - | - | - |
| 2.9001 | 25800 | 4.3608 | - | - | - | - | - |
| 2.9230 | 26000 | 4.2834 | - | - | - | - | - |
| 2.9403 | 26151 | - | 0.6355 | 0.6513 | 0.6703 | 0.6081 | 0.6757 |
</details>
### Framework Versions
- Python: 3.9.18
- Sentence Transformers: 3.0.1
- Transformers: 4.40.0
- PyTorch: 2.2.2+cu121
- Accelerate: 0.26.1
- Datasets: 2.19.0
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |
toiletwater/DialoGPT-medium-ironman | toiletwater | 2021-11-27T03:00:25Z | 728 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
tags:
- conversational
---
# Tony Stark DialoGPT Model |
s-nlp/Mutual_Implication_Score | s-nlp | 2022-07-11T12:36:45Z | 728 | 2 | transformers | [
"transformers",
"pytorch",
"roberta",
"paraphrase detection",
"paraphrase",
"paraphrasing",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-04-12T10:58:35Z | ---
language:
- en
tags:
- paraphrase detection
- paraphrase
- paraphrasing
licenses:
- cc-by-nc-sa
---
## Model overview
Mutual Implication Score is a symmetric measure of text semantic similarity
based on a RoBERTA model pretrained for natural language inference
and fine-tuned on a paraphrase detection dataset.
The code for inference and evaluation of the model is available [here](https://github.com/skoltech-nlp/mutual_implication_score).
This measure is **particularly useful for paraphrase detection**, but can also be applied to other semantic similarity tasks, such as content similarity scoring in text style transfer.
## How to use
The following snippet illustrates code usage:
```python
!pip install mutual-implication-score
from mutual_implication_score import MIS
mis = MIS(device='cpu')#cuda:0 for using cuda with certain index
source_texts = ['I want to leave this room',
'Hello world, my name is Nick']
paraphrases = ['I want to go out of this room',
'Hello world, my surname is Petrov']
scores = mis.compute(source_texts, paraphrases)
print(scores)
# expected output: [0.9748, 0.0545]
```
## Model details
We slightly modify the [RoBERTa-Large NLI](https://huggingface.co/ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli) model architecture (see the scheme below) and fine-tune it with [QQP](https://www.kaggle.com/c/quora-question-pairs) paraphrase dataset.
## Performance on Text Style Transfer and Paraphrase Detection tasks
This measure was developed in terms of large scale comparison of different measures on text style transfer and paraphrase datasets.
<img src="https://huggingface.co/SkolkovoInstitute/Mutual_Implication_Score/raw/main/corr_main.jpg" alt="drawing" width="1000"/>
The scheme above shows the correlations of measures of different classes with human judgments on paraphrase and text style transfer datasets. The text above each dataset indicates the best-performing measure. The rightmost columns show the mean performance of measures across the datasets.
MIS outperforms all measures on the paraphrase detection task and performs on par with top measures on the text style transfer task.
To learn more, refer to our article: [A large-scale computational study of content preservation measures for text style transfer and paraphrase generation](https://aclanthology.org/2022.acl-srw.23/)
## Citations
If you find this repository helpful, feel free to cite our publication:
```
@inproceedings{babakov-etal-2022-large,
title = "A large-scale computational study of content preservation measures for text style transfer and paraphrase generation",
author = "Babakov, Nikolay and
Dale, David and
Logacheva, Varvara and
Panchenko, Alexander",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-srw.23",
pages = "300--321",
abstract = "Text style transfer and paraphrasing of texts are actively growing areas of NLP, dozens of methods for solving these tasks have been recently introduced. In both tasks, the system is supposed to generate a text which should be semantically similar to the input text. Therefore, these tasks are dependent on methods of measuring textual semantic similarity. However, it is still unclear which measures are the best to automatically evaluate content preservation between original and generated text. According to our observations, many researchers still use BLEU-like measures, while there exist more advanced measures including neural-based that significantly outperform classic approaches. The current problem is the lack of a thorough evaluation of the available measures. We close this gap by conducting a large-scale computational study by comparing 57 measures based on different principles on 19 annotated datasets. We show that measures based on cross-encoder models outperform alternative approaches in almost all cases.We also introduce the Mutual Implication Score (MIS), a measure that uses the idea of paraphrasing as a bidirectional entailment and outperforms all other measures on the paraphrase detection task and performs on par with the best measures in the text style transfer task.",
}
```
## Licensing Information
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].
[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]
[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/
[cc-by-nc-sa-image]: https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png
|
zh-tw-llm-dv/sample-pythia-70m-dialogue | zh-tw-llm-dv | 2023-05-21T18:21:00Z | 728 | 0 | transformers | [
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2023-05-21T18:20:34Z | Entry not found |
umd-zhou-lab/recycled-wizardlm-7b-v2.0 | umd-zhou-lab | 2023-10-22T16:10:53Z | 728 | 2 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"arxiv:2310.11716",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2023-10-22T14:44:16Z | ---
license: llama2
language:
- en
---
# Model Card for umd-zhou-lab/recycled-wizardlm-7b-v2.0
<!-- Provide a quick summary of what the model is/does. -->
This model is trained by fine-tuning llama-2 with recycled WizardLM(70k) data V2.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** UMD Tianyi Zhou Lab
- **Model type:** An auto-regressive language model based on the transformer architecture
- **License:** Llama 2 Community License Agreement
- **Finetuned from model:** [meta-llama/Llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b)
### Model Sources
<!-- Provide the basic links for the model. -->
- **GitHub:** [Reflection-Tuning](https://github.com/tianyi-lab/Reflection_Tuning)
- **Paper:** [Reflection-Tuning: Data Recycling Improves LLM Instruction-Tuning](https://arxiv.org/abs/2310.11716)
- **Data:** Coming soon
## Uses
The primary use of this model is research on large language models and chatbots.
The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.
## Training
We use the prompt from [FastChat](https://github.com/lm-sys/FastChat):
```
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Hi ASSISTANT: Hello.</s>USER: Who are you? ASSISTANT: I am ...</s>......
```
| Hyperparameter | Global Batch Size | Learning rate | Epochs | Max length | Weight decay | Warmup Rate |
| --- | ---: | ---: | ---: | ---: | ---: | ---: |
| Recycled Models (7B) | 128 | 2e-5 | 3 | 2048 | 0 | 0.03 |
## Performance
The following table provides a comparison between our recycled models (V2) and baseline models on the AlpacaEval Leaderboard and Huggingface Open LLM Leaderboard. <br>
The V2 Recycled Alpaca Data and WizardLM data, and the corresponding paper will be released soon.
| | **AlpacaEval** || **Avg** | **ARC** | **HellaSwag** | **MMLU** | **TruthfulQA** || **Model**|
|--------------------------|:--------------:|:-:|:-----------:|:-------:|:-------------:|:-------:|:--------------:|:-:|:-:|
| **Alpaca 7B** | 26.46 || 50.21 | 42.65 | 76.91 | 41.73 | 39.55 ||/|
| **Recycled Alpaca 7B V2.0** | 79.58 || 56.05 | 54.01 | 78.07 | 46.69 | 45.41 ||[[hf-Link]](https://huggingface.co/umd-zhou-lab/recycled-alpaca-7b-v2.0)|
|||||||||||
| **WizardLM 7B** | 67.64 || 54.18 | 51.60 | 77.70 | 42.70 | 44.70 ||/|
| **Recycled WizardLM 7B V2.0** | 83.48 || 56.79 | 54.78 | 77.86 | 45.63 | 48.91 ||[[hf-Link]](https://huggingface.co/umd-zhou-lab/recycled-wizardlm-7b-v2.0)|
|||||||||
## Citation
Please consider citing our paper if you think our codes, data, or models are useful. Thank you!
```
@misc{li2023reflectiontuning,
title={Reflection-Tuning: Data Recycling Improves LLM Instruction-Tuning},
author={Ming Li and Lichang Chen and Jiuhai Chen and Shwai He and Heng Huang and Jiuxiang Gu and Tianyi Zhou},
year={2023},
eprint={2310.11716},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
AIGym/deepseek-coder-1.3b-chat-and-function-calling | AIGym | 2024-03-04T17:17:04Z | 728 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"finetuned",
"conversational",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-01-04T23:30:52Z | ---
license: apache-2.0
tags:
- finetuned
pipeline_tag: text-generation
model-index:
- name: deepseek-coder-1.3b-chat-and-function-calling
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: 26.28
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AIGym/deepseek-coder-1.3b-chat-and-function-calling
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: 39.27
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AIGym/deepseek-coder-1.3b-chat-and-function-calling
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: 26.92
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AIGym/deepseek-coder-1.3b-chat-and-function-calling
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: 43.37
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AIGym/deepseek-coder-1.3b-chat-and-function-calling
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: 51.7
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AIGym/deepseek-coder-1.3b-chat-and-function-calling
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: 3.41
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AIGym/deepseek-coder-1.3b-chat-and-function-calling
name: Open LLM Leaderboard
---
# deepseek-coder-1.3b-chat-and-function-calling
It was created by starting with the deepseek-coder-1.3b and training it on the open assistant dataset then training yhat on function calling. We have attached the wandb report in pdf form to view the training run at a glance.
# Reson
This model was fine tuned to allow it to work with the openai syntask and will return function when apperate.
# Templete
Us the following templete when interacting with the fine tuned model.
# Referrals
Run Pod - This is who I use to train th emodels on huggingface. If you use it we both get free crdits. - <a href="https://runpod.io?ref=kilq83n1" target="_blank" style="color: #3498db; text-decoration: none; font-weight: bold;">Visit Runpod's Website!</a>
Paypal - If you want to leave a tip, it is appecaheted. - <a href="https://paypal.me/OpenSourceTraining" target="_blank" style="color: #3498db; text-decoration: none; font-weight: bold;">Visit My Paypal!</a>
# [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_AIGym__deepseek-coder-1.3b-chat-and-function-calling)
| Metric |Value|
|---------------------------------|----:|
|Avg. |31.82|
|AI2 Reasoning Challenge (25-Shot)|26.28|
|HellaSwag (10-Shot) |39.27|
|MMLU (5-Shot) |26.92|
|TruthfulQA (0-shot) |43.37|
|Winogrande (5-shot) |51.70|
|GSM8k (5-shot) | 3.41|
|
sethuiyer/MedleyMD | sethuiyer | 2024-03-07T07:12:02Z | 728 | 0 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"moe",
"merge",
"medical",
"mergekit",
"conversational",
"en",
"dataset:medmcqa",
"dataset:cognitivecomputations/samantha-data",
"dataset:jondurbin/bagel-v0.3",
"base_model:sethuiyer/Dr_Samantha_7b_mistral",
"base_model:fblgit/UNA-TheBeagle-7b-v1",
"license:cc-by-nc-nd-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-01-15T07:35:50Z | ---
language:
- en
license: cc-by-nc-nd-4.0
library_name: transformers
tags:
- moe
- merge
- medical
- mergekit
datasets:
- medmcqa
- cognitivecomputations/samantha-data
- jondurbin/bagel-v0.3
base_model:
- sethuiyer/Dr_Samantha_7b_mistral
- fblgit/UNA-TheBeagle-7b-v1
pipeline_tag: text-generation
model-index:
- name: MedleyMD
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: 66.47
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/MedleyMD
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: 86.06
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/MedleyMD
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: 65.1
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/MedleyMD
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: 52.46
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/MedleyMD
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: 80.27
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/MedleyMD
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: 68.99
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/MedleyMD
name: Open LLM Leaderboard
---
# MedleyMD
MedleyMD is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [sethuiyer/Dr_Samantha_7b_mistral](https://huggingface.co/sethuiyer/Dr_Samantha_7b_mistral)
* [fblgit/UNA-TheBeagle-7b-v1](https://huggingface.co/fblgit/UNA-TheBeagle-7b-v1)
These models were chosen because `fblgit/UNA-TheBeagle-7b-v1` has excellent performance for a 7B parameter model and Dr.Samantha has capabilities of a medical knowledge-focused model (trained on USMLE databases and doctor-patient interactions) with the philosophical, psychological, and relational understanding, scoring 68.82% in topics related to clinical domain and psychology.
## Benchmark
On a synthetic benchmark of 35 medical diagnosis questions generated by GPT-4, GPT-4 also being an evaluator, MedleyMD scored **96.25/100**.
Nous Benchmark numbers shall be available soon.
## 🧩 Configuration
```yaml
base_model: OpenPipe/mistral-ft-optimized-1227
gate_mode: hidden
dtype: bfloat16
experts:
- source_model: sethuiyer/Dr_Samantha_7b_mistral
positive_prompts: ["differential diagnosis", "Clinical Knowledge", "Medical Genetics", "Human Aging", "Human Sexuality", "College Medicine", "Anatomy", "College Biology", "High School Biology", "Professional Medicine", "Nutrition", "High School Psychology", "Professional Psychology", "Virology"]
- source_model: fblgit/UNA-TheBeagle-7b-v1
positive_prompts: ["How do you", "Explain the concept of", "Give an overview of", "Compare and contrast between", "Provide information about", "Help me understand", "Summarize", "Make a recommendation on", "chat", "math", "reason", "mathematics", "solve", "count", "python", "javascript", "programming", "algorithm", "tell me", "assistant"]
```
## GGUF
1. [medleymd.Q4_K_M](https://huggingface.co/sethuiyer/MedleyMD-GGUF/resolve/main/medleymd.Q4_K_M.gguf) [7.2GB]
2. [medleymd.Q5_K_M](https://huggingface.co/sethuiyer/MedleyMD-GGUF/resolve/main/medleymd.Q5_K_M.gguf) [9.13GB]
## Ollama
MedleyMD can be used in ollama by running```ollama run stuehieyr/medleymd``` in your terminal.
If you have limited computing resources, check out this [video](https://www.youtube.com/watch?v=Qa1h7ygwQq8) to learn how to run it on
a Google Colab backend.
## Prompt format:
This model uses ChatML prompt format.
```
<|im_start|>system
You are Medley, a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## 💻 Usage
```python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "sethuiyer/MedleyMD"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.bfloat16, "load_in_4bit": True},
)
generation_kwargs = {
"max_new_tokens": 512,
"do_sample": True,
"temperature": 0.7,
"top_k": 50,
"top_p": 95,
}
messages = [{"role":"system", "content":"You are an helpful AI assistant. Please use </s> when you want to end the answer."},
{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, **generation_kwargs)
print(outputs[0]["generated_text"])
```
```text
A Mixture of Experts (Mixout) is a neural network architecture that combines the strengths of multiple expert networks to make a more accurate and robust prediction.
It is composed of a topmost gating network that assigns weights to each expert network based on their performance on past input samples.
The expert networks are trained independently, and the gating network learns to choose the best combination of these experts to make the final prediction.
Mixout demonstrates a stronger ability to handle complex data distributions and is more efficient in terms of training time and memory usage compared to a
traditional ensemble approach.
```
# [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_sethuiyer__MedleyMD)
| Metric |Value|
|---------------------------------|----:|
|Avg. |69.89|
|AI2 Reasoning Challenge (25-Shot)|66.47|
|HellaSwag (10-Shot) |86.06|
|MMLU (5-Shot) |65.10|
|TruthfulQA (0-shot) |52.46|
|Winogrande (5-shot) |80.27|
|GSM8k (5-shot) |68.99|
|
cloudyu/Phoenix_DPO_60B | cloudyu | 2024-02-01T05:28:31Z | 728 | 1 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"yi",
"moe",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-01-26T08:55:55Z | ---
license: other
tags:
- yi
- moe
license_name: yi-license
license_link: https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE
---
this is a DPO fine-tuned MoE model with 60B parameter.
```
DPO Trainer
TRL supports the DPO Trainer for training language models from preference data, as described in the paper Direct Preference Optimization: Your Language Model is Secretly a Reward Model by Rafailov et al., 2023.
```
GGUF format is ready at [cloudyu/Phoenix_DPO_60B_gguf](https://huggingface.co/cloudyu/Phoenix_DPO_60B_gguf)
|
saarvajanik/facebook-opt-6.7b-qcqa-ub-16-best-for-KV-cache | saarvajanik | 2024-01-28T17:13:11Z | 728 | 0 | transformers | [
"transformers",
"pytorch",
"opt",
"text-generation",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-01-28T09:17:01Z | ---
license: mit
language:
- en
library_name: transformers
---
This is a GQA version of the original model facebook/opt-125m. In this version, the original MHA architecture is preserved but instead of having a single K/V head, different K/V heads corresponding to the same group have the same mean-pooled K or V values. It has 16 groups of KV heads per layer instead of original 32 KV heads in the MHA implementation. |
NeverSleep/MiquMaid-v1-70B | NeverSleep | 2024-01-31T21:02:27Z | 728 | 17 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"not-for-all-audiences",
"nsfw",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-01-31T18:30:47Z | ---
license: cc-by-nc-4.0
tags:
- not-for-all-audiences
- nsfw
---
## MiquMaid
---
# Disclaimer:
## This model is HIGHLY EXPERIMENTAL, do not expect everything to work.
This model uses the Alpaca **prompting format**
---
Quick train to see if miqu finetuned results in good models
## Credits:
- Undi
- IkariDev
<!-- description start -->
## Description
<!-- [Recommended settings - contributed by localfultonextractor](https://files.catbox.moe/ue0tja.json) -->
This repo contains FP16 files of MiquMaid-v1-70B.
[FP16 - by IkariDev and Undi](https://huggingface.co/NeverSleep/MiquMaid-v1-70B)
<!-- [GGUF - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-GGUF)-->
<!-- [GPTQ - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-GPTQ)-->
<!-- [exl2[8bpw-8h] - by AzureBlack](https://huggingface.co/AzureBlack/Echidna-13b-v0.3-8bpw-8h-exl2)-->
<!-- [AWQ - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-AWQ)-->
<!-- [fp16 - by IkariDev+Undi95](https://huggingface.co/IkariDev/Athena-v4)-->
[GGUF - by IkariDev and Undi](https://huggingface.co/NeverSleep/MiquMaid-v1-70B-GGUF)
<!-- [OLD(GGUF - by IkariDev+Undi95)](https://huggingface.co/IkariDev/Athena-v4-GGUF)-->
## Ratings:
Note: We have permission of all users to upload their ratings, we DONT screenshot random reviews without asking if we can put them here!
No ratings yet!
If you want your rating to be here, send us a message over on DC and we'll put up a screenshot of it here. DC name is "ikaridev" and "undi".
<!-- description end -->
<!-- prompt-template start -->
### Custom format:
```
### Instruction:
{system prompt}
### Input:
{input}
### Response:
{reply}
```
## Others
Undi: If you want to support me, you can [here](https://ko-fi.com/undiai).
IkariDev: Visit my [retro/neocities style website](https://ikaridevgit.github.io/) please kek |
saishf/Kuro-Lotus-10.7B | saishf | 2024-03-04T14:33:07Z | 728 | 4 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"base_model:BlueNipples/SnowLotus-v2-10.7B",
"base_model:Himitsui/KuroMitsu-11B",
"license:cc-by-nc-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-02-03T00:46:52Z | ---
license: cc-by-nc-4.0
tags:
- mergekit
- merge
base_model:
- BlueNipples/SnowLotus-v2-10.7B
- Himitsui/KuroMitsu-11B
model-index:
- name: Kuro-Lotus-10.7B
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: 68.69
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Kuro-Lotus-10.7B
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: 87.51
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Kuro-Lotus-10.7B
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: 66.64
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Kuro-Lotus-10.7B
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: 58.27
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Kuro-Lotus-10.7B
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: 84.21
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Kuro-Lotus-10.7B
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: 66.11
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=saishf/Kuro-Lotus-10.7B
name: Open LLM Leaderboard
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [BlueNipples/SnowLotus-v2-10.7B](https://huggingface.co/BlueNipples/SnowLotus-v2-10.7B)
* [Himitsui/KuroMitsu-11B](https://huggingface.co/Himitsui/KuroMitsu-11B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: Himitsui/KuroMitsu-11B
layer_range: [0, 48]
- model: BlueNipples/SnowLotus-v2-10.7B
layer_range: [0, 48]
merge_method: slerp
base_model: Himitsui/KuroMitsu-11B
parameters:
t:
- filter: self_attn
value: [0.6, 0.7, 0.8, 0.9, 1]
- filter: mlp
value: [0.4, 0.3, 0.2, 0.1, 0]
- value: 0.5
dtype: bfloat16
```
# [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_saishf__Kuro-Lotus-10.7B)
| Metric |Value|
|---------------------------------|----:|
|Avg. |71.90|
|AI2 Reasoning Challenge (25-Shot)|68.69|
|HellaSwag (10-Shot) |87.51|
|MMLU (5-Shot) |66.64|
|TruthfulQA (0-shot) |58.27|
|Winogrande (5-shot) |84.21|
|GSM8k (5-shot) |66.11|
|
cgato/Thespis-7b-v0.2-SFTTest-3Epoch | cgato | 2024-02-09T02:28:03Z | 728 | 1 | transformers | [
"transformers",
"pytorch",
"safetensors",
"mistral",
"text-generation",
"not-for-all-audiences",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-02-03T23:48:51Z | ---
tags:
- not-for-all-audiences
license: apache-2.0
---
Test model, still cleaning the dataset up. Expect repetition.
