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hugface34/my_awesome_eli5_mlm_model | hugface34 | 2024-10-16T07:35:15Z | 179 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"fill-mask",
"generated_from_trainer",
"dataset:eli5_category",
"base_model:distilbert/distilroberta-base",
"base_model:finetune:distilbert/distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2024-10-16T05:02:52Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilroberta-base
tags:
- generated_from_trainer
datasets:
- eli5_category
model-index:
- name: my_awesome_eli5_mlm_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_eli5_mlm_model
This model is a fine-tuned version of [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on the eli5_category dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0053
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.2515 | 1.0 | 1320 | 2.0712 |
| 2.1569 | 2.0 | 2640 | 2.0356 |
| 2.1364 | 3.0 | 3960 | 2.0228 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1
|
RichardErkhov/MaziyarPanahi_-_calme-2.2-qwen2.5-72b-gguf | RichardErkhov | 2024-10-16T07:26:21Z | 11 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-15T00:14:14Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
calme-2.2-qwen2.5-72b - GGUF
- Model creator: https://huggingface.co/MaziyarPanahi/
- Original model: https://huggingface.co/MaziyarPanahi/calme-2.2-qwen2.5-72b/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [calme-2.2-qwen2.5-72b.Q2_K.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_calme-2.2-qwen2.5-72b-gguf/blob/main/calme-2.2-qwen2.5-72b.Q2_K.gguf) | Q2_K | 27.76GB |
| [calme-2.2-qwen2.5-72b.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_calme-2.2-qwen2.5-72b-gguf/blob/main/calme-2.2-qwen2.5-72b.IQ3_XS.gguf) | IQ3_XS | 30.58GB |
| [calme-2.2-qwen2.5-72b.IQ3_S.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_calme-2.2-qwen2.5-72b-gguf/blob/main/calme-2.2-qwen2.5-72b.IQ3_S.gguf) | IQ3_S | 32.12GB |
| [calme-2.2-qwen2.5-72b.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_calme-2.2-qwen2.5-72b-gguf/blob/main/calme-2.2-qwen2.5-72b.Q3_K_S.gguf) | Q3_K_S | 32.12GB |
| [calme-2.2-qwen2.5-72b.IQ3_M.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_calme-2.2-qwen2.5-72b-gguf/blob/main/calme-2.2-qwen2.5-72b.IQ3_M.gguf) | IQ3_M | 33.06GB |
| [calme-2.2-qwen2.5-72b.Q3_K.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_calme-2.2-qwen2.5-72b-gguf/blob/main/calme-2.2-qwen2.5-72b.Q3_K.gguf) | Q3_K | 35.11GB |
| [calme-2.2-qwen2.5-72b.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_calme-2.2-qwen2.5-72b-gguf/blob/main/calme-2.2-qwen2.5-72b.Q3_K_M.gguf) | Q3_K_M | 35.11GB |
| [calme-2.2-qwen2.5-72b.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_calme-2.2-qwen2.5-72b-gguf/blob/main/calme-2.2-qwen2.5-72b.Q3_K_L.gguf) | Q3_K_L | 14.37GB |
| [calme-2.2-qwen2.5-72b.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_calme-2.2-qwen2.5-72b-gguf/tree/main/) | IQ4_XS | 37.4GB |
| [calme-2.2-qwen2.5-72b.Q4_0.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_calme-2.2-qwen2.5-72b-gguf/tree/main/) | Q4_0 | 38.4GB |
| [calme-2.2-qwen2.5-72b.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_calme-2.2-qwen2.5-72b-gguf/tree/main/) | IQ4_NL | 38.9GB |
| [calme-2.2-qwen2.5-72b.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_calme-2.2-qwen2.5-72b-gguf/blob/main/calme-2.2-qwen2.5-72b.Q4_K_S.gguf) | Q4_K_S | 36.95GB |
| [calme-2.2-qwen2.5-72b.Q4_K.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_calme-2.2-qwen2.5-72b-gguf/tree/main/) | Q4_K | 44.16GB |
| [calme-2.2-qwen2.5-72b.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_calme-2.2-qwen2.5-72b-gguf/tree/main/) | Q4_K_M | 44.16GB |
| [calme-2.2-qwen2.5-72b.Q4_1.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_calme-2.2-qwen2.5-72b-gguf/tree/main/) | Q4_1 | 42.56GB |
| [calme-2.2-qwen2.5-72b.Q5_0.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_calme-2.2-qwen2.5-72b-gguf/tree/main/) | Q5_0 | 46.71GB |
| [calme-2.2-qwen2.5-72b.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_calme-2.2-qwen2.5-72b-gguf/tree/main/) | Q5_K_S | 47.84GB |
| [calme-2.2-qwen2.5-72b.Q5_K.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_calme-2.2-qwen2.5-72b-gguf/tree/main/) | Q5_K | 50.7GB |
| [calme-2.2-qwen2.5-72b.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_calme-2.2-qwen2.5-72b-gguf/tree/main/) | Q5_K_M | 50.7GB |
| [calme-2.2-qwen2.5-72b.Q5_1.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_calme-2.2-qwen2.5-72b-gguf/tree/main/) | Q5_1 | 50.87GB |
| [calme-2.2-qwen2.5-72b.Q6_K.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_calme-2.2-qwen2.5-72b-gguf/tree/main/) | Q6_K | 59.92GB |
| [calme-2.2-qwen2.5-72b.Q8_0.gguf](https://huggingface.co/RichardErkhov/MaziyarPanahi_-_calme-2.2-qwen2.5-72b-gguf/tree/main/) | Q8_0 | 71.95GB |
Original model description:
---
language:
- en
license: other
library_name: transformers
tags:
- chat
- qwen
- qwen2
- qwen2.5
- finetune
- chatml
base_model: Qwen/Qwen2.5-72B
datasets:
- argilla/ultrafeedback-binarized-preferences
model_name: calme-2.2-qwen2.5-72b
license_name: tongyi-qianwen
license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE
pipeline_tag: text-generation
inference: false
model_creator: MaziyarPanahi
model-index:
- name: calme-2.2-qwen2.5-72b
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 84.77
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.2-qwen2.5-72b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 61.8
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.2-qwen2.5-72b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 3.63
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.2-qwen2.5-72b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 14.54
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.2-qwen2.5-72b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 12.02
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.2-qwen2.5-72b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 51.31
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.2-qwen2.5-72b
name: Open LLM Leaderboard
---
<img src="./calme-2.webp" alt="Calme-2 Models" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
# MaziyarPanahi/calme-2.2-qwen2.5-72b
This model is a fine-tuned version of the powerful `Qwen/Qwen2.5-72B-Instruct`, pushing the boundaries of natural language understanding and generation even further. My goal was to create a versatile and robust model that excels across a wide range of benchmarks and real-world applications.
## Use Cases
This model is suitable for a wide range of applications, including but not limited to:
- Advanced question-answering systems
- Intelligent chatbots and virtual assistants
- Content generation and summarization
- Code generation and analysis
- Complex problem-solving and decision support
# ⚡ Quantized GGUF
coming soon.
# 🏆 [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_MaziyarPanahi__calme-2.2-qwen2.5-72b)
| Metric |Value|
|-------------------|----:|
|Avg. |38.01|
|IFEval (0-Shot) |84.77|
|BBH (3-Shot) |61.80|
|MATH Lvl 5 (4-Shot)| 3.63|
|GPQA (0-shot) |14.54|
|MuSR (0-shot) |12.02|
|MMLU-PRO (5-shot) |51.31|
# Prompt Template
This model uses `ChatML` prompt template:
```
<|im_start|>system
{System}
<|im_end|>
<|im_start|>user
{User}
<|im_end|>
<|im_start|>assistant
{Assistant}
````
# How to use
```python
# Use a pipeline as a high-level helper
from transformers import pipeline
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="MaziyarPanahi/calme-2.2-qwen2.5-72b")
pipe(messages)
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/calme-2.2-qwen2.5-72b")
model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/calme-2.2-qwen2.5-72b")
```
# Ethical Considerations
As with any large language model, users should be aware of potential biases and limitations. We recommend implementing appropriate safeguards and human oversight when deploying this model in production environments.
|
Diksha2001/VLLM-llama3.1-lora-V1 | Diksha2001 | 2024-10-16T07:14:29Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/Meta-Llama-3.1-8B-bnb-4bit",
"base_model:finetune:unsloth/Meta-Llama-3.1-8B-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-16T06:07:50Z | ---
base_model: unsloth/Meta-Llama-3.1-8B-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
---
# Uploaded model
- **Developed by:** Diksha2001
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Meta-Llama-3.1-8B-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
JuniperChinenye/Set | JuniperChinenye | 2024-10-16T07:03:53Z | 38 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-16T07:01:04Z | ---
library_name: transformers
tags: []
---
# 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]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
mradermacher/Mixtronix-8B-i1-GGUF | mradermacher | 2024-10-16T06:59:06Z | 58 | 1 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:bunnycore/Mixtronix-8B",
"base_model:quantized:bunnycore/Mixtronix-8B",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2024-10-16T05:43:07Z | ---
base_model: bunnycore/Mixtronix-8B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/bunnycore/Mixtronix-8B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Mixtronix-8B-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/Mixtronix-8B-i1-GGUF/resolve/main/Mixtronix-8B.i1-IQ1_S.gguf) | i1-IQ1_S | 2.2 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Mixtronix-8B-i1-GGUF/resolve/main/Mixtronix-8B.i1-IQ1_M.gguf) | i1-IQ1_M | 2.4 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Mixtronix-8B-i1-GGUF/resolve/main/Mixtronix-8B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtronix-8B-i1-GGUF/resolve/main/Mixtronix-8B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtronix-8B-i1-GGUF/resolve/main/Mixtronix-8B.i1-IQ2_S.gguf) | i1-IQ2_S | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtronix-8B-i1-GGUF/resolve/main/Mixtronix-8B.i1-IQ2_M.gguf) | i1-IQ2_M | 3.2 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtronix-8B-i1-GGUF/resolve/main/Mixtronix-8B.i1-Q2_K.gguf) | i1-Q2_K | 3.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Mixtronix-8B-i1-GGUF/resolve/main/Mixtronix-8B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mixtronix-8B-i1-GGUF/resolve/main/Mixtronix-8B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtronix-8B-i1-GGUF/resolve/main/Mixtronix-8B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.9 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Mixtronix-8B-i1-GGUF/resolve/main/Mixtronix-8B.i1-IQ3_S.gguf) | i1-IQ3_S | 4.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Mixtronix-8B-i1-GGUF/resolve/main/Mixtronix-8B.i1-IQ3_M.gguf) | i1-IQ3_M | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtronix-8B-i1-GGUF/resolve/main/Mixtronix-8B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.3 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Mixtronix-8B-i1-GGUF/resolve/main/Mixtronix-8B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.6 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Mixtronix-8B-i1-GGUF/resolve/main/Mixtronix-8B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtronix-8B-i1-GGUF/resolve/main/Mixtronix-8B.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 5.0 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Mixtronix-8B-i1-GGUF/resolve/main/Mixtronix-8B.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 5.0 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Mixtronix-8B-i1-GGUF/resolve/main/Mixtronix-8B.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 5.0 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/Mixtronix-8B-i1-GGUF/resolve/main/Mixtronix-8B.i1-Q4_0.gguf) | i1-Q4_0 | 5.0 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Mixtronix-8B-i1-GGUF/resolve/main/Mixtronix-8B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 5.0 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Mixtronix-8B-i1-GGUF/resolve/main/Mixtronix-8B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mixtronix-8B-i1-GGUF/resolve/main/Mixtronix-8B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 6.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtronix-8B-i1-GGUF/resolve/main/Mixtronix-8B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 6.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtronix-8B-i1-GGUF/resolve/main/Mixtronix-8B.i1-Q6_K.gguf) | i1-Q6_K | 7.0 | 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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
QuantFactory/CursorCore-QW2.5-7B-GGUF | QuantFactory | 2024-10-16T06:57:17Z | 330 | 2 | transformers | [
"transformers",
"gguf",
"code",
"text-generation",
"arxiv:2410.07002",
"base_model:Qwen/Qwen2.5-Coder-7B",
"base_model:quantized:Qwen/Qwen2.5-Coder-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2024-10-16T06:14:57Z |
---
tags:
- code
base_model:
- Qwen/Qwen2.5-Coder-7B
library_name: transformers
pipeline_tag: text-generation
license: apache-2.0
---
[](https://hf.co/QuantFactory)
# QuantFactory/CursorCore-QW2.5-7B-GGUF
This is quantized version of [TechxGenus/CursorCore-QW2.5-7B](https://huggingface.co/TechxGenus/CursorCore-QW2.5-7B) created using llama.cpp
# Original Model Card
# CursorCore: Assist Programming through Aligning Anything
<p align="center">
<a href="http://arxiv.org/abs/2410.07002">[📄arXiv]</a> |
<a href="https://hf.co/papers/2410.07002">[🤗HF Paper]</a> |
<a href="https://huggingface.co/collections/TechxGenus/cursorcore-series-6706618c38598468866b60e2">[🤖Models]</a> |
<a href="https://github.com/TechxGenus/CursorCore">[🛠️Code]</a> |
<a href="https://github.com/TechxGenus/CursorWeb">[Web]</a> |
<a href="https://discord.gg/Z5Tev8fV">[Discord]</a>
</p>
<hr>
- [CursorCore: Assist Programming through Aligning Anything](#cursorcore-assist-programming-through-aligning-anything)
- [Introduction](#introduction)
- [Models](#models)
- [Usage](#usage)
- [1) Normal chat](#1-normal-chat)
- [2) Assistant-Conversation](#2-assistant-conversation)
- [3) Web Demo](#3-web-demo)
- [Future Work](#future-work)
- [Citation](#citation)
- [Contribution](#contribution)
<hr>
## Introduction
CursorCore is a series of open-source models designed for AI-assisted programming. It aims to support features such as automated editing and inline chat, replicating the core abilities of closed-source AI-assisted programming tools like Cursor. This is achieved by aligning data generated through Programming-Instruct. Please read [our paper](http://arxiv.org/abs/2410.07002) to learn more.
<p align="center">
<img width="100%" alt="conversation" src="https://raw.githubusercontent.com/TechxGenus/CursorCore/main/pictures/conversation.png">
</p>
## Models
Our models have been open-sourced on Hugging Face. You can access our models here: [CursorCore-Series](https://huggingface.co/collections/TechxGenus/cursorcore-series-6706618c38598468866b60e2"). We also provide pre-quantized weights for GPTQ and AWQ here: [CursorCore-Quantization](https://huggingface.co/collections/TechxGenus/cursorcore-quantization-67066431f29f252494ee8cf3)
## Usage
Here are some examples of how to use our model:
### 1) Normal chat
Script:
````python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("TechxGenus/CursorCore-Yi-9B")
model = AutoModelForCausalLM.from_pretrained(
"TechxGenus/CursorCore-Yi-9B",
torch_dtype=torch.bfloat16,
device_map="auto"
)
messages = [
{"role": "user", "content": "Hi!"},
]
prompt = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
inputs = tokenizer.encode(prompt, return_tensors="pt")
outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=512)
print(tokenizer.decode(outputs[0]))
````
Output:
````txt
<|im_start|>system
You are a helpful programming assistant.<|im_end|>
<|im_start|>user
Hi!<|im_end|>
<|im_start|>assistant
Hello! I'm an AI language model and I can help you with any programming questions you might have. What specific problem or task are you trying to solve?<|im_end|>
````
### 2) Assistant-Conversation
In our work, we introduce a new framework of AI-assisted programming task. It is designed for aligning anything during programming process, used for the implementation of features like Tab and Inline Chat.
Script 1:
````python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from eval.utils import prepare_input_for_wf
tokenizer = AutoTokenizer.from_pretrained("TechxGenus/CursorCore-Yi-9B")
model = AutoModelForCausalLM.from_pretrained(
"TechxGenus/CursorCore-Yi-9B",
torch_dtype=torch.bfloat16,
device_map="auto"
)
sample = {
"history": [
{
"type": "code",
"lang": "python",
"code": """def quick_sort(arr):\n if len(arr) <= 1:\n return arr\n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n return quick_sort(left) + middle + quick_sort(right)"""
}
],
"current": {
"type": "code",
"lang": "python",
"code": """def quick_sort(array):\n if len(arr) <= 1:\n return arr\n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n return quick_sort(left) + middle + quick_sort(right)"""
},
"user": ""
}
prompt = tokenizer.apply_chat_template(
prepare_input_for_wf(sample),
tokenize=False,
chat_template="assistant-conversation",
add_generation_prompt=True
)
inputs = tokenizer.encode(prompt, return_tensors="pt")
outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=512, do_sample=False)
print(tokenizer.decode(outputs[0]))
````
Output 1:
````txt
<|im_start|>system
You are a helpful programming assistant.<|im_end|>
<|im_start|>history
```python
def quick_sort(arr):
if len(arr) <= 1:
return arr
pivot = arr[len(arr) // 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quick_sort(left) + middle + quick_sort(right)
```<|im_end|>
<|im_start|>current
```python
def quick_sort(array):
if len(arr) <= 1:
return arr
pivot = arr[len(arr) // 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quick_sort(left) + middle + quick_sort(right)
```<|im_end|>
<|im_start|>assistant
<|next_start|>```python
def quick_sort(array):
if len(array) <= 1:
return array
pivot = array[len(array) // 2]
left = [x for x in array if x < pivot]
middle = [x for x in array if x == pivot]
right = [x for x in array if x > pivot]
return quick_sort(left) + middle + quick_sort(right)
```<|next_end|>
The user has provided a revised code change that corrects the parameter name from `arr` to `array` in the `quick_sort` function. This change ensures consistency in the function definition and avoids potential confusion or errors.