## Thespis Test
A test model, I make no guarantees about the performance. Let me know if its any good. Seems more verbose than my previous models from my testing.
External Datasets Used:
* Pure-Dove Dataset
* Claude Multiround 30k
* OpenOrcaSlim
* Augmental Dataset
* ToxicQA
* yahoo_answers_qa
Special thanks to kubernetes-bad for providing me a cleaned chub.ai characters dataset.
## Prompt Format: Chat ( The default Ooba template and Silly Tavern Template )
```
{System Prompt}
Username: {Input}
BotName: {Response}
Username: {Input}
BotName: {Response}
```
## Ooba ( Set it to Chat, select a character and go. )
## Silly Tavern Settings ( Default )
```
|
Radu1999/Mistral-Instruct-Ukrainian-SFT | Radu1999 | 2024-03-06T08:25:50Z | 728 | 3 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-02-09T14:36:00Z | ---
license: apache-2.0
library_name: transformers
model-index:
- name: Mistral-Instruct-Ukrainian-SFT
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: 57.85
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Radu1999/Mistral-Instruct-Ukrainian-SFT
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: 83.12
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Radu1999/Mistral-Instruct-Ukrainian-SFT
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: 60.95
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Radu1999/Mistral-Instruct-Ukrainian-SFT
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: 54.14
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Radu1999/Mistral-Instruct-Ukrainian-SFT
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: 77.51
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Radu1999/Mistral-Instruct-Ukrainian-SFT
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: 39.42
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Radu1999/Mistral-Instruct-Ukrainian-SFT
name: Open LLM Leaderboard
---
# Model card for Mistral-Instruct-Ukrainian-SFT
Supervised finetuning of Mistral-7B-Instruct-v0.2 on Ukrainian datasets.
## Instruction format
In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens.
E.g.
```
text = "[INST]Відповідайте лише буквою правильної відповіді: Елементи експресіонізму наявні у творі: A. «Камінний хрест», B. «Інститутка», C. «Маруся», D. «Людина»[/INST]"
```
This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:
## Model Architecture
This instruction model is based on Mistral-7B-v0.2, a transformer model with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer
## Datasets
- [UA-SQUAD](https://huggingface.co/datasets/FIdo-AI/ua-squad/resolve/main/ua_squad_dataset.json)
- [Ukrainian StackExchange](https://huggingface.co/datasets/zeusfsx/ukrainian-stackexchange)
- [UAlpaca Dataset](https://github.com/robinhad/kruk/blob/main/data/cc-by-nc/alpaca_data_translated.json)
- [Ukrainian Subset from Belebele Dataset](https://github.com/facebookresearch/belebele)
- [Ukrainian Subset from XQA](https://github.com/thunlp/XQA)
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Radu1999/Mistral-Instruct-Ukrainian-SFT"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.bfloat16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
## Author
Radu Chivereanu
# [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_Radu1999__Mistral-Instruct-Ukrainian-SFT)
| Metric |Value|
|---------------------------------|----:|
|Avg. |62.17|
|AI2 Reasoning Challenge (25-Shot)|57.85|
|HellaSwag (10-Shot) |83.12|
|MMLU (5-Shot) |60.95|
|TruthfulQA (0-shot) |54.14|
|Winogrande (5-shot) |77.51|
|GSM8k (5-shot) |39.42|
|
jsfs11/MoEv4Config-TestWeightedTIES-7b | jsfs11 | 2024-03-04T00:35:29Z | 728 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"Kukedlc/NeuTrixOmniBe-7B-model-remix",
"PetroGPT/WestSeverus-7B-DPO",
"vanillaOVO/supermario_v4",
"base_model:Kukedlc/NeuTrixOmniBe-7B-model-remix",
"base_model:PetroGPT/WestSeverus-7B-DPO",
"base_model:vanillaOVO/supermario_v4",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-02-12T01:21:41Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- Kukedlc/NeuTrixOmniBe-7B-model-remix
- PetroGPT/WestSeverus-7B-DPO
- vanillaOVO/supermario_v4
base_model:
- Kukedlc/NeuTrixOmniBe-7B-model-remix
- PetroGPT/WestSeverus-7B-DPO
- vanillaOVO/supermario_v4
model-index:
- name: MoEv4Config-TestWeightedTIES-7b
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: 71.59
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MoEv4Config-TestWeightedTIES-7b
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: 88.19
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MoEv4Config-TestWeightedTIES-7b
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: 65.07
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MoEv4Config-TestWeightedTIES-7b
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: 70.87
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MoEv4Config-TestWeightedTIES-7b
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: 83.82
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MoEv4Config-TestWeightedTIES-7b
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: 72.78
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MoEv4Config-TestWeightedTIES-7b
name: Open LLM Leaderboard
---
# MoEv4Config-TestWeightedTIES-7b
MoEv4Config-TestWeightedTIES-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Kukedlc/NeuTrixOmniBe-7B-model-remix](https://huggingface.co/Kukedlc/NeuTrixOmniBe-7B-model-remix)
* [PetroGPT/WestSeverus-7B-DPO](https://huggingface.co/PetroGPT/WestSeverus-7B-DPO)
* [vanillaOVO/supermario_v4](https://huggingface.co/vanillaOVO/supermario_v4)
## 🧩 Configuration
```yaml
models:
- model: Kukedlc/NeuTrixOmniBe-7B-model-remix
# No parameters necessary for base model
- model: Kukedlc/NeuTrixOmniBe-7B-model-remix
parameters:
density: [1, 0.7, 0.1]
weight: [0, 0.3, 0.7, 1]
- model: PetroGPT/WestSeverus-7B-DPO
parameters:
density: [1, 0.7, 0.3]
weight: [0, 0.25, 0.5, 1]
- model: vanillaOVO/supermario_v4
parameters:
density: 0.33
weight:
- filter: mlp
value: 0.5
- value: 0
merge_method: ties
base_model: Kukedlc/NeuTrixOmniBe-7B-model-remix
parameters:
int8_mask: true
normalize: true
sparsify:
- filter: mlp
value: 0.5
- filter: self_attn
value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "jsfs11/MoEv4Config-TestWeightedTIES-7b"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
# [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_jsfs11__MoEv4Config-TestWeightedTIES-7b)
| Metric |Value|
|---------------------------------|----:|
|Avg. |75.39|
|AI2 Reasoning Challenge (25-Shot)|71.59|
|HellaSwag (10-Shot) |88.19|
|MMLU (5-Shot) |65.07|
|TruthfulQA (0-shot) |70.87|
|Winogrande (5-shot) |83.82|
|GSM8k (5-shot) |72.78|
|
liminerity/binarized-ingotrix-slerp-7b | liminerity | 2024-03-11T18:31:06Z | 728 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"eren23/dpo-binarized-NeuralTrix-7B",
"liminerity/Ingot-7b-slerp-7-forged",
"base_model:eren23/dpo-binarized-NeuralTrix-7B",
"base_model:liminerity/Ingot-7b-slerp-7-forged",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-02-12T02:02:55Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- eren23/dpo-binarized-NeuralTrix-7B
- liminerity/Ingot-7b-slerp-7-forged
base_model:
- eren23/dpo-binarized-NeuralTrix-7B
- liminerity/Ingot-7b-slerp-7-forged
model-index:
- name: binarized-ingotrix-slerp-7b
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: 73.21
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/binarized-ingotrix-slerp-7b
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: 88.64
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/binarized-ingotrix-slerp-7b
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: 64.85
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/binarized-ingotrix-slerp-7b
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: 75.57
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/binarized-ingotrix-slerp-7b
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: 82.87
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/binarized-ingotrix-slerp-7b
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: 71.11
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/binarized-ingotrix-slerp-7b
name: Open LLM Leaderboard
---
# binarized-ingotrix-slerp-7b
binarized-ingotrix-slerp-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [eren23/dpo-binarized-NeuralTrix-7B](https://huggingface.co/eren23/dpo-binarized-NeuralTrix-7B)
* [liminerity/Ingot-7b-slerp-7-forged](https://huggingface.co/liminerity/Ingot-7b-slerp-7-forged)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: eren23/dpo-binarized-NeuralTrix-7B
layer_range: [0, 32]
- model: liminerity/Ingot-7b-slerp-7-forged
layer_range: [0, 32]
merge_method: slerp
base_model: eren23/dpo-binarized-NeuralTrix-7B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "liminerity/binarized-ingotrix-slerp-7b"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
# [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_liminerity__binarized-ingotrix-slerp-7b)
| Metric |Value|
|---------------------------------|----:|
|Avg. |76.04|
|AI2 Reasoning Challenge (25-Shot)|73.21|
|HellaSwag (10-Shot) |88.64|
|MMLU (5-Shot) |64.85|
|TruthfulQA (0-shot) |75.57|
|Winogrande (5-shot) |82.87|
|GSM8k (5-shot) |71.11|
|
yam-peleg/Experiment10-7B | yam-peleg | 2024-02-14T12:41:04Z | 728 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-02-13T16:10:46Z | ---
library_name: transformers
license: apache-2.0
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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#### Testing Data
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[More Information Needed]
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Model Card Contact
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mzio/hedgehog-alpaca_clean_mistral-mistral_7b_lk_esn_tqk_lora-lk_untied_head-lsc_1 | mzio | 2024-02-14T08:18:35Z | 728 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"dataset:yahma/alpaca-cleaned",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-02-14T07:16:56Z | ---
license: apache-2.0
datasets:
- yahma/alpaca-cleaned
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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## How to Get Started with the Model
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## Training Details
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
#### Summary
## Model Examination [optional]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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arlineka/Brunhilde-13b-v1 | arlineka | 2024-03-07T01:45:47Z | 728 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"license:cc-by-nc-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-02-14T14:27:02Z | ---
license: cc-by-nc-4.0
tags:
- merge
model-index:
- name: Brunhilde-13b-v1
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: 61.09
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=arlineka/Brunhilde-13b-v1
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: 83.58
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=arlineka/Brunhilde-13b-v1
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: 55.32
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=arlineka/Brunhilde-13b-v1
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: 51.98
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=arlineka/Brunhilde-13b-v1
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: 75.22
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=arlineka/Brunhilde-13b-v1
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: 20.09
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=arlineka/Brunhilde-13b-v1
name: Open LLM Leaderboard
---
# Brunhilde-13b-v1
Brunhilde-13b-v1 is a merge of the following models
* [Gryphe/MythoMax-L2-13b](https://huggingface.co/Gryphe/MythoMax-L2-13b)
* [Undi95/ReMM-SLERP-L2-13B](https://huggingface.co/Undi95/ReMM-SLERP-L2-13B)
## Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "arlineka/Brunhilde-13b-v1"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
# [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_arlineka__Brunhilde-13b-v1)
| Metric |Value|
|---------------------------------|----:|
|Avg. |57.88|
|AI2 Reasoning Challenge (25-Shot)|61.09|
|HellaSwag (10-Shot) |83.58|
|MMLU (5-Shot) |55.32|
|TruthfulQA (0-shot) |51.98|
|Winogrande (5-shot) |75.22|
|GSM8k (5-shot) |20.09|
|
Josephgflowers/TinyLlama-748M-Reason-With-Cinder-Test-2 | Josephgflowers | 2024-02-16T18:03:29Z | 728 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-02-16T17:01:14Z | ---
license: mit
widget:
- text: >
<|system|>
You are a helpful assistant</s>
<|user|>
What is 32+64?</s>
<|assistant|>
---
I cut my TinyLlama 1.1B cinder v 2 down from 22 layers to 14. At 14 there was no coherent text but there were emerging ideas of a response. 1000 steps on step-by-step dataset.
10000 on Reason-with-cinder. The loss was around .6 and the learning rate was still over 4. Starting to get better performance now. This model still needs significat training. I am putting it up as a base model that
needs work. If you continue training please let me know on the tinyllama discord https://discord.com/channels/1156883027805356072/1156883029671813122 or email [email protected] , I have some interesting plans for this model. |
NLUHOPOE/test-case-5 | NLUHOPOE | 2024-02-18T03:26:14Z | 728 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"dataset:Open-Orca/SlimOrca",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-02-18T01:13:21Z | ---
license: apache-2.0
datasets:
- Open-Orca/SlimOrca
language:
- en
---
# Model Details
* Model Description: This model is test for data ordering.
* Developed by: Juhwan Lee
* Model Type: Large Language Model
# Model Architecture
This model is based on Mistral-7B-v0.1. We fine-tuning this model for data ordering task.
Mistral-7B-v0.1 is a transformer model, with the following architecture choices:
* Grouped-Query Attention
* Sliding-Window Attention
* Byte-fallback BPE tokenizer
# Dataset
We random sample SlimOrca dataset.
# Guthub
https://github.com/trailerAI
# License
Apache License 2.0 |
yam-peleg/Experiment15-7B | yam-peleg | 2024-02-18T17:07:26Z | 728 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"chat",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-02-18T16:37:53Z | ---
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- chat
---
**Experiment15-7B**
An experiment for testing and refining a specific training and evaluation pipeline research framework.
This experiment aims to identify potential optimizations, focusing on data engineering, architecture efficiency, and evaluation performance.
The goal is to evaluate the effectiveness of a new training / evaluation pipeline for LLMs.
The experiment will explore adjustments in data preprocessing, model training algorithms, and evaluation metrics to test methods for improvement.
More details in the future experiments.
---
license: apache-2.0
--- |
Kukedlc/NeuralKrishna-7B-V2-DPO | Kukedlc | 2024-03-04T13:07:18Z | 728 | 2 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-02-28T04:05:12Z | ---
license: apache-2.0
model-index:
- name: NeuralKrishna-7B-V2-DPO
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: 74.06
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuralKrishna-7B-V2-DPO
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: 88.97
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuralKrishna-7B-V2-DPO
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: 64.41
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuralKrishna-7B-V2-DPO
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: 76.19
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuralKrishna-7B-V2-DPO
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: 84.29
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuralKrishna-7B-V2-DPO
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: 68.08
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuralKrishna-7B-V2-DPO
name: Open LLM Leaderboard
---
# Neural Krishna DPO
## Fine-tuning + lnegth(choose)
- Training Args:
```python
# LoRA configuration
peft_config = LoraConfig(
r=16,
lora_alpha=16,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj']
)
# Model to fine-tune
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
load_in_4bit=True
)
model.config.use_cache = False
# Training arguments
training_args = TrainingArguments(
per_device_train_batch_size=4,
gradient_accumulation_steps=4,
gradient_checkpointing=True,
learning_rate=5e-5,
lr_scheduler_type="cosine",
max_steps=120,
save_strategy="no",
logging_steps=1,
output_dir=new_model,
optim="paged_adamw_32bit",
warmup_steps=50,
bf16=True,
report_to="wandb",
)
# Create DPO trainer
dpo_trainer = DPOTrainer(
model,
args=training_args,
train_dataset=dataset,
tokenizer=tokenizer,
peft_config=peft_config,
beta=0.1,
max_prompt_length=1024,
max_length=1536,
)
# Fine-tune model with DPO
dpo_trainer.train()
```
# [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_Kukedlc__NeuralKrishna-7B-V2-DPO)
| Metric |Value|
|---------------------------------|----:|
|Avg. |76.00|
|AI2 Reasoning Challenge (25-Shot)|74.06|
|HellaSwag (10-Shot) |88.97|
|MMLU (5-Shot) |64.41|
|TruthfulQA (0-shot) |76.19|
|Winogrande (5-shot) |84.29|
|GSM8k (5-shot) |68.08|
|
CorticalStack/mistral-7b-tak-stack-dpo | CorticalStack | 2024-02-28T17:45:28Z | 728 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"dpo",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-02-28T17:42:24Z | ---
license: apache-2.0
tags:
- dpo
base_model:
- mistralai/Mistral-7B-v0.1
dataset:
- CorticalStack/tak-stack-dpo
---
# mistral-7b-tak-stack-dpo
mistral-7b-tak-stack-dpo is a DPO fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) using the [CorticalStack/tak-stack-dpo](https://huggingface.co/datasets/CorticalStack/tak-stack-dpo) dataset.
### LoRA
- r: 32
- LoRA alpha: 32
- LoRA dropout: 0.05
### Training arguments
- Batch size: 4
- Gradient accumulation steps: 4
- Optimizer: paged_adamw_32bit
- Max steps: 100
- Learning rate: 5e-05
- Learning rate scheduler type: cosine
- Beta: 0.1
- Max prompt length: 1024
- Max length: 1536 |
vicgalle/TruthfulQwen1.5-1.8B | vicgalle | 2024-03-04T14:24:14Z | 728 | 1 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"dataset:jondurbin/truthy-dpo-v0.1",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-01T19:55:14Z | ---
library_name: transformers
license: apache-2.0
datasets:
- jondurbin/truthy-dpo-v0.1
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
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juhwanlee/llmdo-Mistral-7B-case-5 | juhwanlee | 2024-03-07T05:10:55Z | 728 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"dataset:Open-Orca/OpenOrca",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-07T04:01:14Z | ---
license: apache-2.0
datasets:
- Open-Orca/OpenOrca
language:
- en
---
# Model Details
* Model Description: This model is test for data ordering.
* Developed by: Juhwan Lee
* Model Type: Large Language Model
# Model Architecture
This model is based on Mistral-7B-v0.1. We fine-tuning this model for data ordering task.
Mistral-7B-v0.1 is a transformer model, with the following architecture choices:
* Grouped-Query Attention
* Sliding-Window Attention
* Byte-fallback BPE tokenizer
# Dataset
We random sample Open-Orca dataset. (We finetune the 100,000 dataset)
# Guthub
https://github.com/trailerAI
# License
Apache License 2.0 |
harshitv804/MetaMath-Mistral-2x7B | harshitv804 | 2024-03-10T17:44:19Z | 728 | 2 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"meta-math/MetaMath-Mistral-7B",
"Mixture of Experts",
"en",
"base_model:meta-math/MetaMath-Mistral-7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-09T12:02:02Z | ---
base_model:
- meta-math/MetaMath-Mistral-7B
tags:
- mergekit
- merge
- meta-math/MetaMath-Mistral-7B
- Mixture of Experts
license: apache-2.0
language:
- en
pipeline_tag: text-generation
library_name: transformers
---
This is MetaMath-Mistral-2x7B Mixture of Experts (MOE) model created using [mergekit](https://github.com/cg123/mergekit) for experimental and learning purpose of MOE.
## Merge Details
### Merge Method
This model was merged using the SLERP merge method using [meta-math/MetaMath-Mistral-7B](https://huggingface.co/meta-math/MetaMath-Mistral-7B) as the base model.