To implement this, we will:
1. Update the parameter name in the function definition from `arr` to `array`.
2. Ensure that all references to `arr` within the function are updated to `array`.
This will make the function definition and internal references consistent, improving code readability and maintainability.<|im_end|>
````
Script 2:
````python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from eval.utils import prepare_input_for_wf
tokenizer = AutoTokenizer.from_pretrained("TechxGenus/CursorCore-Yi-9B")
model = AutoModelForCausalLM.from_pretrained(
"TechxGenus/CursorCore-Yi-9B",
torch_dtype=torch.bfloat16,
device_map="auto"
)
sample = {
"history": [],
"current": {
"type": "code",
"lang": "python",
"code": """def quick_sort(array):\n if len(arr) <= 1:\n return arr\n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n return quick_sort(left) + middle + quick_sort(right)"""
},
"user": "Add Docstring."
}
prompt = tokenizer.apply_chat_template(
prepare_input_for_wf(sample),
tokenize=False,
chat_template="assistant-conversation",
add_generation_prompt=True
)
inputs = tokenizer.encode(prompt, return_tensors="pt")
outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=512, do_sample=False)
print(tokenizer.decode(outputs[0]))
````
Output 2:
````txt
<|im_start|>system
You are a helpful programming assistant.<|im_end|>
<|im_start|>current
```python
def quick_sort(array):
if len(arr) <= 1:
return arr
pivot = arr[len(arr) // 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quick_sort(left) + middle + quick_sort(right)
```<|im_end|>
<|im_start|>user
Add Docstring.<|im_end|>
<|im_start|>assistant
<|next_start|>```python
def quick_sort(array):
"""
This is an implementation of the quick sort algorithm.
"""
if len(arr) <= 1:
return arr
pivot = arr[len(arr) // 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quick_sort(left) + middle + quick_sort(right)
```<|next_end|><|im_end|>
````
For models in Locate-and-Change (LC) and Search-and-Replace (SR) formats, the output examples are as follows:
Script for LC:
````python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from eval.utils import prepare_input_for_lc
tokenizer = AutoTokenizer.from_pretrained("TechxGenus/CursorCore-Yi-1.5B-LC")
model = AutoModelForCausalLM.from_pretrained(
"TechxGenus/CursorCore-Yi-1.5B-LC",
torch_dtype=torch.bfloat16,
device_map="auto"
)
sample = {
"history": [],
"current": {
"type": "code",
"lang": "python",
"code": """def quick_sort(array):\n if len(arr) <= 1:\n return arr\n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n return quick_sort(left) + middle + quick_sort(right)"""
},
"user": "Add Docstring."
}
prompt = tokenizer.apply_chat_template(
prepare_input_for_lc(sample),
tokenize=False,
chat_template="assistant-conversation",
add_generation_prompt=True
)
inputs = tokenizer.encode(prompt, return_tensors="pt")
outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=512, do_sample=False)
print(tokenizer.decode(outputs[0]))
````
Output for LC:
````txt
<|im_start|>system
You are a helpful programming assistant.<|im_end|>
<|im_start|>current
```python
1 def quick_sort(array):
2 if len(arr) <= 1:
3 return arr
4 pivot = arr[len(arr) // 2]
5 left = [x for x in arr if x < pivot]
6 middle = [x for x in arr if x == pivot]
7 right = [x for x in arr if x > pivot]
8 return quick_sort(left) + middle + quick_sort(right)
```<|im_end|>
<|im_start|>user
Add Docstring.<|im_end|>
<|im_start|>assistant
<|next_start|>1,1
```
'''This function will sort an array using quick sort algorithm'''
```<|next_end|>
To enhance the readability and maintainability of the code, we should add a docstring to the `quick_sort` function. A docstring provides a clear description of what the function does, which is particularly useful for other developers who might use or modify this code in the future.
The docstring will be added immediately after the function definition, explaining that the function uses the quick sort algorithm to sort an array. This will make the code more self-explanatory and easier to understand.
Here's the plan:
1. Add a docstring at the beginning of the `quick_sort` function.
2. Ensure the docstring is clear and concise, describing the purpose of the function.
This modification will improve the code's documentation without altering its functionality.<|im_end|>
````
Script for SR:
````python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from eval.utils import prepare_input_for_sr
tokenizer = AutoTokenizer.from_pretrained("TechxGenus/CursorCore-Yi-1.5B-SR")
model = AutoModelForCausalLM.from_pretrained(
"TechxGenus/CursorCore-Yi-1.5B-SR",
torch_dtype=torch.bfloat16,
device_map="auto"
)
sample = {
"history": [],
"current": {
"type": "code",
"lang": "python",
"code": """def quick_sort(array):\n if len(arr) <= 1:\n return arr\n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n return quick_sort(left) + middle + quick_sort(right)"""
},
"user": "Add Docstring."
}
prompt = tokenizer.apply_chat_template(
prepare_input_for_sr(sample),
tokenize=False,
chat_template="assistant-conversation",
add_generation_prompt=True
)
inputs = tokenizer.encode(prompt, return_tensors="pt")
outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=512, do_sample=False)
print(tokenizer.decode(outputs[0]))
````
Output for SR:
````txt
<|im_start|>system
You are a helpful programming assistant.<|im_end|>
<|im_start|>current
```python
def quick_sort(array):
if len(arr) <= 1:
return arr
pivot = arr[len(arr) // 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quick_sort(left) + middle + quick_sort(right)
```<|im_end|>
<|im_start|>user
Add Docstring.<|im_end|>
<|im_start|>assistant
<|next_start|>```python
def quick_sort(array):
<|search_and_replace|>
def quick_sort(array):
"""
This function implements quick sort algorithm
"""
```<|next_end|><|im_end|>
````
### 3) Web Demo
We create a web demo for CursorCore. Please visit [CursorWeb](https://github.com/TechxGenus/CursorWeb) for more details.
## Future Work
CursorCore is still in a very early stage, and lots of work is needed to achieve a better user experience. For example:
- Repository-level editing support
- Better and faster editing formats
- Better user interface and presentation
- ...
## Citation
```bibtex
@article{jiang2024cursorcore,
title = {CursorCore: Assist Programming through Aligning Anything},
author = {Hao Jiang and Qi Liu and Rui Li and Shengyu Ye and Shijin Wang},
year = {2024},
journal = {arXiv preprint arXiv: 2410.07002}
}
```
## Contribution
Contributions are welcome! If you find any bugs or have suggestions for improvements, please open an issue or submit a pull request.