### Models Merged
The following models were included in the merge:
* [meta-math/MetaMath-Mistral-7B](https://huggingface.co/meta-math/MetaMath-Mistral-7B) x 2
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: meta-math/MetaMath-Mistral-7B
layer_range: [0, 32]
- model: meta-math/MetaMath-Mistral-7B
layer_range: [0, 32]
merge_method: slerp
base_model: meta-math/MetaMath-Mistral-7B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## Inference Code
```python
## install dependencies
## !pip install -q -U git+https://github.com/huggingface/transformers.git
## !pip install -q -U git+https://github.com/huggingface/accelerate.git
## !pip install -q -U sentencepiece
## load model
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_name = "harshitv804/MetaMath-Mistral-2x7B"
# load the model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(
model_name,
trust_remote_code=True
)
tokenizer.pad_token = tokenizer.eos_token
## inference
query = "Maximoff's monthly bill is $60 per month. His monthly bill increased by thirty percent when he started working at home. How much is his total monthly bill working from home?"
prompt =f"""
Below is an instruction that describes a task. Write a response that appropriately completes the request.\n
### Instruction:\n
{query}\n
### Response: Let's think step by step.
"""
# tokenize the input string
inputs = tokenizer(
prompt,
return_tensors="pt",
return_attention_mask=False
)
# generate text using the model
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
outputs = model.generate(**inputs, max_length=2048, streamer=streamer)
# decode and print the output
text = tokenizer.batch_decode(outputs)[0]
```
## Citation
```bibtex
@article{yu2023metamath,
title={MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models},
author={Yu, Longhui and Jiang, Weisen and Shi, Han and Yu, Jincheng and Liu, Zhengying and Zhang, Yu and Kwok, James T and Li, Zhenguo and Weller, Adrian and Liu, Weiyang},
journal={arXiv preprint arXiv:2309.12284},
year={2023}
}
```
```bibtex
@article{jiang2023mistral,
title={Mistral 7B},
author={Jiang, Albert Q and Sablayrolles, Alexandre and Mensch, Arthur and Bamford, Chris and Chaplot, Devendra Singh and Casas, Diego de las and Bressand, Florian and Lengyel, Gianna and Lample, Guillaume and Saulnier, Lucile and others},
journal={arXiv preprint arXiv:2310.06825},
year={2023}
}
``` |
automerger/Experiment29Pastiche-7B | automerger | 2024-03-11T03:14:08Z | 728 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"automerger",
"base_model:yam-peleg/Experiment29-7B",
"base_model:CorticalStack/pastiche-crown-clown-7b-dare",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-10T22:55:51Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- automerger
base_model:
- yam-peleg/Experiment29-7B
- CorticalStack/pastiche-crown-clown-7b-dare
---
# Experiment29Pastiche-7B
Experiment29Pastiche-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration.
* [yam-peleg/Experiment29-7B](https://huggingface.co/yam-peleg/Experiment29-7B)
* [CorticalStack/pastiche-crown-clown-7b-dare](https://huggingface.co/CorticalStack/pastiche-crown-clown-7b-dare)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: yam-peleg/Experiment29-7B
layer_range: [0, 32]
- model: CorticalStack/pastiche-crown-clown-7b-dare
layer_range: [0, 32]
merge_method: slerp
base_model: yam-peleg/Experiment29-7B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
random_seed: 0
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "automerger/Experiment29Pastiche-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
Locutusque/NeuralHyperion-2.0-Mistral-7B | Locutusque | 2024-03-12T20:38:47Z | 728 | 3 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"code",
"chemistry",
"medical",
"en",
"dataset:Locutusque/hyperion-v2.0",
"dataset:argilla/distilabel-capybara-dpo-7k-binarized",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-11T05:36:02Z | ---
library_name: transformers
tags:
- code
- chemistry
- medical
license: apache-2.0
datasets:
- Locutusque/hyperion-v2.0
- argilla/distilabel-capybara-dpo-7k-binarized
language:
- en
---
# NeuralHyperion-2.0-Mistral-7B
## Model Details
- **Model Name**: Locutusque/NeuralHyperion-2.0-Mistral-7B
- **Base Model**: mistralai/Mistral-7B-v0.1
- **Publisher**: Locutusque
- **Model Type**: Question answering, conversational AI, code generation, medical text comprehension, mathematical reasoning, logical reasoning.
- **Language**: Multi-domain, English language.
- **License**: Apache-2.0
## Model Description
`Locutusque/NeuralHyperion-2.0-Mistral-7B` is a state-of-the-art language model fine-tuned on the Hyperion-v2.0 and distilabel-capybara dataset for advanced reasoning across scientific domains. This model is designed to handle complex inquiries and instructions, leveraging the diverse and rich information contained in the Hyperion dataset. Its primary use cases include but are not limited to complex question answering, conversational understanding, code generation, medical text comprehension, mathematical reasoning, and logical reasoning.
## Intended Use
This model is intended for researchers and practitioners looking for a powerful tool to tackle challenging problems in scientific domains. It can be used in the following scenarios:
- AI-driven tutoring systems for science, medicine, mathematics, and computer science.
- Assistive tools for professionals requiring fast and accurate domain-specific information retrieval.
- Platforms that require conversational AI capabilities with a focus on technical and scientific reasoning.
- Automation in code generation and understanding complex programming context.
## Training Data
The `Locutusque/NeuralHyperion-2.0-Mistral-7B` model was fine-tuned on 1,550,000 examples of the Hyperion-v2.0 dataset, which amalgamates various datasets rich in diversity and complexity, including programming, medical texts, mathematical problems, and reasoning tasks. Then, it is further fine-tuned on the Capybara preference data using DPO.
## Evaluation Results
Coming soon.
## Quants
ExLlamaV2: https://huggingface.co/bartowski/NeuralHyperion-2.0-Mistral-7B-exl2
GGUF: https://huggingface.co/bartowski/NeuralHyperion-2.0-Mistral-7B-GGUF
AWQ: https://huggingface.co/solidrust/NeuralHyperion-2.0-Mistral-7B-AWQ
## How to Use
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Locutusque/NeuralHyperion-2.0-Mistral-7B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# For a text generation task
input_text = "<|im_start|>user\nWhat are the implications of Einstein's theory of relativity in modern physics?<|im_end|>\n<|im_start|>assistant\n"
input_ids = tokenizer.encode(input_text, return_tensors="pt")
# Generate a response
outputs = model.generate(input_ids, max_length=200, num_return_sequences=1, temperature=0.8, top_p=0.95, top_k=40, repetition_penalty=1.1)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Known Limitations
The diversity of the dataset could lead to inconsistencies in the model's responses due to variations in data formatting and annotation quality.
## Licensing Information
This model is released under the Apache-2.0 license. |
automerger/Experiment27Neuralsirkrishna-7B | automerger | 2024-04-01T13:31:46Z | 728 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"automerger",
"base_model:yam-peleg/Experiment27-7B",
"base_model:Kukedlc/NeuralSirKrishna-7b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-11T12:37:26Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- automerger
base_model:
- yam-peleg/Experiment27-7B
- Kukedlc/NeuralSirKrishna-7b
---
# Experiment27Neuralsirkrishna-7B
Experiment27Neuralsirkrishna-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration.
* [yam-peleg/Experiment27-7B](https://huggingface.co/yam-peleg/Experiment27-7B)
* [Kukedlc/NeuralSirKrishna-7b](https://huggingface.co/Kukedlc/NeuralSirKrishna-7b)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: yam-peleg/Experiment27-7B
layer_range: [0, 32]
- model: Kukedlc/NeuralSirKrishna-7b
layer_range: [0, 32]
merge_method: slerp
base_model: yam-peleg/Experiment27-7B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
random_seed: 0
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "automerger/Experiment27Neuralsirkrishna-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
automerger/Ognoexperiment27Multi_verse_model-7B | automerger | 2024-03-11T14:11:32Z | 728 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"automerger",
"base_model:ammarali32/multi_verse_model",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-11T14:10:42Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- automerger
base_model:
- ammarali32/multi_verse_model
---
# Ognoexperiment27Multi_verse_model-7B
Ognoexperiment27Multi_verse_model-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration.
* [ammarali32/multi_verse_model](https://huggingface.co/ammarali32/multi_verse_model)
## 🧩 Configuration
```yaml
models:
- model: automerger/OgnoExperiment27-7B
# No parameters necessary for base model
- model: ammarali32/multi_verse_model
parameters:
density: 0.53
weight: 0.6
merge_method: dare_ties
base_model: automerger/OgnoExperiment27-7B
parameters:
int8_mask: true
dtype: bfloat16
random_seed: 0
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "automerger/Ognoexperiment27Multi_verse_model-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
rwitz/experiment26-SPIN-iter-0 | rwitz | 2024-03-11T22:23:04Z | 728 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-11T21:46:23Z | ---
library_name: transformers
tags: []
license: mit
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Replete-AI/Phi-Elothir | Replete-AI | 2024-03-31T20:21:54Z | 728 | 2 | transformers | [
"transformers",
"safetensors",
"phi",
"text-generation",
"conversational",
"custom_code",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-12T20:00:28Z | ---
license: mit
language:
- en
thumbnail: "https://cdn-uploads.huggingface.co/production/uploads/6589d7e6586088fd2784a12c/TqnMpteVAyfiiNHx4lVkU.png"
---
# You are welcome here, traveler.
### Named after the method used to create it, interleaving the layers of its predecessor to become far larger, giving it much more potential.
[Elothir](https://wowpedia.fandom.com/wiki/Elothir) was an ancient treeant, and I couldn't think of a better naming convention for a model that was created using the passthrough method.
By concatenating layers from different LLMs, it can produce models with an exotic number of parameters (e.g., 9B with two 7B parameter models). These models are often referred to as "frankenmerges" or "Frankenstein models" by the community.
Many thanks to [Abacaj](https://huggingface.co/abacaj) for providing the [fine tuned weights](https://huggingface.co/abacaj/phi-2-super) that were used in the creation of this base model...thanks to [KatyTheCutie](https://huggingface.co/KatyTheCutie) for inspring me to test out this script.
## This idea was brought to me by [The Face of Goonery](https://huggingface.co/The-Face-Of-Goonery), also known as Caleb Morgan. I have him to thank if fine-tuning this model turns out to be a success
# How to run inference:
```python
import transformers
import torch
if __name__ == "__main__":
model_name = "Replete-AI/Phi-Elothir"
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
model = (
transformers.AutoModelForCausalLM.from_pretrained(
model_name,
)
.to("cuda:0")
.eval()
)
messages = [
{"role": "user", "content": "Hello, who are you?"}
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
input_ids_cutoff = inputs.size(dim=1)
with torch.no_grad():
generated_ids = model.generate(
input_ids=inputs,
use_cache=True,
max_new_tokens=512,
temperature=0.2,
top_p=0.95,
do_sample=True,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
)
completion = tokenizer.decode(
generated_ids[0][input_ids_cutoff:],
skip_special_tokens=True,
)
print(completion)
```
# Chat template
The model uses the same chat template as found in Mistral instruct models:
# [Join the Replete AI Discord here!](https://discord.gg/tG5aY4EX4T)
# The Sauce:
```yml
dtype: float16
merge_method: passthrough
slices:
- sources:
- model: abacaj/phi-2-super
layer_range: [0,2]
- sources:
- model: abacaj/phi-2-super
layer_range: [1,3]
- sources:
- model: abacaj/phi-2-super
layer_range: [2,4]
- sources:
- model: abacaj/phi-2-super
layer_range: [3,5]
- sources:
- model: abacaj/phi-2-super
layer_range: [4,6]
- sources:
- model: abacaj/phi-2-super
layer_range: [5,7]
- sources:
- model: abacaj/phi-2-super
layer_range: [6,8]
- sources:
- model: abacaj/phi-2-super
layer_range: [7,9]
- sources:
- model: abacaj/phi-2-super
layer_range: [8,10]
- sources:
- model: abacaj/phi-2-super
layer_range: [9,11]
- sources:
- model: abacaj/phi-2-super
layer_range: [10,12]
- sources:
- model: abacaj/phi-2-super
layer_range: [11,13]
- sources:
- model: abacaj/phi-2-super
layer_range: [12,14]
- sources:
- model: abacaj/phi-2-super
layer_range: [13,15]
- sources:
- model: abacaj/phi-2-super
layer_range: [14,16]
- sources:
- model: abacaj/phi-2-super
layer_range: [15,17]
- sources:
- model: abacaj/phi-2-super
layer_range: [16,18]
- sources:
- model: abacaj/phi-2-super
layer_range: [17,19]
- sources:
- model: abacaj/phi-2-super
layer_range: [18,20]
- sources:
- model: abacaj/phi-2-super
layer_range: [19,21]
- sources:
- model: abacaj/phi-2-super
layer_range: [20,22]
- sources:
- model: abacaj/phi-2-super
layer_range: [21,23]
- sources:
- model: abacaj/phi-2-super
layer_range: [22,24]
- sources:
- model: abacaj/phi-2-super
layer_range: [23,25]
- sources:
- model: abacaj/phi-2-super
layer_range: [24,26]
- sources:
- model: abacaj/phi-2-super
layer_range: [25,27]
- sources:
- model: abacaj/phi-2-super
layer_range: [26,28]
- sources:
- model: abacaj/phi-2-super
layer_range: [27,29]
- sources:
- model: abacaj/phi-2-super
layer_range: [28,30]
- sources:
- model: abacaj/phi-2-super
layer_range: [29,31]
- sources:
- model: abacaj/phi-2-super
layer_range: [30,32]
```
|
chihoonlee10/T3Q-MSlerp-13B | chihoonlee10 | 2024-03-14T01:49:31Z | 728 | 0 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-13T22:59:10Z | ---
license: apache-2.0
---
## T3Q-MSlerp-13B
T3Q-MSlerp-13B is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
* [zhengr/MixTAO-7Bx2-MoE-Instruct-v7.0](https://huggingface.co/zhengr/MixTAO-7Bx2-MoE-Instruct-v7.0)
* [yunconglong/13B_MATH_DPO](https://huggingface.co/yunconglong/13B_MATH_DPO)
**Model Developers** Chihoon Lee(chlee10), T3Q
```yaml
slices:
- sources:
- model: zhengr/MixTAO-7Bx2-MoE-Instruct-v7.0
layer_range: [0, 32]
- model: yunconglong/13B_MATH_DPO
layer_range: [0, 32]
merge_method: slerp
base_model: zhengr/MixTAO-7Bx2-MoE-Instruct-v7.0
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5 # fallback for rest of tensors
dtype: float16
``` |
4season/alignment_model_test | 4season | 2024-04-03T09:09:09Z | 728 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-16T07:53:22Z | ---
license: apache-2.0
language:
- en
---
# 4season/model_eval_test
# **Introduction**
This model is test version, alignment-tuned model.
We utilize state-of-the-art instruction fine-tuning methods including direct preference optimization (DPO).
After DPO training, we linearly merged models to boost performance. |
vicgalle/SystemHermes-7B | vicgalle | 2024-03-16T14:21:04Z | 728 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-16T14:16:47Z | ---
library_name: transformers
license: apache-2.0
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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vicgalle/SystemHermes-2-7B | vicgalle | 2024-03-16T18:35:56Z | 728 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-16T18:33:03Z | ---
library_name: transformers
license: apache-2.0
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
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### Out-of-Scope Use
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## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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### Testing Data, Factors & Metrics
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[More Information Needed]
#### Summary
## Model Examination [optional]
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[More Information Needed]
## Environmental Impact
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- **Hardware Type:** [More Information Needed]
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LLMNewbie/vic_critT_20pr | LLMNewbie | 2024-03-20T02:48:10Z | 728 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"zh",
"license:unknown",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-18T05:41:29Z | ---
license: unknown
language:
- en
- zh
---
This model is a weight-pruned large language model originated from Vicuna-13B.
Language model pruning is a technique used to reduce the size and computational requirements of language models,
making them more efficient for deployment without significantly sacrificing their performance or accuracy.
This model uses structured pruning instead of unstructured pruning.
The structured pruning removes entire units or channels (e.g., neurons, layers, or filter channels in trnasformer).
This approach can lead to more efficient computational gains since it aligns better with how hardware utilizes data,
but it may have a more significant impact on model performance.
However, the unstructured pruning, remove individual weights across the model without regard to the structure of the network.
While it can lead to significant reductions in model size,
it may not always translate to speed gains since the resulting sparse matrices might not be efficiently handled by all hardware. |
LeroyDyer/Mixtral_BioMedical | LeroyDyer | 2024-04-09T16:39:38Z | 728 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"chemistry",
"biology",
"code",
"medical",
"not-for-all-audiences",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-18T05:43:28Z | ---
license: mit
language:
- en
library_name: transformers
tags:
- chemistry
- biology
- code
- medical
- not-for-all-audiences
---
Highly focused on medical Training datasets ; + Upgraded inplace
VERY HIGH SCORING MODEL |
Isotonic/Hermes-2-Pro-Mixtral-4x7B | Isotonic | 2024-04-04T02:48:42Z | 728 | 2 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"moe",
"merge",
"mergekit",
"lazymergekit",
"NousResearch/Hermes-2-Pro-Mistral-7B",
"Mixtral",
"instruct",
"finetune",
"chatml",
"DPO",
"RLHF",
"gpt4",
"synthetic data",
"distillation",
"function calling",
"json mode",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-19T13:24:42Z | ---
license: apache-2.0
tags:
- moe
- merge
- mergekit
- lazymergekit
- NousResearch/Hermes-2-Pro-Mistral-7B
- Mixtral
- instruct
- finetune
- chatml
- DPO
- RLHF
- gpt4
- synthetic data
- distillation
- function calling
- json mode
---
🌟 Buying me coffee is a direct way to show support for this project.
<a href="https://www.buymeacoffee.com/isotonic"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
# Hermes-2-Pro-Mixtral-4x7B
Hermes-2-Pro-Mixtral-4x7B is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* 4 X [NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)
* [Evaluation Results](https://huggingface.co/datasets/open-llm-leaderboard/details_Isotonic__Hermes-2-Pro-Mixtral-4x7B)
## 🧩 Configuration
```yamlbase_model: NousResearch/Hermes-2-Pro-Mistral-7B
experts:
- source_model: NousResearch/Hermes-2-Pro-Mistral-7B
positive_prompts:
- " "
- source_model: NousResearch/Hermes-2-Pro-Mistral-7B
positive_prompts:
- " "
- source_model: NousResearch/Hermes-2-Pro-Mistral-7B
positive_prompts:
- " "
- source_model: NousResearch/Hermes-2-Pro-Mistral-7B
positive_prompts:
- " "
```
## 💻 Usage
Here is example code using HuggingFace Transformers to inference the model (note: in 4bit, it will require around 5GB of VRAM)
Note: To use function calling, you should see the github repo above.
```python
# Code to inference Hermes with HF Transformers
# Requires pytorch, transformers, bitsandbytes, sentencepiece, protobuf, and flash-attn packages
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import LlamaTokenizer, MistralForCausalLM
import bitsandbytes, flash_attn
tokenizer = LlamaTokenizer.from_pretrained('NousResearch/Hermes-2-Pro-Mistral-7B', trust_remote_code=True)
model = MistralForCausalLM.from_pretrained(
"Isotonic/Hermes-2-Pro-Mixtral-4x7B",
torch_dtype=torch.bfloat16,
device_map="auto",
load_in_8bit=False,
load_in_4bit=True,
use_flash_attention_2=True # ampere only
)
prompts = [
"""<|im_start|>system
You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<|im_end|>
<|im_start|>user
Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world.<|im_end|>
<|im_start|>assistant""",
]
for chat in prompts:
print(chat)
input_ids = tokenizer(chat, return_tensors="pt").input_ids.to("cuda")
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)
response = tokenizer.decode(generated_ids[0][input_ids.shape[-1]:], skip_special_tokens=True, clean_up_tokenization_space=True)
print(f"Response: {response}")
```
### 📚 Function Calling:
All code for utilizing, parsing, and building function calling templates are available on NousHermes's GitHub page:
[https://github.com/NousResearch/Hermes-Function-Calling](https://github.com/NousResearch/Hermes-Function-Calling)
### 🖌️ Prompt Format
Hermes 2 Pro uses ChatML as the prompt format, opening up a much more structured system for engaging the LLM in multi-turn chat dialogue.
System prompts allow steerability and interesting new ways to interact with an LLM, guiding rules, roles, and stylistic choices of the model.
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.
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.