|
Danielrahmai1991/llama32_ganjoor_adapt_basic_model_16bit | Danielrahmai1991 | 2024-10-16T06:52:42Z | 128 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-16T06:50:58Z | ---
base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** Danielrahmai1991
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
seu5022/Qwen-2.5-Base-7b-SFT-Korean-Article-Dataset | seu5022 | 2024-10-16T06:51:52Z | 2,287 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-15T05:33:46Z | ---
library_name: transformers
tags: []
---
# 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]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
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## 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]
### Out-of-Scope Use
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[More Information Needed]
## 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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### 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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[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]
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[More Information Needed]
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[More Information Needed]
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<!-- 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]
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rosschan/CustomModel_news_summary | rosschan | 2024-10-16T06:47:23Z | 106 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-16T06:47:04Z | ---
library_name: transformers
tags: []
---
# 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. -->
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## 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]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- 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:**
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## Model Card Contact
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Yntec/Darkside | Yntec | 2024-10-16T06:46:17Z | 95 | 3 | diffusers | [
"diffusers",
"safetensors",
"Anime",
"Horror",
"Pixar",
"DucHaiten",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-09-29T06:54:29Z | ---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
tags:
- Anime
- Horror
- Pixar
- DucHaiten
- stable-diffusion
- stable-diffusion-diffusers
- diffusers
- text-to-image
---
# DucHaiten Darkside
fp16 no-ema version of this model: https://civitai.com/models/5426?modelVersionId=6311
Samples and prompt:
Cartoon Pretty CUTE Girl, ilya kuvshinov detailed, DETAILED CHIBI EYES, gorgeous detailed hair, high school, Magazine ad, iconic, 1949, sharp focus. visible brushstrokes By KlaysMoji and artgerm and Clay Mann and and simon cowell and leyendecker. By Dave Rapoza. Pretty CUTE girl. |
win10/phi-3.5-Sakura-Yuzu | win10 | 2024-10-16T06:34:56Z | 128 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"mergekit",
"merge",
"conversational",
"custom_code",
"arxiv:2306.01708",
"base_model:AXCXEPT/Borea-Phi-3.5-mini-Instruct-Common",
"base_model:merge:AXCXEPT/Borea-Phi-3.5-mini-Instruct-Common",
"base_model:ArliAI/Phi-3.5-mini-3.8B-ArliAI-RPMax-v1.1",
"base_model:merge:ArliAI/Phi-3.5-mini-3.8B-ArliAI-RPMax-v1.1",
"base_model:FreedomIntelligence/Apollo2-3.8B",
"base_model:merge:FreedomIntelligence/Apollo2-3.8B",
"base_model:microsoft/Phi-3.5-mini-instruct",
"base_model:merge:microsoft/Phi-3.5-mini-instruct",
"base_model:win10/Phi-3.5-mini-instruct-24-9-29",
"base_model:merge:win10/Phi-3.5-mini-instruct-24-9-29",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-16T06:32:37Z | ---
base_model:
- win10/Phi-3.5-mini-instruct-24-9-29
- FreedomIntelligence/Apollo2-3.8B
- ArliAI/Phi-3.5-mini-3.8B-ArliAI-RPMax-v1.1
- AXCXEPT/Borea-Phi-3.5-mini-Instruct-Common
- microsoft/Phi-3.5-mini-instruct
library_name: transformers
tags:
- mergekit
- merge
---
# 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 [TIES](https://arxiv.org/abs/2306.01708) merge method using [microsoft/Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct) as a base.
### Models Merged
The following models were included in the merge:
* [win10/Phi-3.5-mini-instruct-24-9-29](https://huggingface.co/win10/Phi-3.5-mini-instruct-24-9-29)
* [FreedomIntelligence/Apollo2-3.8B](https://huggingface.co/FreedomIntelligence/Apollo2-3.8B)
* [ArliAI/Phi-3.5-mini-3.8B-ArliAI-RPMax-v1.1](https://huggingface.co/ArliAI/Phi-3.5-mini-3.8B-ArliAI-RPMax-v1.1)
* [AXCXEPT/Borea-Phi-3.5-mini-Instruct-Common](https://huggingface.co/AXCXEPT/Borea-Phi-3.5-mini-Instruct-Common)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: microsoft/Phi-3.5-mini-instruct
#no parameters necessary for base model
- model: FreedomIntelligence/Apollo2-3.8B
parameters:
density: 1
weight: 1
- model: ArliAI/Phi-3.5-mini-3.8B-ArliAI-RPMax-v1.1
parameters:
density: 1
weight: 1
- model: AXCXEPT/Borea-Phi-3.5-mini-Instruct-Common
parameters:
density: 1
weight: 1
- model: win10/Phi-3.5-mini-instruct-24-9-29
parameters:
density: 1
weight: 1
merge_method: ties
base_model: microsoft/Phi-3.5-mini-instruct
parameters:
normalize: false
int8_mask: true
dtype: float16
```
|
MayBashendy/arabert_no_augmentation_organization_task1_fold0 | MayBashendy | 2024-10-16T06:33:29Z | 161 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:aubmindlab/bert-base-arabertv02",
"base_model:finetune:aubmindlab/bert-base-arabertv02",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-16T06:31:04Z | ---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: arabert_no_augmentation_organization_task1_fold0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# arabert_no_augmentation_organization_task1_fold0
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8071
- Qwk: 0.7786
- Mse: 0.8071
- Rmse: 0.8984
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:------:|:----:|:---------------:|:-------:|:------:|:------:|
| No log | 0.1818 | 2 | 4.6091 | -0.0084 | 4.6091 | 2.1469 |
| No log | 0.3636 | 4 | 2.7511 | 0.0000 | 2.7511 | 1.6586 |
| No log | 0.5455 | 6 | 1.9010 | -0.1625 | 1.9010 | 1.3788 |
| No log | 0.7273 | 8 | 1.2735 | 0.0930 | 1.2735 | 1.1285 |
| No log | 0.9091 | 10 | 1.3174 | 0.2584 | 1.3174 | 1.1478 |
| No log | 1.0909 | 12 | 1.2159 | 0.3084 | 1.2159 | 1.1027 |
| No log | 1.2727 | 14 | 1.1049 | 0.3923 | 1.1049 | 1.0511 |
| No log | 1.4545 | 16 | 0.9768 | 0.4541 | 0.9768 | 0.9883 |
| No log | 1.6364 | 18 | 1.2066 | 0.4134 | 1.2066 | 1.0985 |
| No log | 1.8182 | 20 | 1.4322 | 0.4085 | 1.4322 | 1.1967 |
| No log | 2.0 | 22 | 1.4533 | 0.3831 | 1.4533 | 1.2055 |
| No log | 2.1818 | 24 | 1.2317 | 0.5312 | 1.2317 | 1.1098 |
| No log | 2.3636 | 26 | 0.9282 | 0.4561 | 0.9282 | 0.9634 |
| No log | 2.5455 | 28 | 1.0926 | 0.5222 | 1.0926 | 1.0453 |
| No log | 2.7273 | 30 | 1.0693 | 0.5222 | 1.0693 | 1.0341 |
| No log | 2.9091 | 32 | 0.9590 | 0.5088 | 0.9590 | 0.9793 |
| No log | 3.0909 | 34 | 1.1455 | 0.5288 | 1.1455 | 1.0703 |
| No log | 3.2727 | 36 | 1.3818 | 0.5073 | 1.3818 | 1.1755 |
| No log | 3.4545 | 38 | 1.3091 | 0.5073 | 1.3091 | 1.1442 |
| No log | 3.6364 | 40 | 0.9953 | 0.5288 | 0.9953 | 0.9976 |
| No log | 3.8182 | 42 | 0.7863 | 0.4938 | 0.7863 | 0.8867 |
| No log | 4.0 | 44 | 0.8394 | 0.5254 | 0.8394 | 0.9162 |
| No log | 4.1818 | 46 | 0.7831 | 0.5399 | 0.7831 | 0.8849 |
| No log | 4.3636 | 48 | 0.7361 | 0.6087 | 0.7361 | 0.8580 |
| No log | 4.5455 | 50 | 1.0454 | 0.7268 | 1.0454 | 1.0224 |
| No log | 4.7273 | 52 | 1.2795 | 0.5743 | 1.2795 | 1.1312 |
| No log | 4.9091 | 54 | 1.2229 | 0.5896 | 1.2229 | 1.1058 |
| No log | 5.0909 | 56 | 1.0233 | 0.7526 | 1.0233 | 1.0116 |
| No log | 5.2727 | 58 | 0.8234 | 0.6087 | 0.8234 | 0.9074 |
| No log | 5.4545 | 60 | 0.7794 | 0.6087 | 0.7794 | 0.8828 |
| No log | 5.6364 | 62 | 0.8013 | 0.6087 | 0.8013 | 0.8952 |
| No log | 5.8182 | 64 | 0.8913 | 0.6182 | 0.8913 | 0.9441 |
| No log | 6.0 | 66 | 0.9996 | 0.6536 | 0.9996 | 0.9998 |
| No log | 6.1818 | 68 | 1.1340 | 0.6585 | 1.1340 | 1.0649 |
| No log | 6.3636 | 70 | 1.1480 | 0.6585 | 1.1480 | 1.0714 |
| No log | 6.5455 | 72 | 1.0158 | 0.7447 | 1.0158 | 1.0079 |
| No log | 6.7273 | 74 | 0.8611 | 0.6026 | 0.8611 | 0.9280 |
| No log | 6.9091 | 76 | 0.7947 | 0.6087 | 0.7947 | 0.8915 |
| No log | 7.0909 | 78 | 0.7768 | 0.6340 | 0.7768 | 0.8814 |
| No log | 7.2727 | 80 | 0.7826 | 0.5997 | 0.7826 | 0.8846 |
| No log | 7.4545 | 82 | 0.8277 | 0.7109 | 0.8277 | 0.9098 |
| No log | 7.6364 | 84 | 0.8633 | 0.7355 | 0.8633 | 0.9291 |
| No log | 7.8182 | 86 | 0.8868 | 0.7704 | 0.8868 | 0.9417 |
| No log | 8.0 | 88 | 0.9068 | 0.7704 | 0.9068 | 0.9523 |
| No log | 8.1818 | 90 | 0.9279 | 0.7704 | 0.9279 | 0.9633 |
| No log | 8.3636 | 92 | 0.8988 | 0.7955 | 0.8988 | 0.9481 |
| No log | 8.5455 | 94 | 0.8985 | 0.7955 | 0.8985 | 0.9479 |
| No log | 8.7273 | 96 | 0.8973 | 0.7955 | 0.8973 | 0.9473 |
| No log | 8.9091 | 98 | 0.8757 | 0.7955 | 0.8757 | 0.9358 |
| No log | 9.0909 | 100 | 0.8457 | 0.7955 | 0.8457 | 0.9196 |
| No log | 9.2727 | 102 | 0.8353 | 0.7786 | 0.8353 | 0.9140 |
| No log | 9.4545 | 104 | 0.8206 | 0.7786 | 0.8206 | 0.9059 |
| No log | 9.6364 | 106 | 0.8107 | 0.7786 | 0.8107 | 0.9004 |
| No log | 9.8182 | 108 | 0.8081 | 0.7786 | 0.8081 | 0.8990 |
| No log | 10.0 | 110 | 0.8071 | 0.7786 | 0.8071 | 0.8984 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
schnapper79/lumikabra_behemoth_195B_v2-exl2-6.0bpw | schnapper79 | 2024-10-16T06:07:03Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"6-bit",
"exl2",
"region:us"
] | text-generation | 2024-10-16T04:52:16Z | ---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# lumikabra_behemoth_195B_v2
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 della_linear merge method using /workspace/models/schnapper79_lumikabra-195B_v0.3 as a base.