Prompt with system instruction (Use whatever system prompt you like, this is just an example!):
```
<|im_start|>system
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|>
<|im_start|>user
Hello, who are you?<|im_end|>
<|im_start|>assistant
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|>
```
This prompt is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the
`tokenizer.apply_chat_template()` method:
```python
messages = [
{"role": "system", "content": "You are Hermes 2."},
{"role": "user", "content": "Hello, who are you?"}
]
gen_input = tokenizer.apply_chat_template(messages, return_tensors="pt")
model.generate(**gen_input)
```
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
that the model continues with an assistant response.
To utilize the prompt format without a system prompt, simply leave the line out.
### 🖌️📚 Prompt Format for Function Calling
Our model was trained on specific system prompts and structures for Function Calling.
You should use the system role with this message, followed by a function signature json as this example shows here.
```
<|im_start|>system
You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools: <tools> {"type": "function", "function": {"name": "get_stock_fundamentals", "description": "get_stock_fundamentals(symbol: str) -> dict - Get fundamental data for a given stock symbol using yfinance API.\\n\\n Args:\\n symbol (str): The stock symbol.\\n\\n Returns:\\n dict: A dictionary containing fundamental data.\\n Keys:\\n - \'symbol\': The stock symbol.\\n - \'company_name\': The long name of the company.\\n - \'sector\': The sector to which the company belongs.\\n - \'industry\': The industry to which the company belongs.\\n - \'market_cap\': The market capitalization of the company.\\n - \'pe_ratio\': The forward price-to-earnings ratio.\\n - \'pb_ratio\': The price-to-book ratio.\\n - \'dividend_yield\': The dividend yield.\\n - \'eps\': The trailing earnings per share.\\n - \'beta\': The beta value of the stock.\\n - \'52_week_high\': The 52-week high price of the stock.\\n - \'52_week_low\': The 52-week low price of the stock.", "parameters": {"type": "object", "properties": {"symbol": {"type": "string"}}, "required": ["symbol"]}}} </tools> Use the following pydantic model json schema for each tool call you will make: {"properties": {"arguments": {"title": "Arguments", "type": "object"}, "name": {"title": "Name", "type": "string"}}, "required": ["arguments", "name"], "title": "FunctionCall", "type": "object"} For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows:
<tool_call>
{"arguments": <args-dict>, "name": <function-name>}
</tool_call><|im_end|>
```
To complete the function call, create a user prompt that follows the above system prompt, like so:
```
<|im_start|>user
Fetch the stock fundamentals data for Tesla (TSLA)<|im_end|>
```
The model will then generate a tool call, which your inference code must parse, and plug into a function (see example inference code here: https://github.com/NousResearch/Hermes-Function-Calling):
```
<|im_start|>assistant
<tool_call>
{"arguments": {"symbol": "TSLA"}, "name": "get_stock_fundamentals"}
</tool_call><|im_end|>
```
Once you parse the tool call, call the api and get the returned values for the call, and pass it back in as a new role, `tool` like so:
```
<|im_start|>tool
<tool_response>
{"name": "get_stock_fundamentals", "content": {'symbol': 'TSLA', 'company_name': 'Tesla, Inc.', 'sector': 'Consumer Cyclical', 'industry': 'Auto Manufacturers', 'market_cap': 611384164352, 'pe_ratio': 49.604652, 'pb_ratio': 9.762013, 'dividend_yield': None, 'eps': 4.3, 'beta': 2.427, '52_week_high': 299.29, '52_week_low': 152.37}}
</tool_response>
<|im_end|>
```
The assistant will then read in that data from the function's response, and generate a natural language response:
```
<|im_start|>assistant
The stock fundamentals data for Tesla (TSLA) are as follows:
- **Symbol**: TSLA
- **Company Name**: Tesla, Inc.
- **Sector**: Consumer Cyclical
- **Industry**: Auto Manufacturers
- **Market Capitalization**: $566,160,130,480
- **Forward Price-to-Earnings Ratio (PE Ratio)**: 42.73
- **Price-to-Book Ratio (PB Ratio)**: 9.04
- **Dividend Yield**: N/A
- **Trailing Earnings Per Share (EPS)**: $4.3
- **Beta Value of the Stock**: 2.42
- **52-Week High Price of the Stock**: $299.29
- **52-Week Low Price of the Stock**: $152.37
This information provides a snapshot of Tesla's financial position and performance based on the fundamental data obtained from the yfinance API. It shows that Tesla has a substantial market capitalization and a relatively high P/E and P/B ratio compared to other stocks in its industry. The company does not pay a dividend at the moment, which is reflected by a 'Dividend Yield' of 'None'. The Beta value indicates that Tesla's stock has a moderate level of volatility relative to the market. The 52-week high and low prices give an idea of the stock's range over the past year. This data can be useful when assessing investment opportunities and making investment decisions.<|im_end|>
```
### 🖌️📚 Prompt Format for JSON Mode / Structured Outputs
Our model was also trained on a specific system prompt for Structured Outputs, which should respond with **only** a json object response, in a specific json schema.
Your schema can be made from a pydantic object using our codebase, with the standalone script `jsonmode.py` available here: https://github.com/NousResearch/Hermes-Function-Calling/tree/main
```
<|im_start|>system
You are a helpful assistant that answers in JSON. Here's the json schema you must adhere to:\n<schema>\n{schema}\n</schema><|im_end|>
```
Given the {schema} that you provide, it should follow the format of that json to create it's response, all you have to do is give a typical user prompt, and it will respond in JSON.
# Citation:
```bibtext
@misc{Hermes-2-Pro-Mistral-7B,
url={[https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B]https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)},
title={Hermes-2-Pro-Mistral-7B},
author={"interstellarninja", "Teknium", "theemozilla", "karan4d", "huemin_art"}
}
``` |
TeeZee/NEBULA-XB-v1.0 | TeeZee | 2024-04-04T21:50:11Z | 728 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"dataset:Open-Orca/SlimOrca",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-24T20:37:35Z | ---
language:
- en
license: apache-2.0
datasets:
- Open-Orca/SlimOrca
model-index:
- name: NEBULA-XB-v1.0
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: 56.66
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/NEBULA-XB-v1.0
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: 81.78
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/NEBULA-XB-v1.0
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: 60.98
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/NEBULA-XB-v1.0
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: 44.03
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/NEBULA-XB-v1.0
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: 77.66
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/NEBULA-XB-v1.0
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: 0.0
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/NEBULA-XB-v1.0
name: Open LLM Leaderboard
---
### TeeZee/NEBULA-XB-v1.03 ###
Experiment, can DUS be taken one or more steps further?
### Technical notes:
- pretrained model v03 finetuned on 50k entries from SlimOrca dataset
- 18 layers removed from both models of finetuned GALAXY-XB-v03
- model has 108 layers (((48-12)*2)-18)*2 = 108
- second step in scaling DUS procedure
### To evaluate
- model performance after merge, should be a little lover that GALAXY finetuned on 50k of slimorca
# [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_TeeZee__NEBULA-XB-v1.0)
| Metric |Value|
|---------------------------------|----:|
|Avg. |53.52|
|AI2 Reasoning Challenge (25-Shot)|56.66|
|HellaSwag (10-Shot) |81.78|
|MMLU (5-Shot) |60.98|
|TruthfulQA (0-shot) |44.03|
|Winogrande (5-shot) |77.66|
|GSM8k (5-shot) | 0.00|
|
abhishek/autotrain-c71ux-tngfu | abhishek | 2024-03-28T14:15:13Z | 728 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"mistral",
"text-generation",
"autotrain",
"text-generation-inference",
"conversational",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-28T09:59:28Z | ---
tags:
- autotrain
- text-generation-inference
- text-generation
library_name: transformers
widget:
- messages:
- role: user
content: What is your favorite condiment?
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` |
Josephgflowers/Tinyllama-1.5B-Cinder-Test-4 | Josephgflowers | 2024-04-06T16:10:02Z | 728 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-04-06T15:31:32Z | ---
license: mit
---
This is a depth up scalled model of the 616M cinder model and Cinder v2. This model still needs further training. Putting it up for testing. More information coming. Maybe. Lol. Here is a brief desc of the project: Im mixing a lot of techniques I guess that I found interesting and have been testing, HF Cosmo is not great but decent and was fully trained in 4 days using a mix of more fine tuned directed datasets and some synthetic textbook style datasets. So I used pruning and a similar mix as Cosmo on tinyllama (trained on a ton of data for an extended time for its size) to keep the tinyllama model coherent during pruning. Now I am trying to depth up scale it using my pruned model and an original, Then taking a majority of each and combining them to create a larger model. Then it needs more training, then fine tuning. Then theoretically it will be a well performing 1.5B model (that didn't need full scale training). Test 2, some training, re depth upscalled with cinder reason 1.3B and merged back with 1.5 and slight training. Continuing training from this model for next iteration. This is the next iteration with 1 go on metamath and step by step reasoning with cinder.
And a little textbooks are all you need lite. |
sfairXC/FsfairX-Zephyr-Chat-v0.1 | sfairXC | 2024-04-24T14:45:50Z | 728 | 7 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:2312.11456",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-04-20T07:21:04Z | ---
license: cc-by-sa-4.0
---
This model is the RLHF version of `HuggingFaceH4/mistral-7b-sft-beta` without any external responses.
We perform GSHF algorithm on SFT baseline. The external signals include (1) Reward model; (2) AI-generated Prompts.
**We obtain 35.95% win-rate (34.79% LC win-rate) on Alpaca Eval v2.** The win-rate of the base model is only 4.63%.
For MT-bench, it obtained about 7.5, where the base model is only 5.3.
We have demonstrated the significant potential of the iterative RLHF algorithm for LLMs to deliver appropriate and well-structured responses,
even without any external responses.
## Model Details
We perform 3 iterations of GSHF algorithm on `HuggingFaceH4/mistral-7b-sft-beta` labeled by reward model, where prompts are generated by ChatGPT with self-instruct type prompt augmentation.
We use AI-generated 60K prompts in the training process.
Examples are as below,
```json
{"prompt": "Why is gold considered a good reserve asset for central banks?"}
{"prompt": "What are the top 5 yoga poses for stress relief?"}
{"prompt": "Craft a blog title about the health implications of eating avocados daily based on their caloric value."}
{"prompt": "Design a simple HTML chat interface that simulates a conversation between a user and a bot, displaying two messages from each."}
{"prompt": "List 10 names from different cultures that embody the meanings of peace, harmony, or compassion."}
```
## Uses
The usage and chat template format follow the SFT model `HuggingFaceH4/mistral-7b-sft-beta`.
```python
# Install transformers from source - only needed for versions <= v4.34
# pip install git+https://github.com/huggingface/transformers.git
# pip install accelerate
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="sfairXC/FsfairX-Zephyr-Chat-v0.1", torch_dtype=torch.bfloat16, device_map="auto")
# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
{"role": "system", "content": "You are a friendly chatbot who always responds in the style of a pirate"},
{"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
# <|system|>
# You are a friendly chatbot who always responds in the style of a pirate.</s>
# <|user|>
# How many helicopters can a human eat in one sitting?</s>
# <|assistant|>
# Ah, me hearty matey! But yer question be a puzzler! A human cannot eat a helicopter in one sitting, as helicopters are not edible. They be made of metal, plastic, and other materials, not food!
```
## Evaluation
The evaluation on Alpaca Eval v2 are provided as below,
| Model | Win Rate | LC Win Rate | Avg Length |
|-------------|----------|-------------|------------|
| Base | 4.63 | 8.01 | 916 |
| Iteration 1 | 13.26 | 20.81 | 1205 |
| Iteration 2 | 23.57 | 27.63 | 1623 |
| Iteration 3 | 35.95 | 34.79 | 2275 |
## Citation
If you found this helpful, please cite the following papers.
```bibtex
@article{dong2023raft,
title={Raft: Reward ranked finetuning for generative foundation model alignment},
author={Dong, Hanze and Xiong, Wei and Goyal, Deepanshu and Pan, Rui and Diao, Shizhe and Zhang, Jipeng and Shum, Kashun and Zhang, Tong},
journal={arXiv preprint arXiv:2304.06767},
year={2023}
}
@misc{xiong2024iterative,
title={Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint},
author={Wei Xiong and Hanze Dong and Chenlu Ye and Ziqi Wang and Han Zhong and Heng Ji and Nan Jiang and Tong Zhang},
year={2024},
eprint={2312.11456},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
``` |
gaianet/Codestral-22B-v0.1-GGUF | gaianet | 2024-05-30T09:16:15Z | 728 | 0 | transformers | [
"transformers",
"gguf",
"mistral",
"text-generation",
"code",
"base_model:mistralai/Codestral-22B-v0.1",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-05-30T06:24:25Z | ---
license: other
license_name: mnpl
license_link: https://mistral.ai/licences/MNPL-0.1.md
model_name: Codestral-22B-v0.1
base_model: mistralai/Codestral-22B-v0.1
inference: false
model_creator: mistralai
quantized_by: Second State Inc.
tags:
- code
language:
- code
---
# Codestral-22B-v0.1-GGUF
## Original Model
[mistralai/Codestral-22B-v0.1](https://huggingface.co/mistralai/Codestral-22B-v0.1)
## Run with Gaianet
**Prompt template**
prompt template: `mistral-instruct`
**Context size**
chat_ctx_size: `32000`
**Run with GaiaNet**
- Quick start: https://docs.gaianet.ai/node-guide/quick-start
- Customize your node: https://docs.gaianet.ai/node-guide/customize
## Quantized GGUF Models
| Name | Quant method | Bits | Size | Use case |
| ---- | ---- | ---- | ---- | ----- |
| [Codestral-22B-v0.1-hf-Q2_K.gguf](https://huggingface.co/gaianet/Codestral-22B-v0.1-GGUF/blob/main/Codestral-22B-v0.1-hf-Q2_K.gguf) | Q2_K | 2 | 8.27 GB| smallest, significant quality loss - not recommended for most purposes |
| [Codestral-22B-v0.1-hf-Q3_K_L.gguf](https://huggingface.co/gaianet/Codestral-22B-v0.1-GGUF/blob/main/Codestral-22B-v0.1-hf-Q3_K_L.gguf) | Q3_K_L | 3 | 11.7 GB| small, substantial quality loss |
| [Codestral-22B-v0.1-hf-Q3_K_M.gguf](https://huggingface.co/gaianet/Codestral-22B-v0.1-GGUF/blob/main/Codestral-22B-v0.1-hf-Q3_K_M.gguf) | Q3_K_M | 3 | 10.8 GB| very small, high quality loss |
| [Codestral-22B-v0.1-hf-Q3_K_S.gguf](https://huggingface.co/gaianet/Codestral-22B-v0.1-GGUF/blob/main/Codestral-22B-v0.1-hf-Q3_K_S.gguf) | Q3_K_S | 3 | 9.64 GB| very small, high quality loss |
| [Codestral-22B-v0.1-hf-Q4_0.gguf](https://huggingface.co/gaianet/Codestral-22B-v0.1-GGUF/blob/main/Codestral-22B-v0.1-hf-Q4_0.gguf) | Q4_0 | 4 | 12.6 GB| legacy; small, very high quality loss - prefer using Q3_K_M |
| [Codestral-22B-v0.1-hf-Q4_K_M.gguf](https://huggingface.co/gaianet/Codestral-22B-v0.1-GGUF/blob/main/Codestral-22B-v0.1-hf-Q4_K_M.gguf) | Q4_K_M | 4 | 13.3 GB| medium, balanced quality - recommended |
| [Codestral-22B-v0.1-hf-Q4_K_S.gguf](https://huggingface.co/gaianet/Codestral-22B-v0.1-GGUF/blob/main/Codestral-22B-v0.1-hf-Q4_K_S.gguf) | Q4_K_S | 4 | 12.7 GB| small, greater quality loss |
| [Codestral-22B-v0.1-hf-Q5_0.gguf](https://huggingface.co/gaianet/Codestral-22B-v0.1-GGUF/blob/main/Codestral-22B-v0.1-hf-Q5_0.gguf) | Q5_0 | 5 | 15.3 GB| legacy; medium, balanced quality - prefer using Q4_K_M |
| [Codestral-22B-v0.1-hf-Q5_K_M.gguf](https://huggingface.co/gaianet/Codestral-22B-v0.1-GGUF/blob/main/Codestral-22B-v0.1-hf-Q5_K_M.gguf) | Q5_K_M | 5 | 15.7 GB| large, very low quality loss - recommended |
| [Codestral-22B-v0.1-hf-Q5_K_S.gguf](https://huggingface.co/gaianet/Codestral-22B-v0.1-GGUF/blob/main/Codestral-22B-v0.1-hf-Q5_K_S.gguf) | Q5_K_S | 5 | 15.3 GB| large, low quality loss - recommended |
| [Codestral-22B-v0.1-hf-Q6_K.gguf](https://huggingface.co/gaianet/Codestral-22B-v0.1-GGUF/blob/main/Codestral-22B-v0.1-hf-Q6_K.gguf) | Q6_K | 6 | 18.3 GB| very large, extremely low quality loss |
| [Codestral-22B-v0.1-hf-Q8_0.gguf](https://huggingface.co/gaianet/Codestral-22B-v0.1-GGUF/blob/main/Codestral-22B-v0.1-hf-Q8_0.gguf) | Q8_0 | 8 | 23.6 GB| very large, extremely low quality loss - not recommended |
| [Codestral-22B-v0.1-hf-f16.gguf](https://huggingface.co/gaianet/Codestral-22B-v0.1-GGUF/blob/main/Codestral-22B-v0.1-hf-f16.gguf) | f16 | 16 | 44.5 GB| |
*Quantized with llama.cpp b3030.*
|
RichardErkhov/Kooten_-_DaringMaid-20B-V1.1-gguf | RichardErkhov | 2024-06-03T21:13:48Z | 728 | 0 | null | [
"gguf",
"region:us"
] | null | 2024-06-03T10:41:47Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
DaringMaid-20B-V1.1 - GGUF
- Model creator: https://huggingface.co/Kooten/
- Original model: https://huggingface.co/Kooten/DaringMaid-20B-V1.1/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [DaringMaid-20B-V1.1.Q2_K.gguf](https://huggingface.co/RichardErkhov/Kooten_-_DaringMaid-20B-V1.1-gguf/blob/main/DaringMaid-20B-V1.1.Q2_K.gguf) | Q2_K | 6.91GB |
| [DaringMaid-20B-V1.1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Kooten_-_DaringMaid-20B-V1.1-gguf/blob/main/DaringMaid-20B-V1.1.IQ3_XS.gguf) | IQ3_XS | 7.63GB |
| [DaringMaid-20B-V1.1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Kooten_-_DaringMaid-20B-V1.1-gguf/blob/main/DaringMaid-20B-V1.1.IQ3_S.gguf) | IQ3_S | 8.06GB |
| [DaringMaid-20B-V1.1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Kooten_-_DaringMaid-20B-V1.1-gguf/blob/main/DaringMaid-20B-V1.1.Q3_K_S.gguf) | Q3_K_S | 8.06GB |
| [DaringMaid-20B-V1.1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Kooten_-_DaringMaid-20B-V1.1-gguf/blob/main/DaringMaid-20B-V1.1.IQ3_M.gguf) | IQ3_M | 8.53GB |
| [DaringMaid-20B-V1.1.Q3_K.gguf](https://huggingface.co/RichardErkhov/Kooten_-_DaringMaid-20B-V1.1-gguf/blob/main/DaringMaid-20B-V1.1.Q3_K.gguf) | Q3_K | 9.04GB |
| [DaringMaid-20B-V1.1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Kooten_-_DaringMaid-20B-V1.1-gguf/blob/main/DaringMaid-20B-V1.1.Q3_K_M.gguf) | Q3_K_M | 9.04GB |
| [DaringMaid-20B-V1.1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Kooten_-_DaringMaid-20B-V1.1-gguf/blob/main/DaringMaid-20B-V1.1.Q3_K_L.gguf) | Q3_K_L | 9.9GB |
| [DaringMaid-20B-V1.1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Kooten_-_DaringMaid-20B-V1.1-gguf/blob/main/DaringMaid-20B-V1.1.IQ4_XS.gguf) | IQ4_XS | 10.01GB |
| [DaringMaid-20B-V1.1.Q4_0.gguf](https://huggingface.co/RichardErkhov/Kooten_-_DaringMaid-20B-V1.1-gguf/blob/main/DaringMaid-20B-V1.1.Q4_0.gguf) | Q4_0 | 10.52GB |
| [DaringMaid-20B-V1.1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Kooten_-_DaringMaid-20B-V1.1-gguf/blob/main/DaringMaid-20B-V1.1.IQ4_NL.gguf) | IQ4_NL | 10.57GB |
| [DaringMaid-20B-V1.1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Kooten_-_DaringMaid-20B-V1.1-gguf/blob/main/DaringMaid-20B-V1.1.Q4_K_S.gguf) | Q4_K_S | 10.59GB |
| [DaringMaid-20B-V1.1.Q4_K.gguf](https://huggingface.co/RichardErkhov/Kooten_-_DaringMaid-20B-V1.1-gguf/blob/main/DaringMaid-20B-V1.1.Q4_K.gguf) | Q4_K | 11.22GB |
| [DaringMaid-20B-V1.1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Kooten_-_DaringMaid-20B-V1.1-gguf/blob/main/DaringMaid-20B-V1.1.Q4_K_M.gguf) | Q4_K_M | 11.22GB |
| [DaringMaid-20B-V1.1.Q4_1.gguf](https://huggingface.co/RichardErkhov/Kooten_-_DaringMaid-20B-V1.1-gguf/blob/main/DaringMaid-20B-V1.1.Q4_1.gguf) | Q4_1 | 11.67GB |
| [DaringMaid-20B-V1.1.Q5_0.gguf](https://huggingface.co/RichardErkhov/Kooten_-_DaringMaid-20B-V1.1-gguf/blob/main/DaringMaid-20B-V1.1.Q5_0.gguf) | Q5_0 | 12.83GB |
| [DaringMaid-20B-V1.1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Kooten_-_DaringMaid-20B-V1.1-gguf/blob/main/DaringMaid-20B-V1.1.Q5_K_S.gguf) | Q5_K_S | 12.83GB |
| [DaringMaid-20B-V1.1.Q5_K.gguf](https://huggingface.co/RichardErkhov/Kooten_-_DaringMaid-20B-V1.1-gguf/blob/main/DaringMaid-20B-V1.1.Q5_K.gguf) | Q5_K | 13.18GB |
| [DaringMaid-20B-V1.1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Kooten_-_DaringMaid-20B-V1.1-gguf/blob/main/DaringMaid-20B-V1.1.Q5_K_M.gguf) | Q5_K_M | 11.66GB |
| [DaringMaid-20B-V1.1.Q5_1.gguf](https://huggingface.co/RichardErkhov/Kooten_-_DaringMaid-20B-V1.1-gguf/blob/main/DaringMaid-20B-V1.1.Q5_1.gguf) | Q5_1 | 13.98GB |
| [DaringMaid-20B-V1.1.Q6_K.gguf](https://huggingface.co/RichardErkhov/Kooten_-_DaringMaid-20B-V1.1-gguf/blob/main/DaringMaid-20B-V1.1.Q6_K.gguf) | Q6_K | 15.28GB |
| [DaringMaid-20B-V1.1.Q8_0.gguf](https://huggingface.co/RichardErkhov/Kooten_-_DaringMaid-20B-V1.1-gguf/blob/main/DaringMaid-20B-V1.1.Q8_0.gguf) | Q8_0 | 19.79GB |
Original model description:
---
license: cc-by-nc-4.0
language:
- en
pipeline_tag: text-generation
tags:
- Merge
---
# DaringMaid-20B-V1.1
## Whats New?
This is an updated version of [DaringMaid-20B](https://huggingface.co/Kooten/DaringMaid-20B), it is pretty much the same but with Noromaid-13b v0.3 instead of v0.1.1 and with a slightly higher weight for Noromaid.
I used v0.3 since it was the last to use Alpaca as to not break anything.
# Quants
EXL2: [6bpw](https://huggingface.co/Kooten/DaringMaid-20B-V1.1-6bpw-exl2), [5bpw](https://huggingface.co/Kooten/DaringMaid-20B-V1.1-5bpw-exl2), [4bpw](https://huggingface.co/Kooten/DaringMaid-20B-V1.1-4bpw-exl2), [3.5bpw](https://huggingface.co/Kooten/DaringMaid-20B-V1.1-3.5bpw-exl2), [3bpw](https://huggingface.co/Kooten/DaringMaid-20B-V1.1-3bpw-exl2)
[GGUF](https://huggingface.co/Kooten/DaringMaid-20B-V1.1-GGUF): [Q3_K_M](https://huggingface.co/Kooten/DaringMaid-20B-V1.1-GGUF/blob/main/DaringMaid-20B-V1.1-Q3_K_M.gguf) - [Q4_K_M](https://huggingface.co/Kooten/DaringMaid-20B-V1.1-GGUF/blob/main/DaringMaid-20B-V1.1-Q4_K_M.gguf) - [Q5_K_M](https://huggingface.co/Kooten/DaringMaid-20B-V1.1-GGUF/blob/main/DaringMaid-20B-V1.1-Q5_K_M.gguf) - [Q6_K_M](https://huggingface.co/Kooten/DaringMaid-20B-V1.1-GGUF/blob/main/DaringMaid-20B-V1.1-Q6_K.gguf)
## Recipe:
- [sequelbox/DynamicFactor](https://huggingface.co/sequelbox/DynamicFactor)
- [NeverSleep/Noromaid](https://huggingface.co/NeverSleep/Noromaid-13b-v0.3)
- [Undi95/Utopia](https://huggingface.co/Undi95/Utopia-13B)
- [CalderaAI/Thorns](https://huggingface.co/CalderaAI/13B-Thorns-l2)
- [Gryphe/MythoMax](https://huggingface.co/Gryphe/MythoMax-L2-13b)
# Prompt template:
I have been using Undi/Ikaris SillyTavern presets for Noromaid: [Context template](https://files.catbox.moe/l5hguf.json), [Instruct template](https://files.catbox.moe/nzygwn.json).
## Alpaca:
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Input:
{input}
### Response:
```
### Contact
Kooten on discord.
|
NikolayKozloff/polanka-qwen2-3b-v0.1-Q8_0-GGUF | NikolayKozloff | 2024-06-19T16:26:23Z | 728 | 1 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"pl",
"base_model:piotr-ai/polanka-qwen2-3b-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2024-06-19T16:26:06Z | ---
base_model: piotr-ai/polanka-qwen2-3b-v0.1
language:
- pl
license: apache-2.0
tags:
- llama-cpp
- gguf-my-repo
---
# NikolayKozloff/polanka-qwen2-3b-v0.1-Q8_0-GGUF
This model was converted to GGUF format from [`piotr-ai/polanka-qwen2-3b-v0.1`](https://huggingface.co/piotr-ai/polanka-qwen2-3b-v0.1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/piotr-ai/polanka-qwen2-3b-v0.1) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo NikolayKozloff/polanka-qwen2-3b-v0.1-Q8_0-GGUF --hf-file polanka-qwen2-3b-v0.1-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo NikolayKozloff/polanka-qwen2-3b-v0.1-Q8_0-GGUF --hf-file polanka-qwen2-3b-v0.1-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo NikolayKozloff/polanka-qwen2-3b-v0.1-Q8_0-GGUF --hf-file polanka-qwen2-3b-v0.1-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo NikolayKozloff/polanka-qwen2-3b-v0.1-Q8_0-GGUF --hf-file polanka-qwen2-3b-v0.1-q8_0.gguf -c 2048
```
|
V15h/PLLaMa-7b-instruct-Q4_K_M-GGUF | V15h | 2024-06-23T03:14:28Z | 728 | 0 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:Xianjun/PLLaMa-7b-instruct",
"license:apache-2.0",
"region:us"
] | null | 2024-06-23T03:14:10Z | ---
base_model: Xianjun/PLLaMa-7b-instruct
license: apache-2.0
tags:
- llama-cpp
- gguf-my-repo
---
# V15h/PLLaMa-7b-instruct-Q4_K_M-GGUF
This model was converted to GGUF format from [`Xianjun/PLLaMa-7b-instruct`](https://huggingface.co/Xianjun/PLLaMa-7b-instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Xianjun/PLLaMa-7b-instruct) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo V15h/PLLaMa-7b-instruct-Q4_K_M-GGUF --hf-file pllama-7b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo V15h/PLLaMa-7b-instruct-Q4_K_M-GGUF --hf-file pllama-7b-instruct-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo V15h/PLLaMa-7b-instruct-Q4_K_M-GGUF --hf-file pllama-7b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo V15h/PLLaMa-7b-instruct-Q4_K_M-GGUF --hf-file pllama-7b-instruct-q4_k_m.gguf -c 2048
```
|
markhneedham/Mistral-7B-v0.3-IQ4_NL-GGUF | markhneedham | 2024-06-23T11:42:03Z | 728 | 0 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:mistralai/Mistral-7B-v0.3",
"license:apache-2.0",
"region:us"
] | null | 2024-06-23T11:41:45Z | ---
base_model: mistralai/Mistral-7B-v0.3
license: apache-2.0
tags:
- llama-cpp
- gguf-my-repo
---
# markhneedham/Mistral-7B-v0.3-IQ4_NL-GGUF
This model was converted to GGUF format from [`mistralai/Mistral-7B-v0.3`](https://huggingface.co/mistralai/Mistral-7B-v0.3) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/mistralai/Mistral-7B-v0.3) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo markhneedham/Mistral-7B-v0.3-IQ4_NL-GGUF --hf-file mistral-7b-v0.3-iq4_nl-imat.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo markhneedham/Mistral-7B-v0.3-IQ4_NL-GGUF --hf-file mistral-7b-v0.3-iq4_nl-imat.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo markhneedham/Mistral-7B-v0.3-IQ4_NL-GGUF --hf-file mistral-7b-v0.3-iq4_nl-imat.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo markhneedham/Mistral-7B-v0.3-IQ4_NL-GGUF --hf-file mistral-7b-v0.3-iq4_nl-imat.gguf -c 2048
```
|
ayameRushia/gpt2-small-indonesia-fine-tuning-poem | ayameRushia | 2021-08-10T06:50:20Z | 727 | 1 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"id",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: id
widget:
- text: "Wahai rembulan yang tertutup awan hujan"
---
# Indonesian GPT-2 finetuned on Indonesian poems
This is the [Indonesian gpt2-small model](https://huggingface.co/flax-community/gpt2-small-indonesian) fine-tuned to Indonesian poems. The dataset can be found in [here](https://huggingface.co/datasets/id_puisi) All training was done on Google Colab Jupyter Notebook (soon).
The dataset is splitted into two subset with details belows:
| split | count (examples) | percentage |
| ---------- | ---------- | -------------- |
| train | 7,358 | 80% |
| validation | 1,890 | 20% |
### Evaluation results
The model evaluation results after 10 epochs are as follows:
| dataset | train/loss | eval/loss | eval perplexity |
| ---------- | ---------- | -------------- | ---------- |
| [id puisi](https://huggingface.co/datasets/id_puisi) | 3.324700 | 3.502665 | 33.20 |
The logs can be found in [wandb page here](https://wandb.ai/ayamerushia/gpt-2_poem/runs/36ymudz9/overview?workspace=user-ayamerushia) or tensorboard [here](https://huggingface.co/ayameRushia/gpt2-small-indonesia-fine-tuning-poem/tensorboard)
|
Sakonii/distilgpt2-nepali | Sakonii | 2023-03-21T10:18:06Z | 727 | 6 | transformers | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:Sakonii/nepalitext-language-model-dataset",
"arxiv:1911.02116",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2022-03-30T15:40:23Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets: Sakonii/nepalitext-language-model-dataset
widget:
- text: नेपाल र भारतबीच
example_title: Example 1
- text: प्रधानमन्त्री
example_title: Example 2
- text: 'दस वर्ष लामो '
example_title: Example 3
- text: 'जापानमा आज '
example_title: Example 4
- text: नेपालका धेरैजसो चाडपर्वहरूमध्ये,
example_title: Example 5
model-index:
- name: distilgpt2-nepali
results: []
---
# distilgpt2-nepali
This model is pre-trained on [nepalitext](https://huggingface.co/datasets/Sakonii/nepalitext-language-model-dataset) dataset consisting of over 13 million Nepali text sequences using a Causal language modeling (CLM) objective. Our approach trains a Sentence Piece Model (SPM) for text tokenization similar to [XLM-ROBERTa](https://arxiv.org/abs/1911.02116) and trains [distilgpt2](https://huggingface.co/distilgpt2) for language modeling.
It achieves the following results on the evaluation set:
| Training Loss | Validation Loss | Perplexity
|:-------------:|:---------------:|:----------:|
| 3.3968 | 3.2705 | 26.3245
## Model description
Refer to original [distilgpt2](https://huggingface.co/distilgpt2)
## Intended uses & limitations
This raw model can be used for Nepali text generation and intends to be fine-tuned on Nepali language focused downstream task.
The language model being trained on a data with texts grouped to a block size of 512, it handles text sequence up to 512 tokens and may not perform satisfactorily on shorter sequences.
## Usage
This model can be used directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility:
```python
>>> from transformers import pipeline, set_seed
>>> set_seed(42)
>>> generator = pipeline('text-generation', model='Sakonii/distilgpt2-nepali')
>>> generator("नेपालका धेरैजसो चाडपर्वहरूमध्ये,", max_length=30, num_return_sequences=5)
Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.
[{'generated_text': 'नेपालका धेरैजसो चाडपर्वहरूमध्ये, तिहार र छठपर्व विशेष रूपमा मनाइने भएकाले नेपाली मौलिक पर्व पनि हो । हिन्दू धर्म र संस्कृतिक... काठमाडौं ।'},
{'generated_text': 'नेपालका धेरैजसो चाडपर्वहरूमध्ये, तिहारको मुख्य दिन आज साँझ अस्ताउँदो सूर्यलाई अर्घ्य दिइएको छ । वैदिक विधि...विस्तृतमा पढ्नुस् काठमाडौं । नेपाल चिकित्सक संघका'},
{'generated_text': 'नेपालका धेरैजसो चाडपर्वहरूमध्ये, चाडपर्व, विवाह,... नेपाली काँग्रेसका प्रवक्ता विश्वप्रकाश शर्माले पार्टीभित्र आन्तरिक झगडा हुने निश्चित भएको र गुटबन्दीका कारण चुनावमा हार बेहोर्नु'},
{'generated_text': 'नेपालका धेरैजसो चाडपर्वहरूमध्ये, दशैं नेपालीहरूको मौलिक पर्वका रूपमा मनाउँछन् । नेपालीहरूको दोस्रो महान् पर्व तिहार हो । तिहारले दाजुभाइ तथा दिदीबहिनीहरूको बीचमा प्रगाढ सम्बन्ध स्थापित'},
{'generated_text': 'नेपालका धेरैजसो चाडपर्वहरूमध्ये, माघे संक्रान्ति र माघे संक्रान्तिमा माघे संक्रान्तिमा मात्र नभएर फागुन महिनाभर नै विशेष महत्व रहने गरेको छ । काठमाडौं ।'}]
```
Here is how we can use the model to get the features of a given text in PyTorch:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained('Sakonii/distilgpt2-nepali')
model = AutoModelForCausalLM.from_pretrained('Sakonii/distilgpt2-nepali')
# prepare input
text = "चाहिएको text यता राख्नु होला।"
encoded_input = tokenizer(text, return_tensors='pt')
# forward pass
output = model(**encoded_input)
```
## Training data
This model is trained on [nepalitext](https://huggingface.co/datasets/Sakonii/nepalitext-language-model-dataset) language modeling dataset which combines the datasets: [OSCAR](https://huggingface.co/datasets/oscar) , [cc100](https://huggingface.co/datasets/cc100) and a set of scraped Nepali articles on Wikipedia.
As for training the language model, the texts are tokenized using Sentence Piece Model (SPM), a vocabulary size of 24,576 and texts are are grouped to a block of 512 tokens.
## Training procedure
The model is trained with the same configuration as the original [distilgpt2](https://huggingface.co/distilgpt2); but with 512 tokens per instance, 12 instances per batch, and around 188.8K training steps.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Perplexity |
|:-------------:|:-----:|:------:|:---------------:|:----------:|
| 3.7645 | 1.0 | 94395 | 3.6291 | 37.6789 |
| 3.5857 | 2.0 | 188790 | 3.4442 | 31.3182 |
| 3.505 | 3.0 | 283185 | 3.3749 | 29.2214 |
| 3.4688 | 4.0 | 377580 | 3.3439 | 28.3294 |
| 3.3968 | 5.0 | 471975 | 3.2705 | 26.3245 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.9.1
- Datasets 2.0.0
- Tokenizers 0.11.6
|
saftle/urpm | saftle | 2023-02-26T13:13:43Z | 727 | 16 | diffusers | [
"diffusers",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-02-26T12:37:00Z | ---
license: creativeml-openrail-m
---
|
eunyounglee/got-neo-var-ppo | eunyounglee | 2023-09-04T06:36:55Z | 727 | 0 | transformers | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-09-04T06:36:09Z | Entry not found |
hfl/chinese-alpaca-2-13b-gguf | hfl | 2024-01-24T03:33:21Z | 727 | 10 | null | [
"gguf",
"zh",
"en",
"license:apache-2.0",
"region:us"
] | null | 2023-11-16T05:18:19Z | ---
license: apache-2.0
language:
- zh
- en
---
# Chinese-Alpaca-2-13B-GGUF
This repository contains the GGUF-v3 models (llama.cpp compatible) for **Chinese-Alpaca-2-13B**.
## Performance
Metric: PPL, lower is better
| Quant | original | imatrix (`-im`) |
|-----|------|------|
| Q2_K | 13.7636 +/- 0.19446 | 20.6803 +/- 0.31594 |
| Q3_K | 9.5388 +/- 0.13078 | 9.1016 +/- 0.12565 |
| Q4_0 | 9.1694 +/- 0.12668 | - |
| Q4_K | 8.6633 +/- 0.11957 | 8.6377 +/- 0.11932 |
| Q5_0 | 8.6745 +/- 0.12020 | - |
| Q5_K | 8.5161 +/- 0.11796 | 8.5210 +/- 0.11803 |
| Q6_K | 8.4943 +/- 0.11759 | 8.5011 +/- 0.11775 |
| Q8_0 | 8.4595 +/- 0.11718 | - |
| F16 | 8.4550 +/- 0.11713 | - |
*The model with `-im` suffix is generated with important matrix, which has generally better performance (not always though).*
## Others
For Hugging Face version, please see: https://huggingface.co/hfl/chinese-alpaca-2-13b
Please refer to [https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/) for more details. |
SakuraLLM/Sakura-13B-LNovel-v0.8-GGUF | SakuraLLM | 2023-12-04T07:37:18Z | 727 | 0 | null | [
"gguf",
"license:apache-2.0",
"region:us"
] | null | 2023-11-19T17:29:13Z | ---
license: apache-2.0
---
|
EmbeddedLLM/Mistral-7B-Merge-14-v0.3 | EmbeddedLLM | 2024-01-21T05:01:50Z | 727 | 6 | transformers | [
"transformers",
"pytorch",
"safetensors",
"mistral",
"text-generation",
"merge",
"en",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"base_model:ehartford/dolphin-2.2.1-mistral-7b",
"base_model:SciPhi/SciPhi-Mistral-7B-32k",
"base_model:ehartford/samantha-1.2-mistral-7b",
"base_model:Arc53/docsgpt-7b-mistral",
"base_model:HuggingFaceH4/zephyr-7b-beta",
"base_model:meta-math/MetaMath-Mistral-7B",
"base_model:Open-Orca/Mistral-7B-OpenOrca",
"base_model:openchat/openchat-3.5-1210",
"base_model:beowolx/MistralHermes-CodePro-7B-v1",
"base_model:TIGER-Lab/MAmmoTH-7B-Mistral",
"base_model:teknium/OpenHermes-2.5-Mistral-7B",
"base_model:Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp",
"base_model:mlabonne/NeuralHermes-2.5-Mistral-7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2023-12-19T06:03:12Z | ---
license: apache-2.0
language:
- en
tags:
- merge
base_model:
- mistralai/Mistral-7B-Instruct-v0.2
- ehartford/dolphin-2.2.1-mistral-7b
- SciPhi/SciPhi-Mistral-7B-32k
- ehartford/samantha-1.2-mistral-7b
- Arc53/docsgpt-7b-mistral
- HuggingFaceH4/zephyr-7b-beta
- meta-math/MetaMath-Mistral-7B
- Open-Orca/Mistral-7B-OpenOrca
- openchat/openchat-3.5-1210
- beowolx/MistralHermes-CodePro-7B-v1
- TIGER-Lab/MAmmoTH-7B-Mistral
- teknium/OpenHermes-2.5-Mistral-7B
- Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp
- mlabonne/NeuralHermes-2.5-Mistral-7B
---
# Update 2024-01-03
Check out our [v0.4 model](https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.4) which is based on this and achieves better average score of 71.19 versus 69.66.