### Models Merged
The following models were included in the merge:
* /workspace/merges/lumikabra_behemoth_195b
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: /workspace/merges/lumikabra_behemoth_195b
parameters:
weight: 0.5
density: 0.8
merge_method: della_linear
base_model: /workspace/models/schnapper79_lumikabra-195B_v0.3
parameters:
epsilon: 0.05
lambda: 1
int8_mask: true
dtype: bfloat16
```
|
Diksha2001/ollama-llama3.1-lora-V1 | Diksha2001 | 2024-10-16T06:06:51Z | 6 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/Meta-Llama-3.1-8B-bnb-4bit",
"base_model:quantized:unsloth/Meta-Llama-3.1-8B-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-10-15T06:53:12Z | ---
base_model: unsloth/Meta-Llama-3.1-8B-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
---
# Uploaded model
- **Developed by:** Diksha2001
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Meta-Llama-3.1-8B-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
schnapper79/lumikabra_behemoth_195B_v2-exl2-5.0bpw | schnapper79 | 2024-10-16T05:55:20Z | 6 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"5-bit",
"exl2",
"region:us"
] | text-generation | 2024-10-16T04:52:13Z | ---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# lumikabra_behemoth_195B_v2
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 della_linear merge method using /workspace/models/schnapper79_lumikabra-195B_v0.3 as a base.
### Models Merged
The following models were included in the merge:
* /workspace/merges/lumikabra_behemoth_195b
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: /workspace/merges/lumikabra_behemoth_195b
parameters:
weight: 0.5
density: 0.8
merge_method: della_linear
base_model: /workspace/models/schnapper79_lumikabra-195B_v0.3
parameters:
epsilon: 0.05
lambda: 1
int8_mask: true
dtype: bfloat16
```
|
OrangeEye/qwen-25-1.5b-base-sft | OrangeEye | 2024-10-16T05:49:05Z | 91 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-08T16:06:40Z | ---
library_name: transformers
tags: []
---
# 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]
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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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Hanisnabila/results | Hanisnabila | 2024-10-16T05:48:44Z | 112 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:cardiffnlp/twitter-roberta-base-sentiment-latest",
"base_model:finetune:cardiffnlp/twitter-roberta-base-sentiment-latest",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-16T01:55:26Z | ---
library_name: transformers
base_model: cardiffnlp/twitter-roberta-base-sentiment-latest
tags:
- generated_from_trainer
model-index:
- name: results
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4070
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 8e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 40
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 62 | 1.5829 |
| No log | 2.0 | 124 | 1.4903 |
| No log | 3.0 | 186 | 1.9193 |
| No log | 4.0 | 248 | 2.3094 |
| No log | 5.0 | 310 | 2.4070 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.2.2+cu118
- Datasets 3.0.1
- Tokenizers 0.20.0
|
mradermacher/Llama-3_2-1B-suicide-related-text-classification-GGUF | mradermacher | 2024-10-16T05:28:27Z | 8 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:AndresR2909/Llama-3_2-1B-suicide-related-text-classification",
"base_model:quantized:AndresR2909/Llama-3_2-1B-suicide-related-text-classification",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-16T05:26:55Z | ---
base_model: AndresR2909/Llama-3_2-1B-suicide-related-text-classification
language:
- en
library_name: transformers
quantized_by: mradermacher
tags: []
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/AndresR2909/Llama-3_2-1B-suicide-related-text-classification
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/Llama-3_2-1B-suicide-related-text-classification-GGUF/resolve/main/Llama-3_2-1B-suicide-related-text-classification.Q2_K.gguf) | Q2_K | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3_2-1B-suicide-related-text-classification-GGUF/resolve/main/Llama-3_2-1B-suicide-related-text-classification.Q3_K_S.gguf) | Q3_K_S | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3_2-1B-suicide-related-text-classification-GGUF/resolve/main/Llama-3_2-1B-suicide-related-text-classification.Q3_K_M.gguf) | Q3_K_M | 0.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3_2-1B-suicide-related-text-classification-GGUF/resolve/main/Llama-3_2-1B-suicide-related-text-classification.Q3_K_L.gguf) | Q3_K_L | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3_2-1B-suicide-related-text-classification-GGUF/resolve/main/Llama-3_2-1B-suicide-related-text-classification.IQ4_XS.gguf) | IQ4_XS | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3_2-1B-suicide-related-text-classification-GGUF/resolve/main/Llama-3_2-1B-suicide-related-text-classification.Q4_K_S.gguf) | Q4_K_S | 0.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3_2-1B-suicide-related-text-classification-GGUF/resolve/main/Llama-3_2-1B-suicide-related-text-classification.Q4_K_M.gguf) | Q4_K_M | 0.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3_2-1B-suicide-related-text-classification-GGUF/resolve/main/Llama-3_2-1B-suicide-related-text-classification.Q5_K_S.gguf) | Q5_K_S | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3_2-1B-suicide-related-text-classification-GGUF/resolve/main/Llama-3_2-1B-suicide-related-text-classification.Q5_K_M.gguf) | Q5_K_M | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3_2-1B-suicide-related-text-classification-GGUF/resolve/main/Llama-3_2-1B-suicide-related-text-classification.Q6_K.gguf) | Q6_K | 1.1 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3_2-1B-suicide-related-text-classification-GGUF/resolve/main/Llama-3_2-1B-suicide-related-text-classification.Q8_0.gguf) | Q8_0 | 1.4 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3_2-1B-suicide-related-text-classification-GGUF/resolve/main/Llama-3_2-1B-suicide-related-text-classification.f16.gguf) | f16 | 2.6 | 16 bpw, overkill |
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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
cuongdev/test-hntanh | cuongdev | 2024-10-16T05:25:24Z | 29 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-10-16T05:21:40Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### test-hntanh Dreambooth model trained by cuongdev with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
mav23/OpenHathi-7B-Hi-v0.1-Base-GGUF | mav23 | 2024-10-16T05:14:02Z | 10 | 0 | null | [
"gguf",
"hi",
"license:llama2",
"endpoints_compatible",
"region:us"
] | null | 2024-10-16T04:33:08Z | ---
license: llama2
language:
- hi
---
This repository is the first model in the OpenHathi series of models that will be released by Sarvam AI. This is a 7B parameter, based on Llama2, trained on Hindi, English, and Hinglish. More details about the model, its training procedure, and evaluations can be found [here](https://www.sarvam.ai/blog/announcing-openhathi-series).
Note: this is a base model and not meant to be used as is. We recommend first finetuning it on task(s) you are interested in.
```
# Usage
import torch
from transformers import LlamaTokenizer, LlamaForCausalLM
tokenizer = LlamaTokenizer.from_pretrained('sarvamai/OpenHathi-7B-Hi-v0.1-Base')
model = LlamaForCausalLM.from_pretrained('sarvamai/OpenHathi-7B-Hi-v0.1-Base', torch_dtype=torch.bfloat16)
prompt = "मैं एक अच्छा हाथी हूँ"
inputs = tokenizer(prompt, return_tensors="pt")
# Generate
generate_ids = model.generate(inputs.input_ids, max_length=30)
tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
``` |
gurevichan/Chad-Cooper-7948092_000110000 | gurevichan | 2024-10-16T05:12:29Z | 127 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-16T05:05:19Z | ---
library_name: transformers
tags: []
---
# 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] |
bren219/my_awesome_eli5_mlm_model | bren219 | 2024-10-16T05:09:50Z | 179 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"fill-mask",
"generated_from_trainer",
"dataset:eli5_category",
"base_model:distilbert/distilroberta-base",
"base_model:finetune:distilbert/distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2024-10-16T05:03:03Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilroberta-base
tags:
- generated_from_trainer
datasets:
- eli5_category
model-index:
- name: my_awesome_eli5_mlm_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_eli5_mlm_model
This model is a fine-tuned version of [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on the eli5_category dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0707
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.2459 | 1.0 | 1321 | 2.0769 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1
|
wq0613/koc_writter_model_1012 | wq0613 | 2024-10-16T04:54:43Z | 13 | 0 | transformers | [
"transformers",
"pytorch",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/Meta-Llama-3.1-8B-bnb-4bit",
"base_model:quantized:unsloth/Meta-Llama-3.1-8B-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-10-16T03:26:17Z | ---
base_model: unsloth/Meta-Llama-3.1-8B-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
---
# Uploaded model
- **Developed by:** wq0613
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Meta-Llama-3.1-8B-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
phuongntc/vit5_base_align_translate | phuongntc | 2024-10-16T04:40:26Z | 114 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-10-16T04:39:42Z | ---
library_name: transformers
tags: []
---
# 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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mav23/Yi-6B-GGUF | mav23 | 2024-10-16T04:28:18Z | 27 | 0 | null | [
"gguf",
"text-generation",
"arxiv:2403.04652",
"arxiv:2311.16502",
"arxiv:2401.11944",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-16T03:50:47Z | ---
license: apache-2.0
widget:
- example_title: "Yi-34B-Chat"
text: "hi"
output:
text: " Hello! How can I assist you today?"
- example_title: "Yi-34B"
text: "There's a place where time stands still. A place of breath taking wonder, but also"
output:
text: " an eerie sense that something is just not right…\nBetween the two worlds lies The Forgotten Kingdom - home to creatures long since thought extinct and ancient magic so strong it defies belief! Only here can you find what has been lost for centuries: An Elixir Of Life which will restore youth and vitality if only those who seek its power are brave enough to face up against all manner of dangers lurking in this mysterious land! But beware; some say there may even exist powerful entities beyond our comprehension whose intentions towards humanity remain unclear at best ---- they might want nothing more than destruction itself rather then anything else from their quest after immortality (and maybe someone should tell them about modern medicine)? In any event though – one thing remains true regardless : whether or not success comes easy depends entirely upon how much effort we put into conquering whatever challenges lie ahead along with having faith deep down inside ourselves too ;) So let’s get started now shall We?"
pipeline_tag: text-generation
new_version: 01-ai/Yi-1.5-6B
---
<div align="center">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_dark.svg" width="200px">
<source media="(prefers-color-scheme: light)" srcset="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="200px">
<img alt="specify theme context for images" src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg">
</picture>
</br>
</br>
<div style="display: inline-block;">
<a href="https://github.com/01-ai/Yi/actions/workflows/build_docker_image.yml">
<img src="https://github.com/01-ai/Yi/actions/workflows/build_docker_image.yml/badge.svg">
</a>
</div>
<div style="display: inline-block;">
<a href="mailto:[email protected]">
<img src="https://img.shields.io/badge/✉️[email protected]">
</a>
</div>
</div>
<div align="center">
<h3 align="center">Building the Next Generation of Open-Source and Bilingual LLMs</h3>
</div>
<p align="center">
🤗 <a href="https://huggingface.co/01-ai" target="_blank">Hugging Face</a> • 🤖 <a href="https://www.modelscope.cn/organization/01ai/" target="_blank">ModelScope</a> • ✡️ <a href="https://wisemodel.cn/organization/01.AI" target="_blank">WiseModel</a>
</p>
<p align="center">
👩🚀 Ask questions or discuss ideas on <a href="https://github.com/01-ai/Yi/discussions" target="_blank"> GitHub </a>
</p>
<p align="center">
👋 Join us on <a href="https://discord.gg/hYUwWddeAu" target="_blank"> 👾 Discord </a> or <a href="有官方的微信群嘛 · Issue #43 · 01-ai/Yi" target="_blank"> 💬 WeChat </a>
</p>
<p align="center">
📝 Check out <a href="https://arxiv.org/abs/2403.04652"> Yi Tech Report </a>
</p>
<p align="center">
📚 Grow at <a href="#learning-hub"> Yi Learning Hub </a>
</p>
<!-- DO NOT REMOVE ME -->
<hr>
<details open>
<summary></b>📕 Table of Contents</b></summary>
- [What is Yi?](#what-is-yi)
- [Introduction](#introduction)
- [Models](#models)
- [Chat models](#chat-models)
- [Base models](#base-models)
- [Model info](#model-info)
- [News](#news)
- [How to use Yi?](#how-to-use-yi)
- [Quick start](#quick-start)
- [Choose your path](#choose-your-path)
- [pip](#quick-start---pip)
- [docker](#quick-start---docker)
- [llama.cpp](#quick-start---llamacpp)
- [conda-lock](#quick-start---conda-lock)
- [Web demo](#web-demo)
- [Fine-tuning](#fine-tuning)
- [Quantization](#quantization)
- [Deployment](#deployment)
- [FAQ](#faq)
- [Learning hub](#learning-hub)
- [Why Yi?](#why-yi)
- [Ecosystem](#ecosystem)
- [Upstream](#upstream)
- [Downstream](#downstream)
- [Serving](#serving)
- [Quantization](#quantization-1)
- [Fine-tuning](#fine-tuning-1)
- [API](#api)
- [Benchmarks](#benchmarks)
- [Base model performance](#base-model-performance)
- [Chat model performance](#chat-model-performance)
- [Tech report](#tech-report)
- [Citation](#citation)
- [Who can use Yi?](#who-can-use-yi)
- [Misc.](#misc)
- [Acknowledgements](#acknowledgments)
- [Disclaimer](#disclaimer)
- [License](#license)
</details>
<hr>
# What is Yi?
## Introduction
- 🤖 The Yi series models are the next generation of open-source large language models trained from scratch by [01.AI](https://01.ai/).
- 🙌 Targeted as a bilingual language model and trained on 3T multilingual corpus, the Yi series models become one of the strongest LLM worldwide, showing promise in language understanding, commonsense reasoning, reading comprehension, and more. For example,
- Yi-34B-Chat model **landed in second place (following GPT-4 Turbo)**, outperforming other LLMs (such as GPT-4, Mixtral, Claude) on the AlpacaEval Leaderboard (based on data available up to January 2024).
- Yi-34B model **ranked first among all existing open-source models** (such as Falcon-180B, Llama-70B, Claude) in **both English and Chinese** on various benchmarks, including Hugging Face Open LLM Leaderboard (pre-trained) and C-Eval (based on data available up to November 2023).
- 🙏 (Credits to Llama) Thanks to the Transformer and Llama open-source communities, as they reduce the efforts required to build from scratch and enable the utilization of the same tools within the AI ecosystem.
<details style="display: inline;"><summary> If you're interested in Yi's adoption of Llama architecture and license usage policy, see <span style="color: green;">Yi's relation with Llama.</span> ⬇️</summary> <ul> <br>
> 💡 TL;DR
>
> The Yi series models adopt the same model architecture as Llama but are **NOT** derivatives of Llama.
- Both Yi and Llama are based on the Transformer structure, which has been the standard architecture for large language models since 2018.
- Grounded in the Transformer architecture, Llama has become a new cornerstone for the majority of state-of-the-art open-source models due to its excellent stability, reliable convergence, and robust compatibility. This positions Llama as the recognized foundational framework for models including Yi.