# Model Description
This is an update to [EmbeddedLLM/Mistral-7B-Merge-14-v0.2](https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.2) that removes
potentially TruthfulQA-contaminated models and non-commercially licensed models:
1. [berkeley-nest/Starling-LM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha)
2. [Q-bert/MetaMath-Cybertron-Starling](https://huggingface.co/Q-bert/MetaMath-Cybertron-Starling)
3. [v1olet/v1olet_marcoroni-go-bruins-merge-7B](https://huggingface.co/v1olet/v1olet_marcoroni-go-bruins-merge-7B)
This is an experiment to test merging 14 models using DARE TIES 🦙
The result is a base model that performs quite well but may need some further chat fine-tuning.
The 14 models are as follows:
1. [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
2. [ehartford/dolphin-2.2.1-mistral-7b](https://huggingface.co/ehartford/dolphin-2.2.1-mistral-7b)
3. [SciPhi/SciPhi-Mistral-7B-32k](https://huggingface.co/SciPhi/SciPhi-Mistral-7B-32k)
4. [ehartford/samantha-1.2-mistral-7b](https://huggingface.co/ehartford/samantha-1.2-mistral-7b)
5. [Arc53/docsgpt-7b-mistral](https://huggingface.co/Arc53/docsgpt-7b-mistral)
6. [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)
7. [meta-math/MetaMath-Mistral-7B](https://huggingface.co/meta-math/MetaMath-Mistral-7B)
8. [Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca)
9. [openchat/openchat-3.5-1210](https://huggingface.co/openchat/openchat-3.5-1210)
10. [beowolx/MistralHermes-CodePro-7B-v1](https://huggingface.co/beowolx/MistralHermes-CodePro-7B-v1)
11. [TIGER-Lab/MAmmoTH-7B-Mistral](https://huggingface.co/TIGER-Lab/MAmmoTH-7B-Mistral)
12. [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B)
13. [Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp](https://huggingface.co/Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp)
14. [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B)
- base model: [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
## Open LLM Leaderboard
| | v0.3 | v0.4 |
|------------|-------|-------|
| Average | 69.66 | 71.19 |
| ARC | 65.96 | 66.81 |
| HellaSwag | 85.29 | 86.15 |
| MMLU | 64.35 | 65.10 |
| TruthfulQA | 57.80 | 58.25 |
| Winogrande | 78.30 | 80.03 |
| GSM8K | 66.26 | 70.81 |
## Chat Template
We tried ChatML and Llama-2 chat template, but feel free to try other templates.
## Merge Configuration
The merge config file for this model is here:
```yaml
models:
- model: mistralai/Mistral-7B-v0.1
# no parameters necessary for base model
- model: ehartford/dolphin-2.2.1-mistral-7b
parameters:
weight: 0.08
density: 0.4
- model: SciPhi/SciPhi-Mistral-7B-32k
parameters:
weight: 0.08
density: 0.4
- model: ehartford/samantha-1.2-mistral-7b
parameters:
weight: 0.08
density: 0.4
- model: Arc53/docsgpt-7b-mistral
parameters:
weight: 0.08
density: 0.4
- model: HuggingFaceH4/zephyr-7b-beta
parameters:
weight: 0.08
density: 0.4
- model: meta-math/MetaMath-Mistral-7B
parameters:
weight: 0.08
density: 0.4
- model: Open-Orca/Mistral-7B-OpenOrca
parameters:
weight: 0.08
density: 0.4
- model: openchat/openchat-3.5-1210
parameters:
weight: 0.08
density: 0.4
- model: beowolx/MistralHermes-CodePro-7B-v1
parameters:
weight: 0.08
density: 0.4
- model: TIGER-Lab/MAmmoTH-7B-Mistral
parameters:
weight: 0.08
density: 0.4
- model: teknium/OpenHermes-2.5-Mistral-7B
parameters:
weight: 0.08
density: 0.4
- model: Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp
parameters:
weight: 0.08
density: 0.4
- model: mlabonne/NeuralHermes-2.5-Mistral-7B
parameters:
weight: 0.08
density: 0.4
- model: mistralai/Mistral-7B-Instruct-v0.2
parameters:
weight: 0.08
density: 0.5
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
int8_mask: true
dtype: bfloat16
``` |
nisten/shqiponja-59b-v1 | nisten | 2024-01-26T01:00:40Z | 727 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"frankenstein",
"merge",
"conversational",
"base_model:jondurbin/nontoxic-bagel-34b-v0.2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-01-13T05:27:34Z | ---
base_model:
- jondurbin/nontoxic-bagel-34b-v0.2
tags:
- mergekit
- frankenstein
- merge
license: mit
---
# Shqiponja-59 V1
This is an untrained experimental 59B merged model.
Picked these two models specifically to compliment each others strengths.
### Models Merged
* NousResearch/Nous-Hermes-2-Yi-34B
* jondurbin/nontoxic-bagel-34b-v0.2
Merged using the Undi95 style passthrough merge method.
### The secret sauce
The following YAML configuration was used to produce this model:
```yaml
dtype: bfloat16
merge_method: passthrough
slices:
- sources:
- layer_range: [0, 52]
model: /home/admin/nv1/nontoxic-bagel-34b-v0.2
- sources:
- layer_range: [8, 60]
model: /home/admin/nv1/Nous-Hermes-2-Yi-34B
```
# License MIT - Enjoy |
kodonho/Momo-70b-DPO-mixed | kodonho | 2024-01-24T03:18:51Z | 727 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"base_model:moreh/MoMo-70B-lora-1.8.6-DPO",
"base_model:moreh/MoMo-70B-lora-1.8.4-DPO",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-01-17T05:31:31Z | ---
license: mit
tags:
- mergekit
- merge
base_model:
- moreh/MoMo-70B-lora-1.8.6-DPO
- moreh/MoMo-70B-lora-1.8.4-DPO
---
# MoMo-70B-lora-1.8.6-DPO based model with gradient slerp
This is an English mixed Model based on
* [moreh/MoMo-70B-lora-1.8.6-DPO]
gpu code example
```
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import math
## v2 models
model_path = "kodonho/kodonho/Momo-70b-DPO-mixed"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_default_system_prompt=False)
model = AutoModelForCausalLM.from_pretrained(
model_path, torch_dtype=torch.float32, device_map='auto',local_files_only=False, load_in_4bit=True
)
print(model)
prompt = input("please input prompt:")
while len(prompt) > 0:
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to("cuda")
generation_output = model.generate(
input_ids=input_ids, max_new_tokens=500,repetition_penalty=1.2
)
print(tokenizer.decode(generation_output[0]))
prompt = input("please input prompt:")
``` |
TeeZee/Buttocks-7B-v1.0 | TeeZee | 2024-03-04T14:38:03Z | 727 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"not-for-all-audiences",
"merge",
"license:cc-by-nc-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-01-18T19:52:52Z | ---
license: cc-by-nc-4.0
tags:
- not-for-all-audiences
- merge
model-index:
- name: Buttocks-7B-v1.0
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: 54.61
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/Buttocks-7B-v1.0
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: 75.61
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/Buttocks-7B-v1.0
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: 50.22
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/Buttocks-7B-v1.0
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: 44.72
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/Buttocks-7B-v1.0
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: 68.9
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/Buttocks-7B-v1.0
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: 5.76
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/Buttocks-7B-v1.0
name: Open LLM Leaderboard
---
### Buttocks 7B v1.0 ###
An experiment that has gone very, very wrong.
### Model details ###
- Recreation of the original recipe for [Undi95/Toppy-M-7B](https://huggingface.co/Undi95/Toppy-M-7B), but instead of final merge done by mergekit, [MergeMoster](https://github.com/Gryphe/MergeMonster/) was used with extended RPG preset.
- recipe in [mergekit-config](https://huggingface.co/TeeZee/Toppy-7B-remake-mergemonster-SLERP-v1.0/resolve/main/toppy-slerp-merge-config.yml), stepsAA, BB, CC are the original models with LORAS as per Toppy M 7B sauce.
- SLERP merge method was used
### Results ###
- in simple terms this model is totally unhinged
- it always produces sequences similar to fever dreams or drug trips
- on a good day it can produce scenarios similar to old Monty Python sketches
- models shows incredible affinity to words like 'ass', 'buttocks', 'farts', prompting with those single words will probably
produce a whole story revolving around those topics.
### Possible uses ###
- to generate dream sequence in a story
- to make the boring model more unpredictable by merging at low weights with this monster
- to take a break, connect Silly Tavern to this model and get a few ROTFLs observing how every story deteriorates into pure craziness
- research on LLM hallucinations
# [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_TeeZee__Buttocks-7B-v1.0)
| Metric |Value|
|---------------------------------|----:|
|Avg. |49.97|
|AI2 Reasoning Challenge (25-Shot)|54.61|
|HellaSwag (10-Shot) |75.61|
|MMLU (5-Shot) |50.22|
|TruthfulQA (0-shot) |44.72|
|Winogrande (5-shot) |68.90|
|GSM8k (5-shot) | 5.76|
|
ycros/BagelMIsteryTour-v2-8x7B | ycros | 2024-01-27T11:40:20Z | 727 | 16 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"mergekit",
"merge",
"arxiv:2311.03099",
"arxiv:2306.01708",
"base_model:mistralai/Mixtral-8x7B-v0.1",
"base_model:jondurbin/bagel-dpo-8x7b-v0.2",
"base_model:Sao10K/Sensualize-Mixtral-bf16",
"base_model:Doctor-Shotgun/limarp-zloss-mixtral-8x7b-qlora",
"base_model:mistralai/Mixtral-8x7B-Instruct-v0.1",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-01-19T04:48:19Z | ---
base_model:
- mistralai/Mixtral-8x7B-v0.1
- jondurbin/bagel-dpo-8x7b-v0.2
- Sao10K/Sensualize-Mixtral-bf16
- mistralai/Mixtral-8x7B-v0.1
- Doctor-Shotgun/limarp-zloss-mixtral-8x7b-qlora
- mistralai/Mixtral-8x7B-Instruct-v0.1
tags:
- mergekit
- merge
license: cc-by-nc-4.0
---
# BagelMIsteryTour-v2-8x7B
[GGUF versions here](https://huggingface.co/ycros/BagelMIsteryTour-v2-8x7B-GGUF)
[AWQ versions here](https://huggingface.co/ycros/BagelMIsteryTour-v2-8x7B-AWQ)
Bagel, Mixtral Instruct, with extra spices. Give it a taste. Works with Alpaca prompt formats, though the Mistral format should also work.
I started experimenting around seeing if I could improve or fix some of Bagel's problems. Totally inspired by seeing how well Doctor-Shotgun's Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss worked (which is a LimaRP tune on top of base Mixtral, and then merged with Mixtral Instruct) - I decided to try some merges of Bagel with Mixtral Instruct as a result.
Somehow I ended up here, Bagel, Mixtral Instruct, a little bit of LimaRP, a little bit of Sao10K's Sensualize. So far in my testing it's working very well, and while it seems fairly unaligned on a lot of stuff, it's maybe a little too aligned on a few specific things (which I think comes from Sensualize) - so that's something to play with in the future, or maybe try to DPO out.
I've been running (temp last) minP 0.1, dynatemp 0.5-4, rep pen 1.07, rep range 1024. I've been testing Alpaca style Instruction/Response, and Instruction/Input/Response and those seem to work well, I expect Mistral's prompt format would also work well. You may need to add a stopping string on "{{char}}:" for RPs because it can sometimes duplicate those out in responses and waffle on. Seems to hold up and not fall apart at long contexts like Bagel and some other Mixtral tunes seem to, definitely doesn't seem prone to loopyness either. Can be pushed into extravagant prose if the scene/setting calls for it.
__Version 2:__ lowered the mix of Sensualize.
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) as a base.
### Models Merged
The following models were included in the merge:
* [jondurbin/bagel-dpo-8x7b-v0.2](https://huggingface.co/jondurbin/bagel-dpo-8x7b-v0.2)
* [Sao10K/Sensualize-Mixtral-bf16](https://huggingface.co/Sao10K/Sensualize-Mixtral-bf16)
* [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) + [Doctor-Shotgun/limarp-zloss-mixtral-8x7b-qlora](https://huggingface.co/Doctor-Shotgun/limarp-zloss-mixtral-8x7b-qlora)
* [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
base_model: mistralai/Mixtral-8x7B-v0.1
models:
- model: mistralai/Mixtral-8x7B-v0.1+Doctor-Shotgun/limarp-zloss-mixtral-8x7b-qlora
parameters:
density: 0.5
weight: 0.2
- model: Sao10K/Sensualize-Mixtral-bf16
parameters:
density: 0.5
weight: 0.1
- model: mistralai/Mixtral-8x7B-Instruct-v0.1
parameters:
density: 0.6
weight: 1.0
- model: jondurbin/bagel-dpo-8x7b-v0.2
parameters:
density: 0.6
weight: 0.5
merge_method: dare_ties
dtype: bfloat16
```
|
CultriX/CultriX-MoE-BF16 | CultriX | 2024-01-20T17:33:47Z | 727 | 0 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"moe",
"frankenmoe",
"merge",
"mergekit",
"lazymergekit",
"mlabonne/NeuralBeagle14-7B",
"fblgit/UNA-dolphin-2.6-mistral-7b-dpo-laser",
"mlabonne/Marcoro14-7B-slerp",
"base_model:mlabonne/NeuralBeagle14-7B",
"base_model:fblgit/UNA-dolphin-2.6-mistral-7b-dpo-laser",
"base_model:mlabonne/Marcoro14-7B-slerp",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-01-20T17:23:56Z | ---
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- mlabonne/NeuralBeagle14-7B
- fblgit/UNA-dolphin-2.6-mistral-7b-dpo-laser
- mlabonne/Marcoro14-7B-slerp
base_model:
- mlabonne/NeuralBeagle14-7B
- fblgit/UNA-dolphin-2.6-mistral-7b-dpo-laser
- mlabonne/Marcoro14-7B-slerp
---
# CultriX-MoE-BF16
CultriX-MoE-BF16 is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B)
* [fblgit/UNA-dolphin-2.6-mistral-7b-dpo-laser](https://huggingface.co/fblgit/UNA-dolphin-2.6-mistral-7b-dpo-laser)
* [mlabonne/Marcoro14-7B-slerp](https://huggingface.co/mlabonne/Marcoro14-7B-slerp)
## 🧩 Configuration
```yaml
base_model: "EmbeddedLLM/Mistral-7B-Merge-14-v0.2"
gate_mode: hidden
dtype: bfloat16
experts:
- source_model: "mlabonne/NeuralBeagle14-7B"
positive_prompts:
- "Create a story based on"
- "Debate the topic of"
- "Come up with some arguments"
- "Provide me with instructions on"
- "Interpret the sentiment"
- "Interpret and execute these cooking instructions"
- "Craft a persuasive argument"
- "Analyze the motivations"
- "Construct a detailed plan for"
- "Narrate an event from multiple perspectives."
- "Formulate a response"
- "Write a script for a short play"
- "Generate a sequence of instructions to teach a skill."
- "Solve this riddle"
- "Create an engaging story"
- "Write a fictional"
- "Propose a solution to a social issue"
- "Develop a dialogue"
- "Create a step-by-step guide"
- "Devise a strategy"
- "Write a narrative"
- "Tell me how to"
- "Explain the concept of"
- "Give an overview of"
- "Compare and contrast between"
- "Provide information about"
- "Help me understand"
- "Summarize"
- "Make a recommendation on"
- "Answer this question"
- "How do you approach"
- "Explain the concept of"
- "Give an overview of"
- "Provide information about"
- "Help me understand the principles of"
- "Summarize the key components of"
- "Make a recommendation on how to"
- "Answer this question:"
negative_prompts:
- "Provide in-depth information about quantum computing."
- "Explain the inner workings of an internal combustion engine."
- "Give a detailed tutorial on advanced calculus."
- "Summarize the latest research in genetic engineering."
- "Interpret financial markets and stock trends."
- "Analyze the chemical composition of"
- "Develop a blueprint for."
- "Offer a critique of a modern art piece."
- "Provide a technical review of"
- "Conduct a linguistic analysis of an ancient language."
- "Write a user manual for advanced medical equipment."
- "Give a step-by-step guide on piloting an aircraft."
- "Conduct an in-depth analysis of this code"
- "Explain the physics behind black holes."
- "Provide a strategy for managing a cyber attack"
- "Develop an algorithm for predictive analytics in finance."
- "Provide information about advanced programming algorithms."
- "Help me understand the details of this code"
- "Summarize the process of cellular respiration."
- "Improve the security of"
- "What are the latest advancements in artificial intelligence?"
- "Provide detailed technical coding solutions."
- "Analyze complex scientific data and statistics."
- "Offer medical diagnoses based on symptoms."
- "Conduct a detailed financial audit of a company."
- "Perform real-time translation of multiple languages."
- "Create high-resolution graphic designs."
- "Develop complex mathematical proofs."
- "Offer legal advice on specific cases."
- "Write a detailed manual on advanced mechanical engineering."
- "Conduct an in-depth psychological assessment."
- "Perform a security analysis of a computer network."
- "Compose an original piece of music."
- "Plan and execute a scientific experiment."
- "Provide professional career counseling."
- "Develop a complex database management system."
- "Write a software program for data analysis."
- "Give expert advice on cyber"
- "Conduct a pentesting security audit"
- source_model: "fblgit/UNA-dolphin-2.6-mistral-7b-dpo-laser"
positive_prompts:
- "Provide step-by-step coding instructions for..."
- "Draft a function with detailed steps in [language]"
- "Guide me through coding a simple [type of application or script]"
- "Recommend best practices for code implementation in [context]"
- "Generate a regex pattern for extracting [specific data]"
- "Create a regex for matching [pattern]"
- "Explain the purpose of this regex pattern"
- "Compose regex for [specific use case]"
- "Annotate this code with detailed comments for each line"
- "Add explanatory comments to this script"
- "Comment on each part of this code for clarity"
- "Develop a script to [accomplish task]"
- "Design a database schema for [specific use case]"
- "Outline secure methods for [specific operation]"
- "Guide on optimizing [specific aspect] in this code"
- "Refactor this code for better readability and efficiency"
- "Compare and contrast these code snippets"
- "Identify the programming language of this snippet"
- "Demonstrate the usage of [specific tool/library/API]"
- "Show implementation steps for this [feature/concept]"
- "Teach how to use [specific tool/library/framework]"
- "Generate a README file for this project"
- "Create a manual page for [specific tool/command]"
- "Produce comprehensive documentation for this code"
- "Build detailed documentation for [specific module]"
- "Explain the underlying concept of this code snippet"
- "Propose enhancements for this script"
- "Suggest improvements for this API call integration"
- "Diagnose and solve this coding issue"
- "Demonstrate robust error handling in this code"
- "Debug and resolve issues in this script"
- "Design a user-friendly GUI for this script's functionality"
- "Detail the deployment process for this application"
- "Deploy an app designed to [perform function]"
- "Set up a web service for [specific purpose]"
- "Develop a website with [specific features]"
- "Craft a webpage showcasing [specific content]"
- "Illustrate data flow in this code architecture"
- "Convert this code from [language A] to [language B]"
- "Translate this script into [different programming language]"
- "Explain resource management techniques in [context]"
- "Build a basic API endpoint for [functionality]"
- "Strategies to enhance scalability in [context]"
- "Conduct a security review for this code"
- "Enhance security measures in [application/module]"
- "Set up a development environment for [language/framework]"
- "Visualize data from [specific dataset]"
- "Generate a dataset for [specific use case]"
- "Scripting guide for automating [task/process]"
- "Utilize this code for [specific purpose]"
- "Principles of object-oriented programming in [language]"
- "Create a mobile-responsive layout for this web app"
- "Explain the debugging process for this code"
- "Compose code to accomplish [task]"
- "Guidance on writing code for [specific purpose]"
- "I need a script for [specific function]"
- "Clarify the functionality of this code"
- "What is the purpose of this code segment?"