- Thanks to the Transformer and Llama architectures, other models can leverage their power, reducing the effort required to build from scratch and enabling the utilization of the same tools within their ecosystems.
- However, the Yi series models are NOT derivatives of Llama, as they do not use Llama's weights.
- As Llama's structure is employed by the majority of open-source models, the key factors of determining model performance are training datasets, training pipelines, and training infrastructure.
- Developing in a unique and proprietary way, Yi has independently created its own high-quality training datasets, efficient training pipelines, and robust training infrastructure entirely from the ground up. This effort has led to excellent performance with Yi series models ranking just behind GPT4 and surpassing Llama on the [Alpaca Leaderboard in Dec 2023](https://tatsu-lab.github.io/alpaca_eval/).
</ul>
</details>
<p align="right"> [
<a href="#top">Back to top ⬆️ </a> ]
</p>
## News
<details>
<summary>🔥 <b>2024-07-29</b>: The <a href="https://github.com/Haijian06/Yi/tree/main/Cookbook">Yi Cookbook 1.0 </a> is released, featuring tutorials and examples in both Chinese and English.</summary>
</details>
<details>
<summary>🎯 <b>2024-05-13</b>: The <a href="https://github.com/01-ai/Yi-1.5">Yi-1.5 series models </a> are open-sourced, further improving coding, math, reasoning, and instruction-following abilities.</summary>
</details>
<details>
<summary>🎯 <b>2024-03-16</b>: The <code>Yi-9B-200K</code> is open-sourced and available to the public.</summary>
</details>
<details>
<summary>🎯 <b>2024-03-08</b>: <a href="https://arxiv.org/abs/2403.04652">Yi Tech Report</a> is published! </summary>
</details>
<details open>
<summary>🔔 <b>2024-03-07</b>: The long text capability of the Yi-34B-200K has been enhanced. </summary>
<br>
In the "Needle-in-a-Haystack" test, the Yi-34B-200K's performance is improved by 10.5%, rising from 89.3% to an impressive 99.8%. We continue to pre-train the model on 5B tokens long-context data mixture and demonstrate a near-all-green performance.
</details>
<details open>
<summary>🎯 <b>2024-03-06</b>: The <code>Yi-9B</code> is open-sourced and available to the public.</summary>
<br>
<code>Yi-9B</code> stands out as the top performer among a range of similar-sized open-source models (including Mistral-7B, SOLAR-10.7B, Gemma-7B, DeepSeek-Coder-7B-Base-v1.5 and more), particularly excelling in code, math, common-sense reasoning, and reading comprehension.
</details>
<details open>
<summary>🎯 <b>2024-01-23</b>: The Yi-VL models, <code><a href="https://huggingface.co/01-ai/Yi-VL-34B">Yi-VL-34B</a></code> and <code><a href="https://huggingface.co/01-ai/Yi-VL-6B">Yi-VL-6B</a></code>, are open-sourced and available to the public.</summary>
<br>
<code><a href="https://huggingface.co/01-ai/Yi-VL-34B">Yi-VL-34B</a></code> has ranked <strong>first</strong> among all existing open-source models in the latest benchmarks, including <a href="https://arxiv.org/abs/2311.16502">MMMU</a> and <a href="https://arxiv.org/abs/2401.11944">CMMMU</a> (based on data available up to January 2024).</li>
</details>
<details>
<summary>🎯 <b>2023-11-23</b>: <a href="#chat-models">Chat models</a> are open-sourced and available to the public.</summary>
<br>This release contains two chat models based on previously released base models, two 8-bit models quantized by GPTQ, and two 4-bit models quantized by AWQ.
- `Yi-34B-Chat`
- `Yi-34B-Chat-4bits`
- `Yi-34B-Chat-8bits`
- `Yi-6B-Chat`
- `Yi-6B-Chat-4bits`
- `Yi-6B-Chat-8bits`
You can try some of them interactively at:
- [Hugging Face](https://huggingface.co/spaces/01-ai/Yi-34B-Chat)
- [Replicate](https://replicate.com/01-ai)
</details>
<details>
<summary>🔔 <b>2023-11-23</b>: The Yi Series Models Community License Agreement is updated to <a href="https://github.com/01-ai/Yi/blob/main/MODEL_LICENSE_AGREEMENT.txt">v2.1</a>.</summary>
</details>
<details>
<summary>🔥 <b>2023-11-08</b>: Invited test of Yi-34B chat model.</summary>
<br>Application form:
- [English](https://cn.mikecrm.com/l91ODJf)
- [Chinese](https://cn.mikecrm.com/gnEZjiQ)
</details>
<details>
<summary>🎯 <b>2023-11-05</b>: <a href="#base-models">The base models, </a><code>Yi-6B-200K</code> and <code>Yi-34B-200K</code>, are open-sourced and available to the public.</summary>
<br>This release contains two base models with the same parameter sizes as the previous
release, except that the context window is extended to 200K.
</details>
<details>
<summary>🎯 <b>2023-11-02</b>: <a href="#base-models">The base models, </a><code>Yi-6B</code> and <code>Yi-34B</code>, are open-sourced and available to the public.</summary>
<br>The first public release contains two bilingual (English/Chinese) base models
with the parameter sizes of 6B and 34B. Both of them are trained with 4K
sequence length and can be extended to 32K during inference time.
</details>
<p align="right"> [
<a href="#top">Back to top ⬆️ </a> ]
</p>
## Models
Yi models come in multiple sizes and cater to different use cases. You can also fine-tune Yi models to meet your specific requirements.
If you want to deploy Yi models, make sure you meet the [software and hardware requirements](#deployment).
### Chat models
| Model | Download |
|---|---|
|Yi-34B-Chat | • [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-34B-Chat) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-34B-Chat/summary) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-34B-Chat) |
|Yi-34B-Chat-4bits | • [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-34B-Chat-4bits) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-34B-Chat-4bits/summary) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-34B-Chat-4bits) |
|Yi-34B-Chat-8bits | • [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-34B-Chat-8bits) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-34B-Chat-8bits/summary) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-34B-Chat-8bits) |
|Yi-6B-Chat| • [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-6B-Chat) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-6B-Chat/summary) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-6B-Chat) |
|Yi-6B-Chat-4bits | • [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-6B-Chat-4bits) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-6B-Chat-4bits/summary) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-6B-Chat-4bits) |
|Yi-6B-Chat-8bits | • [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-6B-Chat-8bits) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-6B-Chat-8bits/summary) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-6B-Chat-8bits) |
<sub><sup> - 4-bit series models are quantized by AWQ. <br> - 8-bit series models are quantized by GPTQ <br> - All quantized models have a low barrier to use since they can be deployed on consumer-grade GPUs (e.g., 3090, 4090). </sup></sub>
### Base models
| Model | Download |
|---|---|
|Yi-34B| • [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-34B) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-34B/summary) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-6B-Chat-8bits) |
|Yi-34B-200K|• [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-34B-200K) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-34B-200K/summary) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-6B-Chat-8bits)|
|Yi-9B|• [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-9B) • [🤖 ModelScope](https://wisemodel.cn/models/01.AI/Yi-6B-Chat-8bits) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-9B)|
|Yi-9B-200K | • [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-9B-200K) • [🤖 ModelScope](https://wisemodel.cn/models/01.AI/Yi-9B-200K) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-6B-Chat-8bits) |
|Yi-6B| • [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-6B) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-6B/summary) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-6B-Chat-8bits) |
|Yi-6B-200K | • [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-6B-200K) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-6B-200K/summary) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-6B-Chat-8bits) |
<sub><sup> - 200k is roughly equivalent to 400,000 Chinese characters. <br> - If you want to use the previous version of the Yi-34B-200K (released on Nov 5, 2023), run `git checkout 069cd341d60f4ce4b07ec394e82b79e94f656cf` to download the weight. </sup></sub>
### Model info
- For chat and base models
<table>
<thead>
<tr>
<th>Model</th>
<th>Intro</th>
<th>Default context window</th>
<th>Pretrained tokens</th>
<th>Training Data Date</th>
</tr>
</thead>
<tbody><tr>
<td>6B series models</td>
<td>They are suitable for personal and academic use.</td>
<td rowspan="3">4K</td>
<td>3T</td>
<td rowspan="3">Up to June 2023</td>
</tr>
<tr>
<td>9B series models</td>
<td>It is the best at coding and math in the Yi series models.</td>
<td>Yi-9B is continuously trained based on Yi-6B, using 0.8T tokens.</td>
</tr>
<tr>
<td>34B series models</td>
<td>They are suitable for personal, academic, and commercial (particularly for small and medium-sized enterprises) purposes. It's a cost-effective solution that's affordable and equipped with emergent ability.</td>
<td>3T</td>
</tr>
</tbody></table>
- For chat models
<details style="display: inline;"><summary>For chat model limitations, see the explanations below. ⬇️</summary>
<ul>
<br>The released chat model has undergone exclusive training using Supervised Fine-Tuning (SFT). Compared to other standard chat models, our model produces more diverse responses, making it suitable for various downstream tasks, such as creative scenarios. Furthermore, this diversity is expected to enhance the likelihood of generating higher quality responses, which will be advantageous for subsequent Reinforcement Learning (RL) training.