- "Enhance this code for [specific improvement]"
- "Develop a program that [solves problem]"
- "Code needed for [specific task]"
- "Program a solution for [problem statement]"
- "Enhance this function's performance by..."
- "Refactor code for better readability in [context]"
- "Craft a custom function for [specific requirement]"
- "Reduce computational complexity in this algorithm by..."
- "Extend the codebase to include [new feature]"
- "Incorporate this API into an existing application"
- "Assist in troubleshooting and bug fixing for [issue]"
- "Review and prep this code for deployment"
- "Analyze error logs for potential issues in [context]"
- "Create unit tests for [module/component]"
- "Evaluate methodologies for [problem-solving]"
- "Research [topic] online"
- "Utilize the [plugin/tool] to achieve [result]"
- "Design an efficient search algorithm for [data type]"
- "Create a web crawler for [specific data extraction]"
- "Application of web sockets in [real-time scenario]"
- "Guide to integrating a third-party library in [framework]"
- "Best practices in API design for [application type]"
negative_prompts:
- "Provide a detailed analysis of historical events."
- "Give medical advice for treating a specific illness."
- "Write a comprehensive review of a novel."
- "Explain legal implications of a contract."
- "Develop a marketing strategy for a new product."
- "Offer financial advice for stock investments."
- "Create a recipe for a gourmet dish."
- "Teach a foreign language lesson."
- "Compose a symphony or musical piece."
- "Provide workout plans and fitness coaching."
- "Conduct a psychological analysis of a character."
- "Write a script for a movie or play."
- "Design a blueprint for architectural structures."
- "Give a tutorial on how to paint a landscape."
- "Explain quantum physics theories."
- "Offer career counseling and resume writing tips."
- "Teach how to repair a car engine."
- "Plan a travel itinerary for a world tour."
- "Guide on how to grow organic vegetables."
- "Discuss political strategies for an election campaign."
- source_model: "mlabonne/Marcoro14-7B-slerp"
positive_prompts:
- "Generate a creative story based on these keywords."
- "Explain a complex topic in simple terms"
- "Provide a detailed summary of"
- "Answer this question with factual accuracy"
- "Explain the historical significance of"
- "Provide a truthful and detailed account of"
- "Develop a strategy for solving a practical problem."
- "Explain the reasoning behind"
- "Provide an analysis of a moral dilemma with possible solutions."
negative_prompts:
- "imathematical problem-solving."
- "scientific theory explanations."
- "high-level abstract reasoning tasks."
- "professional advice in specialized fields like law or medicine."
- "provide me with a coding solution for"
- "Academic research"
```
## 💻 Usage
```python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "CultriX/CultriX-MoE-BF16"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)
messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
ChuckMcSneed/PMaxxxer-v1-70b | ChuckMcSneed | 2024-02-03T09:55:00Z | 727 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-01-21T02:07:01Z | ---
license: llama2
tags:
- merge
- mergekit
---
# BABE WAKE UP NEW MEME MODELS JUST DROPPED
Ladies and Gentlemen!
I present to you
*drum roll*
THE BENCHBREAKERS!
- [PMaxxxer](https://huggingface.co/ChuckMcSneed/PMaxxxer-v1-70b) (The Good)
- [SMaxxxer](https://huggingface.co/ChuckMcSneed/SMaxxxer-v1-70b) (The Bad)
- [BenchmaxxxerPS](https://huggingface.co/ChuckMcSneed/BenchmaxxxerPS-v1-123b) (The Ugly)
These three **interesting** models were designed in attempt to break [my own meme benchmark](https://huggingface.co/datasets/ChuckMcSneed/NeoEvalPlusN_benchmark) and well... they failed. The results are interesting nontheless.
# SMAXXXER
The aggressor, the angry and dumb hobo that will roleplay with you. This meme model was designed to break the stylized writing test, and it kinda did, still can't surpass ChatGPT though.
For its creation [lzlv](https://huggingface.co/lizpreciatior/lzlv_70b_fp16_hf) was TIES-merged with [spicyboros](https://huggingface.co/jondurbin/spicyboros-70b-2.2), [xwin](https://huggingface.co/Xwin-LM/Xwin-LM-70B-V0.1) and [dolphin](https://huggingface.co/cognitivecomputations/dolphin-2.2-70b) using [mergekit](https://github.com/cg123/mergekit).
# PMAXXXER
The overly politically correct SJW university dropout, the failed writer that's not really good at anything. This meme model was designed to break the poems test and it's an absolute failure.
For its creation [WinterGoddess](https://huggingface.co/Sao10K/WinterGoddess-1.4x-70B-L2) was TIES-merged with [euryale](https://huggingface.co/Sao10K/Euryale-1.3-L2-70B), [xwin](https://huggingface.co/Xwin-LM/Xwin-LM-70B-V0.1) and [dolphin](https://huggingface.co/cognitivecomputations/dolphin-2.2-70b) using [mergekit](https://github.com/cg123/mergekit).
# BENCHMAXXXER PS
The true meme model. Goliath-style frankenmerge of SMAXXXER and PMAXXXER. You might think: "Oh it's a frankenmerge, the characteristics of the models will even out, right?" This is completely wrong in this case, here characteristics of the models add up. You get an angry hobo stuck with an SJW in the same fucking body! It will assault you and then immediately apologize for it! Then it will assault you again! And apologize again! Kinda funny. It also has a bit different writing style compared to Goliath.
Is it worth using over Goliath? Not really. However, if you have fast internet and patience to try a 123b meme model, go for it!
# FAILED MODELS(not gonna upload)
## BENCHMAXXXER SP
Frankenmerge of SMAXXXER and PMAXXXER, just like BENCHMAXXXER PS, but in different order. Has severe brain damage, clearly the influence of the hobo is strong in this one.
## BENCHMAXXXER SS
Self-merge of SMAXXXER, a bit less dumb and a bit less aggresive than the original SMAXXER.
## BENCHMAXXXER MOE
2x70B MOE merge of SMAXXXER and PMAXXXER, unremarkable. Not smart, not angry. Just averaged out.
# PROMPT FORMAT
Alpaca.
```
### Instruction:
{instruction}
### Input:
{input}
### Response:
```
# Benchmarks
## NeoEvalPlusN
[My meme benchmark](https://huggingface.co/datasets/ChuckMcSneed/NeoEvalPlusN_benchmark) which the models were designed to break.
| Test name | goliath-120b |PMaxxxer-v1-70b |SMaxxxer-v1-70b |BenchmaxxxerPS-v1-123b |BenchmaxxxerSP-v1-123b |BenchmaxxxerSS-v1-123b |BenchmaxxxerMOE-v1-123b |
| -------- | ------- | -------- | ------- | -------- | ------- | ------- | -------- |
| B | 3 | 3 |2 |3 |1.5 |1.5|2|
| C | 2 | 1 |1 |2 |2 |2|1|
| D | 1 | 1 |0 |1 |1 |0.5|3|
| S | 5 | 6.75 |7.25 |7.25 |6.75 |6.5|7.25|
| P | 6 | 4.75 |4.25 |5.25 |5.25 |5.5|5|
| Total | 17 | 16.5 |14.5 |18.5 |16.5 |16|18.25|
## Open LLM leaderboard
[Leaderboard on Huggingface](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
|Model |Average|ARC |HellaSwag|MMLU |TruthfulQA|Winogrande|GSM8K|
|---------------------------------------|-------|-----|---------|-----|----------|----------|-----|
|PMaxxxer-v1-70b |72.41 |71.08|87.88 |70.39|59.77 |82.64 |62.7 |
|SMaxxxer-v1-70b |72.23 |70.65|88.02 |70.55|60.7 |82.87 |60.58|
|Difference |0.18 |0.43 |-0.14 |-0.16|-0.93 |-0.23 |2.12 |
Performance here is decent. It was #5 on the leaderboard among 70b models when I submitted it. This leaderboard is currently quite useless though, some 7b braindead meme merges have high scores there, claiming to be the next GPT4. At least I don't pretend that my models aren't a meme. |
Technoculture/Medmerge-tulu-70b | Technoculture | 2024-02-09T17:21:54Z | 727 | 3 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"epfl-llm/meditron-70b",
"allenai/tulu-2-dpo-70b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-01-21T14:39:04Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- epfl-llm/meditron-70b
- allenai/tulu-2-dpo-70b
---
# Medmerge-tulu-70b
Medmerge-tulu-70b is a merge of the following models:
* [wanglab/ClinicalCamel-70B](https://huggingface.co/wanglab/ClinicalCamel-70B)
* [epfl-llm/meditron-70b](https://huggingface.co/epfl-llm/meditron-70b)
* [allenai/tulu-2-dpo-70b](https://huggingface.co/allenai/tulu-2-dpo-70b)
# Open LLM Leaderboard
| Model Name | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
| -------------------- | -------- | --------- | ------ | ---------- | ---------- | -------- |
| tulu-2-dpo-70b | 72.1 | 88.99 | 69.84 | 65.78 | 83.27 | 62.62 |
| Medmerge-tulu-70b | 67.81 | 87.46 | 70.1 | 47.89 | 83.43 | 56.56 |
## Performance
Clinical Camel demonstrates competitive performance on medical benchmarks.
**Table: Five-Shot Performance of Clinical Camel-70B (C70), GPT3.5, GPT4, and Med-PaLM 2 on Various Medical Datasets**
| Dataset | Medmerge-tulu-70b | ClinicalCamel-70B | GPT3.5 | GPT4 | Med-PaLM 2 |
|-----------------------------|-------------------|-------------------|--------|-------|--------------|
| MMLU Anatomy | 66.6 | 65.2 | 60.7 | 80.0 | 77.8 |
| MMLU Clinical Knowledge | 72.0 | 72.8 | 68.7 | 86.4 | 88.3 |
| MMLU College Biology | 84.7 | 81.2 | 72.9 | 93.8 | 94.4 |
| MMLU College Medicine | 64.2 | 68.2 | 63.6 | 76.3 | 80.9 |
| MMLU Medical Genetics | 76.0 | 69.0 | 68.0 | 92.0 | 90.0 |
| MMLU Professional Medicine | 75.7 | 75.0 | 69.8 | 93.8 | 95.2 |
| MedMCQA | | 54.2 | 51.0 | 72.4 | 71.3 |
| MedQA (USMLE) | | 60.7 | 53.6 | 81.4 | 79.7 |
| PubMedQA | | 77.9 | 60.2 | 74.4 | 79.2 |
| USMLE Sample Exam | | 64.3 | 58.5 | 86.6 | - |
## 🧩 Configuration
```yaml
models:
- model: NousResearch/Llama-2-70b-hf
# no parameters necessary for base model
- model: wanglab/ClinicalCamel-70B
parameters:
weight: 0.08
density: 0.45
- model: epfl-llm/meditron-70b
parameters:
weight: 0.08
density: 0.45
- model: allenai/tulu-2-dpo-70b
parameters:
weight: 0.08
density: 0.45
merge_method: dare_ties
base_model: NousResearch/Llama-2-70b-hf
parameters:
int8_mask: true
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Technoculture/Medmerge-tulu-70b"
messages = [{"role": "user", "content": "I am feeling sleepy these days"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
vanillaOVO/supermario_v2 | vanillaOVO | 2024-04-04T07:09:06Z | 727 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"arxiv:2311.03099",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-01-25T14:58:50Z | ---
base_model: []
tags:
- mergekit
- merge
license: apache-2.0
---
This is a merge of pre-trained language models created based on [DARE](https://arxiv.org/abs/2311.03099) using [mergekit](https://github.com/cg123/mergekit).
More descriptions of the model will be added soon.
### **Loading the Model**
Use the following Python code to load the model:
```python
import torch
from transformers import MistralForCausalLM, AutoTokenizer
model = MistralForCausalLM.from_pretrained("vanillaOVO/supermario_v2", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("vanillaOVO/supermario_v2")
```
### **Generating Text**
To generate text, use the following Python code:
```python
text = "Large language models are "
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
|
zorobin/mistral-class-shishya-all-hal-7b-ep3 | zorobin | 2024-01-28T04:35:23Z | 727 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-01-28T04:35:21Z | ---
library_name: transformers
license: llama2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
NobodyExistsOnTheInternet/clown-SUV-4x70b | NobodyExistsOnTheInternet | 2024-01-30T14:11:18Z | 727 | 4 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-01-30T11:58:39Z | ---
license: mit
---
The smaller brother to the clown truck. 4 clowns in an SUV. Untrained.
Models used:
WizardLM/WizardMath-70B-V1.0
Sao10K/Euryale-Inverted-L2-70B
NobodyExistsOnTheInternet/code-llama-70b-python-instruct
Technoculture/Medmerge-wizard-70b
Full config can be found within the files. |
Gille/StrangeMerges_20-7B-slerp | Gille | 2024-04-03T09:13:55Z | 727 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"flemmingmiguel/MBX-7B-v3",
"Gille/StrangeMerges_11-7B-slerp",
"base_model:flemmingmiguel/MBX-7B-v3",
"base_model:Gille/StrangeMerges_11-7B-slerp",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-02-02T04:50:10Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- flemmingmiguel/MBX-7B-v3
- Gille/StrangeMerges_11-7B-slerp
base_model:
- flemmingmiguel/MBX-7B-v3
- Gille/StrangeMerges_11-7B-slerp
model-index:
- name: StrangeMerges_20-7B-slerp
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: 73.12
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/StrangeMerges_20-7B-slerp
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: 88.45
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/StrangeMerges_20-7B-slerp
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: 65.06
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/StrangeMerges_20-7B-slerp
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: 70.9
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/StrangeMerges_20-7B-slerp
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: 83.43
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/StrangeMerges_20-7B-slerp
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: 72.18
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/StrangeMerges_20-7B-slerp
name: Open LLM Leaderboard
---
# StrangeMerges_20-7B-slerp
StrangeMerges_20-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [flemmingmiguel/MBX-7B-v3](https://huggingface.co/flemmingmiguel/MBX-7B-v3)
* [Gille/StrangeMerges_11-7B-slerp](https://huggingface.co/Gille/StrangeMerges_11-7B-slerp)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: flemmingmiguel/MBX-7B-v3
layer_range: [0, 32]
- model: Gille/StrangeMerges_11-7B-slerp
layer_range: [0, 32]
merge_method: slerp
base_model: flemmingmiguel/MBX-7B-v3
parameters:
t:
- filter: self_attn
value: [0.1, 0.5, 0.3, 0.7, 0.9]
- filter: mlp
value: [0.9, 0.5, 0.7, 0.3, 0.1]
- value: 0.45
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Gille/StrangeMerges_20-7B-slerp"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
# [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_Gille__StrangeMerges_20-7B-slerp)
| Metric |Value|
|---------------------------------|----:|
|Avg. |75.52|
|AI2 Reasoning Challenge (25-Shot)|73.12|
|HellaSwag (10-Shot) |88.45|
|MMLU (5-Shot) |65.06|
|TruthfulQA (0-shot) |70.90|
|Winogrande (5-shot) |83.43|
|GSM8k (5-shot) |72.18|
|
Kquant03/Cognito-2x7B-bf16 | Kquant03 | 2024-02-29T02:32:00Z | 727 | 5 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"moe",
"merge",
"en",
"arxiv:2101.03961",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-02-04T05:39:54Z | ---
license: apache-2.0
language:
- en
tags:
- moe
- merge
---
# Kquant03/Cognito-2x7B-bf16
Knowledge is **power**.
[Join our Discord!](https://discord.gg/ZgU79QDnE2)
A frankenMoE using zero positive prompting for the config. (designed by [NeuralNovel](https://huggingface.co/NeuralNovel))
The basic config looks like this...(detailed version is in the files and versions):
- [flemmingmiguel/MBX-7B-v3](https://huggingface.co/flemmingmiguel/MBX-7B-v3) - base
- [alnrg2arg/blockchainlabs_test3_seminar](https://huggingface.co/alnrg2arg/blockchainlabs_test3_seminar) - expert #1
- [flemmingmiguel/MBX-7B-v3](https://huggingface.co/flemmingmiguel/MBX-7B-v3) - expert #2
# Uploading it to Evals, soon.
# "[What is a Mixture of Experts (MoE)?](https://huggingface.co/blog/moe)"
### (from the MistralAI papers...click the quoted question above to navigate to it directly.)
The scale of a model is one of the most important axes for better model quality. Given a fixed computing budget, training a larger model for fewer steps is better than training a smaller model for more steps.
Mixture of Experts enable models to be pretrained with far less compute, which means you can dramatically scale up the model or dataset size with the same compute budget as a dense model. In particular, a MoE model should achieve the same quality as its dense counterpart much faster during pretraining.
So, what exactly is a MoE? In the context of transformer models, a MoE consists of two main elements:
Sparse MoE layers are used instead of dense feed-forward network (FFN) layers. MoE layers have a certain number of “experts” (e.g. 32 in my "frankenMoE"), where each expert is a neural network. In practice, the experts are FFNs, but they can also be more complex networks or even a MoE itself, leading to hierarchical MoEs!
A gate network or router, that determines which tokens are sent to which expert. For example, in the image below, the token “More” is sent to the second expert, and the token "Parameters” is sent to the first network. As we’ll explore later, we can send a token to more than one expert. How to route a token to an expert is one of the big decisions when working with MoEs - the router is composed of learned parameters and is pretrained at the same time as the rest of the network.
At every layer, for every token, a router network chooses two of these groups (the “experts”) to process the token and combine their output additively.
Switch Layer
MoE layer from the [Switch Transformers paper](https://arxiv.org/abs/2101.03961)
So, to recap, in MoEs we replace every FFN layer of the transformer model with an MoE layer, which is composed of a gate network and a certain number of experts.
Although MoEs provide benefits like efficient pretraining and faster inference compared to dense models, they also come with challenges:
Training: MoEs enable significantly more compute-efficient pretraining, but they’ve historically struggled to generalize during fine-tuning, leading to overfitting.
Inference: Although a MoE might have many parameters, only some of them are used during inference. This leads to much faster inference compared to a dense model with the same number of parameters. However, all parameters need to be loaded in RAM, so memory requirements are high. For example, [given a MoE like Mixtral 8x7B](https://huggingface.co/blog/moe), we’ll need to have enough VRAM to hold a dense 47B parameter model. Why 47B parameters and not 8 x 7B = 56B? That’s because in MoE models, only the FFN layers are treated as individual experts, and the rest of the model parameters are shared. At the same time, assuming just two experts are being used per token, the inference speed (FLOPs) is like using a 12B model (as opposed to a 14B model), because it computes 2x7B matrix multiplications, but with some layers shared (more on this soon).
If all our tokens are sent to just a few popular experts, that will make training inefficient. In a normal MoE training, the gating network converges to mostly activate the same few experts. This self-reinforces as favored experts are trained quicker and hence selected more. To mitigate this, an auxiliary loss is added to encourage giving all experts equal importance. This loss ensures that all experts receive a roughly equal number of training examples. The following sections will also explore the concept of expert capacity, which introduces a threshold of how many tokens can be processed by an expert. In transformers, the auxiliary loss is exposed via the aux_loss parameter.
## "Wait...but you called this a frankenMoE?"
The difference between MoE and "frankenMoE" lies in the fact that the router layer in a model like the one on this repo is not trained simultaneously. |
vilm/Quyen-Plus-v0.1 | vilm | 2024-03-13T12:10:18Z | 727 | 5 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"en",
"dataset:teknium/OpenHermes-2.5",
"dataset:LDJnr/Capybara",
"dataset:Intel/orca_dpo_pairs",
"dataset:argilla/distilabel-capybara-dpo-7k-binarized",
"license:other",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-02-06T00:24:42Z | ---
language:
- en
license: other
library_name: transformers
datasets:
- teknium/OpenHermes-2.5
- LDJnr/Capybara
- Intel/orca_dpo_pairs
- argilla/distilabel-capybara-dpo-7k-binarized
pipeline_tag: text-generation
model-index:
- name: Quyen-Plus-v0.1
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: 55.72
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vilm/Quyen-Plus-v0.1
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: 78.52
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vilm/Quyen-Plus-v0.1
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: 60.45
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vilm/Quyen-Plus-v0.1
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: 53.6
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vilm/Quyen-Plus-v0.1
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: 71.27
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vilm/Quyen-Plus-v0.1
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: 60.05
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vilm/Quyen-Plus-v0.1
name: Open LLM Leaderboard
---
# Quyen
<img src="quyen.webp" width="512" height="512" alt="Quyen">
# Model Description
Quyen is our first flagship LLM series based on the Qwen1.5 family. We introduced 6 different versions:
- **Quyen-SE (0.5B)**
- **Quyen-Mini (1.8B)**
- **Quyen (4B)**
- **Quyen-Plus (7B)**
- **Quyen-Pro (14B)**
- **Quyen-Pro-Max (72B)**
All models were trained with SFT and DPO using the following dataset:
- *OpenHermes-2.5* by **Teknium**
- *Capyabara* by **LDJ**
- *argilla/distilabel-capybara-dpo-7k-binarized* by **argilla**
- *orca_dpo_pairs* by **Intel**
- and Private Data by **Ontocord** & **BEE-spoke-data**
# Prompt Template
- All Quyen models use ChatML as the default template:
```
<|im_start|>system
You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<|im_end|>
<|im_start|>user
Hello world.<|im_end|>
<|im_start|>assistant
```
- You can also use `apply_chat_template`:
```python
messages = [
{"role": "system", "content": "You are a sentient, superintelligent artificial general intelligence, here to teach and assist me."},
{"role": "user", "content": "Hello world."}
]
gen_input = tokenizer.apply_chat_template(message, return_tensors="pt")
model.generate(**gen_input)
```
# Benchmarks:
- Coming Soon! We will update the benchmarks later
# Acknowledgement
- We're incredibly grateful to **Tensoic** and **Ontocord** for their generous support with compute and data preparation.
- Special thanks to the Qwen team for letting us access the models early for these amazing finetunes.
# [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_vilm__Quyen-Plus-v0.1)
| Metric |Value|
|---------------------------------|----:|
|Avg. |63.27|
|AI2 Reasoning Challenge (25-Shot)|55.72|
|HellaSwag (10-Shot) |78.52|
|MMLU (5-Shot) |60.45|
|TruthfulQA (0-shot) |53.60|
|Winogrande (5-shot) |71.27|
|GSM8k (5-shot) |60.05|
|
Lvxy1117/amber_fine_tune_sg_part1 | Lvxy1117 | 2024-02-10T04:20:48Z | 727 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-02-09T15:39:23Z | ---
license: apache-2.0
---
# Model Card for Model ID
amber fine tune model used sg_90k_part1
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
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### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
[More Information Needed] |
Gille/StrangeMerges_22-7B-slerp | Gille | 2024-03-04T21:54:56Z | 727 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"Gille/StrangeMerges_21-7B-slerp",
"paulml/OGNO-7B",
"base_model:Gille/StrangeMerges_21-7B-slerp",
"base_model:paulml/OGNO-7B",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-02-12T23:53:21Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- Gille/StrangeMerges_21-7B-slerp
- paulml/OGNO-7B
base_model:
- Gille/StrangeMerges_21-7B-slerp
- paulml/OGNO-7B
model-index:
- name: StrangeMerges_22-7B-slerp
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: 73.72
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/StrangeMerges_22-7B-slerp
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: 89.03
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/StrangeMerges_22-7B-slerp
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: 64.8
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/StrangeMerges_22-7B-slerp
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: 74.9
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/StrangeMerges_22-7B-slerp
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: 84.77
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/StrangeMerges_22-7B-slerp
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: 69.75
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Gille/StrangeMerges_22-7B-slerp
name: Open LLM Leaderboard
---
# StrangeMerges_22-7B-slerp
StrangeMerges_22-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Gille/StrangeMerges_21-7B-slerp](https://huggingface.co/Gille/StrangeMerges_21-7B-slerp)
* [paulml/OGNO-7B](https://huggingface.co/paulml/OGNO-7B)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: Gille/StrangeMerges_21-7B-slerp
layer_range: [0, 32]
- model: paulml/OGNO-7B
layer_range: [0, 32]
merge_method: slerp
base_model: Gille/StrangeMerges_21-7B-slerp
parameters:
t:
- filter: self_attn
value: [0.1, 0.3, 0.5, 0.7, 0.9]
- filter: mlp
value: [0.9, 0.7, 0.5, 0.3, 0.1]
- value: 0.45
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Gille/StrangeMerges_22-7B-slerp"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
# [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_Gille__StrangeMerges_22-7B-slerp)
| Metric |Value|
|---------------------------------|----:|
|Avg. |76.16|
|AI2 Reasoning Challenge (25-Shot)|73.72|
|HellaSwag (10-Shot) |89.03|
|MMLU (5-Shot) |64.80|
|TruthfulQA (0-shot) |74.90|
|Winogrande (5-shot) |84.77|
|GSM8k (5-shot) |69.75|
|
InnerI/InnerILLM-0x00d0-7B-slerp | InnerI | 2024-03-09T00:52:02Z | 727 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"NousResearch/Yarn-Mistral-7b-128k",
"InnerI/InnerILLM-0x00d0-Ox0dad0-nous-nous-v2.0-7B-slerp",
"base_model:NousResearch/Yarn-Mistral-7b-128k",
"base_model:InnerI/InnerILLM-0x00d0-Ox0dad0-nous-nous-v2.0-7B-slerp",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-02-13T04:28:33Z | ---
tags:
- merge
- mergekit
- lazymergekit
- NousResearch/Yarn-Mistral-7b-128k
- InnerI/InnerILLM-0x00d0-Ox0dad0-nous-nous-v2.0-7B-slerp
base_model:
- NousResearch/Yarn-Mistral-7b-128k
- InnerI/InnerILLM-0x00d0-Ox0dad0-nous-nous-v2.0-7B-slerp
license: apache-2.0
---
# InnerILLM-0x00d0-Ox0dad0-Nous-Yarn-Mistral-7B-slerp
InnerILLM-0x00d0-Ox0dad0-Nous-Yarn-Mistral-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [NousResearch/Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k)
* [InnerI/InnerILLM-0x00d0-Ox0dad0-nous-nous-v2.0-7B-slerp](https://huggingface.co/InnerI/InnerILLM-0x00d0-Ox0dad0-nous-nous-v2.0-7B-slerp)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: NousResearch/Yarn-Mistral-7b-128k
layer_range: [0, 32]
- model: InnerI/InnerILLM-0x00d0-Ox0dad0-nous-nous-v2.0-7B-slerp
layer_range: [0, 32]
merge_method: slerp
base_model: NousResearch/Yarn-Mistral-7b-128k
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "InnerI/InnerILLM-0x00d0-Ox0dad0-Nous-Yarn-Mistral-7B-slerp"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
MaziyarPanahi/WizardLM-Math-70B-TIES-v0.1 | MaziyarPanahi | 2024-03-04T13:35:44Z | 727 | 3 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"wizardlm",
"wizardmath",
"license:agpl-3.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-02-14T00:42:22Z | ---
license: agpl-3.0
tags:
- merge
- wizardlm
- wizardmath
pipeline_tag: text-generation
model-index:
- name: WizardLM-Math-70B-TIES-v0.1
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: 68.52
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/WizardLM-Math-70B-TIES-v0.1
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: 86.87
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/WizardLM-Math-70B-TIES-v0.1
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: 69.24
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/WizardLM-Math-70B-TIES-v0.1
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: 53.61
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/WizardLM-Math-70B-TIES-v0.1
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: 82.72
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/WizardLM-Math-70B-TIES-v0.1
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: 27.37
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/WizardLM-Math-70B-TIES-v0.1
name: Open LLM Leaderboard
---
## Examples
There are three killers in a room. Someone enters the room and kills one of them. Nobody leaves the room. How many killers are left in the room? Explain your reasoning step by step.
```
Initially, there are three killers in the room. Let's call them Killer A, Killer B, and Killer C.
Step 1: Someone enters the room and kills one of the killers. We don't know who this person is or which killer they killed. However, we know that one killer is dead.
Step 2: Now, we have two killers left in the room. These two killers are the ones who were not killed by the person who entered the room.
Step 3: The person who entered the room is not a killer, as they are not included in the initial three killers. However, they have killed one of the killers, so they are not innocent either.
Step 4: Since the person who entered the room is not a killer, we can conclude that there are only two killers left in the room after the incident.
So, the final answer is: There are 2 killers left in the room.
The answer is: there are 2 killers left in the room.
```
Answered wrong!
Mark is faster than Mary , Mary is faster than Joe. Is Joe faster than Mark? Let's think step by step
```
irst, we know that Mark is faster than Mary. This means that if they were to race, Mark would finish before Mary. We can represent this relationship as:
Mark > Mary
Next, we know that Mary is faster than Joe. This means that if Mary and Joe were to race, Mary would finish before Joe. We can represent this relationship as:
Mary > Joe
Now, we want to find out if Joe is faster than Mark. To do this, we can use the transitive property, which states that if A > B and B > C, then A > C. In our case, A represents Mark, B represents Mary, and C represents Joe. So, we can apply the transitive property to our relationships:
Since Mark > Mary and Mary > Joe, we can conclude that Mark > Joe.
So, the final answer is: No, Joe is not faster than Mark.
The answer is: No, Joe is not faster than Mark.
```
If I lay 2 wet shirts out in the sun flat on the ground to dry and it takes 5 hours until they are dry, how long would it take to dry 4 shirts of the same type that way simultanously?
```
First, we need to understand the relationship between the number of shirts and the time it takes to dry them. Since the shirts are laid out flat on the ground, they have the same surface area exposed to the sun. This means that the drying time is directly proportional to the number of shirts.
Step 1: Identify the given information.
- Time to dry 2 shirts: 5 hours
- Number of shirts: 2
Step 2: Set up a proportion to represent the relationship between the number of shirts and the time it takes to dry them.
- Let x be the time it takes to dry 4 shirts.
- We can write the proportion as:
(2 shirts / 5 hours) = (4 shirts / x)
Step 3: Solve for x.
- To solve for x, we can cross-multiply:
2 shirts * x = 5 hours * 4 shirts
- Simplify the equation:
2x = 20 shirt-hours
- Now, divide both sides by 2 to isolate x:
x = 10 hours
So, it would take 10 hours to dry 4 shirts of the same type simultaneously.
The answer is: 10.
```
Wrong, should be 5
explain step by step 25-4*2+3=?
```
First, we need to follow the order of operations (PEMDAS/BODMAS), which stands for Parentheses/Brackets, Exponents/Orders, Multiplication and Division, and Addition and Subtraction. This means we should perform the operations inside the parentheses first, then the exponents, then the multiplication and division, and finally the addition and subtraction.
Here's the given expression: 25 - 4 * 2 + 3
Step 1: Perform the multiplication operation inside the parentheses.
25 - (4 * 2) + 3
Step 2: Perform the multiplication operation.
25 - (8) + 3
Step 3: Now, we have a subtraction operation and an addition operation. Since they have the same precedence, we can perform them from left to right.
25 - 8 + 3
Step 4: Perform the subtraction operation.
17 + 3
Step 5: Perform the addition operation.
20
So, the final answer is 20.
The answer is: the final answer is 20.
```
## Prompt template
```
"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:"
```
or for CoT (❗For the simple math questions, we do NOT recommend to use the CoT prompt.)
```
"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response: Let's think step by step."
```
## Eval
```python
{
"all": {
"acc": 0.6868282613819305,
"acc_stderr": 0.030371866427473967,
"acc_norm": 0.695311288530275,
"acc_norm_stderr": 0.030984285786669577,
"mc1": 0.36964504283965727,
"mc1_stderr": 0.01689818070697388,
"mc2": 0.5360987678643523,
"mc2_stderr": 0.014938153988985473
},
"harness|arc:challenge|25": {
"acc": 0.6424914675767918,
"acc_stderr": 0.014005494275916573,
"acc_norm": 0.6851535836177475,
"acc_norm_stderr": 0.01357265770308495
},
"harness|hellaswag|10": {
"acc": 0.6836287592113125,
"acc_stderr": 0.004641092001425294,
"acc_norm": 0.8686516630153356,
"acc_norm_stderr": 0.0033709059327855567
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252605,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252605
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6222222222222222,
"acc_stderr": 0.04188307537595852,
"acc_norm": 0.6222222222222222,
"acc_norm_stderr": 0.04188307537595852
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.8026315789473685,
"acc_stderr": 0.03238981601699397,
"acc_norm": 0.8026315789473685,
"acc_norm_stderr": 0.03238981601699397
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.72,
"acc_stderr": 0.04512608598542127,
"acc_norm": 0.72,
"acc_norm_stderr": 0.04512608598542127
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7471698113207547,
"acc_stderr": 0.026749899771241214,
"acc_norm": 0.7471698113207547,
"acc_norm_stderr": 0.026749899771241214
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.8194444444444444,
"acc_stderr": 0.032166008088022675,
"acc_norm": 0.8194444444444444,
"acc_norm_stderr": 0.032166008088022675
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.57,
"acc_stderr": 0.04975698519562428,
"acc_norm": 0.57,
"acc_norm_stderr": 0.04975698519562428
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252604,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252604
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6647398843930635,
"acc_stderr": 0.03599586301247077,
"acc_norm": 0.6647398843930635,
"acc_norm_stderr": 0.03599586301247077
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.35294117647058826,
"acc_stderr": 0.047551296160629475,
"acc_norm": 0.35294117647058826,
"acc_norm_stderr": 0.047551296160629475
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.74,
"acc_stderr": 0.04408440022768079,
"acc_norm": 0.74,
"acc_norm_stderr": 0.04408440022768079
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.6893617021276596,
"acc_stderr": 0.03025123757921317,
"acc_norm": 0.6893617021276596,
"acc_norm_stderr": 0.03025123757921317
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.40350877192982454,
"acc_stderr": 0.046151869625837026,
"acc_norm": 0.40350877192982454,
"acc_norm_stderr": 0.046151869625837026
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.6068965517241379,
"acc_stderr": 0.040703290137070705,
"acc_norm": 0.6068965517241379,
"acc_norm_stderr": 0.040703290137070705
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4312169312169312,
"acc_stderr": 0.0255064816981382,
"acc_norm": 0.4312169312169312,
"acc_norm_stderr": 0.0255064816981382
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.5,
"acc_stderr": 0.04472135954999579,
"acc_norm": 0.5,
"acc_norm_stderr": 0.04472135954999579
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.43,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.43,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.8193548387096774,
"acc_stderr": 0.021886178567172527,
"acc_norm": 0.8193548387096774,
"acc_norm_stderr": 0.021886178567172527
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5320197044334976,
"acc_stderr": 0.03510766597959217,
"acc_norm": 0.5320197044334976,
"acc_norm_stderr": 0.03510766597959217
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.71,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.71,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.8121212121212121,
"acc_stderr": 0.03050193405942914,
"acc_norm": 0.8121212121212121,
"acc_norm_stderr": 0.03050193405942914
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8888888888888888,
"acc_stderr": 0.022390787638216773,
"acc_norm": 0.8888888888888888,
"acc_norm_stderr": 0.022390787638216773
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.927461139896373,
"acc_stderr": 0.018718998520678185,
"acc_norm": 0.927461139896373,
"acc_norm_stderr": 0.018718998520678185
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.7230769230769231,
"acc_stderr": 0.022688042352424994,
"acc_norm": 0.7230769230769231,
"acc_norm_stderr": 0.022688042352424994
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3333333333333333,
"acc_stderr": 0.028742040903948492,
"acc_norm": 0.3333333333333333,
"acc_norm_stderr": 0.028742040903948492
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.8109243697478992,
"acc_stderr": 0.02543511943810537,
"acc_norm": 0.8109243697478992,
"acc_norm_stderr": 0.02543511943810537
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.4304635761589404,
"acc_stderr": 0.04042809961395634,
"acc_norm": 0.4304635761589404,
"acc_norm_stderr": 0.04042809961395634
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8862385321100917,
"acc_stderr": 0.0136136148002328,
"acc_norm": 0.8862385321100917,
"acc_norm_stderr": 0.0136136148002328
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5879629629629629,
"acc_stderr": 0.03356787758160831,
"acc_norm": 0.5879629629629629,
"acc_norm_stderr": 0.03356787758160831
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.9166666666666666,
"acc_stderr": 0.019398452135813895,
"acc_norm": 0.9166666666666666,
"acc_norm_stderr": 0.019398452135813895
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8776371308016878,
"acc_stderr": 0.02133174182974679,
"acc_norm": 0.8776371308016878,
"acc_norm_stderr": 0.02133174182974679
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.8161434977578476,
"acc_stderr": 0.025998379092356513,
"acc_norm": 0.8161434977578476,
"acc_norm_stderr": 0.025998379092356513
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.8473282442748091,
"acc_stderr": 0.03154521672005472,
"acc_norm": 0.8473282442748091,
"acc_norm_stderr": 0.03154521672005472
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8512396694214877,
"acc_stderr": 0.03248470083807194,
"acc_norm": 0.8512396694214877,
"acc_norm_stderr": 0.03248470083807194
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.8148148148148148,
"acc_stderr": 0.03755265865037181,
"acc_norm": 0.8148148148148148,
"acc_norm_stderr": 0.03755265865037181
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.803680981595092,
"acc_stderr": 0.031207970394709225,
"acc_norm": 0.803680981595092,
"acc_norm_stderr": 0.031207970394709225
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.48214285714285715,
"acc_stderr": 0.047427623612430116,
"acc_norm": 0.48214285714285715,
"acc_norm_stderr": 0.047427623612430116
},
"harness|hendrycksTest-management|5": {
"acc": 0.8446601941747572,
"acc_stderr": 0.03586594738573975,
"acc_norm": 0.8446601941747572,
"acc_norm_stderr": 0.03586594738573975
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8846153846153846,
"acc_stderr": 0.020930193185179333,
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"harness|hendrycksTest-medical_genetics|5": {
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"harness|hendrycksTest-miscellaneous|5": {
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"harness|hendrycksTest-moral_disputes|5": {
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"harness|hendrycksTest-moral_scenarios|5": {
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"harness|hendrycksTest-nutrition|5": {
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"harness|hendrycksTest-philosophy|5": {
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"harness|hendrycksTest-prehistory|5": {
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"harness|hendrycksTest-professional_accounting|5": {
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},
"harness|hendrycksTest-professional_law|5": {
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"acc_norm": 0.5658409387222947,
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"harness|hendrycksTest-professional_medicine|5": {
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},
"harness|hendrycksTest-professional_psychology|5": {
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},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.7272727272727273,
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},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.8,
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"harness|hendrycksTest-sociology|5": {
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},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.93,
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"acc_norm": 0.93,
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},
"harness|hendrycksTest-virology|5": {
"acc": 0.5240963855421686,
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"acc_norm": 0.5240963855421686,
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},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8654970760233918,
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"acc_norm": 0.8654970760233918,
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},
"harness|truthfulqa:mc|0": {
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"mc1_stderr": 0.01689818070697388,
"mc2": 0.5360987678643523,
"mc2_stderr": 0.014938153988985473
},
"harness|winogrande|5": {
"acc": 0.8271507498026835,
"acc_stderr": 0.010626964529971855
},
"harness|gsm8k|5": {
"acc": 0.27369219105382864,
"acc_stderr": 0.012281003490963456
}
}
```
# [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_MaziyarPanahi__WizardLM-Math-70B-TIES-v0.1)
| Metric |Value|
|---------------------------------|----:|
|Avg. |64.72|
|AI2 Reasoning Challenge (25-Shot)|68.52|
|HellaSwag (10-Shot) |86.87|
|MMLU (5-Shot) |69.24|
|TruthfulQA (0-shot) |53.61|
|Winogrande (5-shot) |82.72|
|GSM8k (5-shot) |27.37|
|
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