<br>However, this higher diversity might amplify certain existing issues, including:
<li>Hallucination: This refers to the model generating factually incorrect or nonsensical information. With the model's responses being more varied, there's a higher chance of hallucination that are not based on accurate data or logical reasoning.</li>
<li>Non-determinism in re-generation: When attempting to regenerate or sample responses, inconsistencies in the outcomes may occur. The increased diversity can lead to varying results even under similar input conditions.</li>
<li>Cumulative Error: This occurs when errors in the model's responses compound over time. As the model generates more diverse responses, the likelihood of small inaccuracies building up into larger errors increases, especially in complex tasks like extended reasoning, mathematical problem-solving, etc.</li>
<li>To achieve more coherent and consistent responses, it is advisable to adjust generation configuration parameters such as temperature, top_p, or top_k. These adjustments can help in the balance between creativity and coherence in the model's outputs.</li>
</ul>
</details>
<p align="right"> [
<a href="#top">Back to top ⬆️ </a> ]
</p>
# How to use Yi?
- [Quick start](#quick-start)
- [Choose your path](#choose-your-path)
- [pip](#quick-start---pip)
- [docker](#quick-start---docker)
- [conda-lock](#quick-start---conda-lock)
- [llama.cpp](#quick-start---llamacpp)
- [Web demo](#web-demo)
- [Fine-tuning](#fine-tuning)
- [Quantization](#quantization)
- [Deployment](#deployment)
- [FAQ](#faq)
- [Learning hub](#learning-hub)
## Quick start
Getting up and running with Yi models is simple with multiple choices available.
### Choose your path
Select one of the following paths to begin your journey with Yi!
#### 🎯 Deploy Yi locally
If you prefer to deploy Yi models locally,
- 🙋♀️ and you have **sufficient** resources (for example, NVIDIA A800 80GB), you can choose one of the following methods:
- [pip](#quick-start---pip)
- [Docker](#quick-start---docker)
- [conda-lock](#quick-start---conda-lock)
- 🙋♀️ and you have **limited** resources (for example, a MacBook Pro), you can use [llama.cpp](#quick-start---llamacpp).
#### 🎯 Not to deploy Yi locally
If you prefer not to deploy Yi models locally, you can explore Yi's capabilities using any of the following options.
##### 🙋♀️ Run Yi with APIs
If you want to explore more features of Yi, you can adopt one of these methods:
- Yi APIs (Yi official)
- [Early access has been granted](https://x.com/01AI_Yi/status/1735728934560600536?s=20) to some applicants. Stay tuned for the next round of access!
- [Yi APIs](https://replicate.com/01-ai/yi-34b-chat/api?tab=nodejs) (Replicate)
##### 🙋♀️ Run Yi in playground
If you want to chat with Yi with more customizable options (e.g., system prompt, temperature, repetition penalty, etc.), you can try one of the following options:
- [Yi-34B-Chat-Playground](https://platform.lingyiwanwu.com/prompt/playground) (Yi official)
- Access is available through a whitelist. Welcome to apply (fill out a form in [English](https://cn.mikecrm.com/l91ODJf) or [Chinese](https://cn.mikecrm.com/gnEZjiQ)).
- [Yi-34B-Chat-Playground](https://replicate.com/01-ai/yi-34b-chat) (Replicate)
##### 🙋♀️ Chat with Yi
If you want to chat with Yi, you can use one of these online services, which offer a similar user experience:
- [Yi-34B-Chat](https://huggingface.co/spaces/01-ai/Yi-34B-Chat) (Yi official on Hugging Face)
- No registration is required.
- [Yi-34B-Chat](https://platform.lingyiwanwu.com/) (Yi official beta)
- Access is available through a whitelist. Welcome to apply (fill out a form in [English](https://cn.mikecrm.com/l91ODJf) or [Chinese](https://cn.mikecrm.com/gnEZjiQ)).
<p align="right"> [
<a href="#top">Back to top ⬆️ </a> ]
</p>
### Quick start - pip
This tutorial guides you through every step of running **Yi-34B-Chat locally on an A800 (80G)** and then performing inference.
#### Step 0: Prerequisites
- Make sure Python 3.10 or a later version is installed.
- If you want to run other Yi models, see [software and hardware requirements](#deployment).
#### Step 1: Prepare your environment
To set up the environment and install the required packages, execute the following command.
```bash
git clone https://github.com/01-ai/Yi.git
cd yi
pip install -r requirements.txt
```
#### Step 2: Download the Yi model
You can download the weights and tokenizer of Yi models from the following sources:
- [Hugging Face](https://huggingface.co/01-ai)
- [ModelScope](https://www.modelscope.cn/organization/01ai/)
- [WiseModel](https://wisemodel.cn/organization/01.AI)
#### Step 3: Perform inference
You can perform inference with Yi chat or base models as below.
##### Perform inference with Yi chat model
1. Create a file named `quick_start.py` and copy the following content to it.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = '<your-model-path>'
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
# Since transformers 4.35.0, the GPT-Q/AWQ model can be loaded using AutoModelForCausalLM.
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)
```
2. Run `quick_start.py`.
```bash
python quick_start.py
```
Then you can see an output similar to the one below. 🥳
```bash
Hello! How can I assist you today?
```
##### Perform inference with Yi base model
- Yi-34B
The steps are similar to [pip - Perform inference with Yi chat model](#perform-inference-with-yi-chat-model).
You can use the existing file [`text_generation.py`](https://github.com/01-ai/Yi/tree/main/demo).
```bash
python demo/text_generation.py --model <your-model-path>
```
Then you can see an output similar to the one below. 🥳
<details>
<summary>Output. ⬇️ </summary>
<br>
**Prompt**: Let me tell you an interesting story about cat Tom and mouse Jerry,
**Generation**: Let me tell you an interesting story about cat Tom and mouse Jerry, which happened in my childhood. My father had a big house with two cats living inside it to kill mice. One day when I was playing at home alone, I found one of the tomcats lying on his back near our kitchen door, looking very much like he wanted something from us but couldn’t get up because there were too many people around him! He kept trying for several minutes before finally giving up...
</details>
- Yi-9B
Input
```bash
from transformers import AutoModelForCausalLM, AutoTokenizer
MODEL_DIR = "01-ai/Yi-9B"
model = AutoModelForCausalLM.from_pretrained(MODEL_DIR, torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR, use_fast=False)
input_text = "# write the quick sort algorithm"
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_length=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
Output
```bash
# write the quick sort algorithm
def quick_sort(arr):
if len(arr) <= 1:
return arr
pivot = arr[len(arr) // 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quick_sort(left) + middle + quick_sort(right)
# test the quick sort algorithm
print(quick_sort([3, 6, 8, 10, 1, 2, 1]))
```
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</p>
### Quick start - Docker
<details>
<summary> Run Yi-34B-chat locally with Docker: a step-by-step guide. ⬇️</summary>
<br>This tutorial guides you through every step of running <strong>Yi-34B-Chat on an A800 GPU</strong> or <strong>4*4090</strong> locally and then performing inference.
<h4>Step 0: Prerequisites</h4>
<p>Make sure you've installed <a href="https://docs.docker.com/engine/install/?open_in_browser=true">Docker</a> and <a href="https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html">nvidia-container-toolkit</a>.</p>
<h4> Step 1: Start Docker </h4>
<pre><code>docker run -it --gpus all \
-v <your-model-path>: /models
ghcr.io/01-ai/yi:latest
</code></pre>
<p>Alternatively, you can pull the Yi Docker image from <code>registry.lingyiwanwu.com/ci/01-ai/yi:latest</code>.</p>
<h4>Step 2: Perform inference</h4>
<p>You can perform inference with Yi chat or base models as below.</p>
<h5>Perform inference with Yi chat model</h5>
<p>The steps are similar to <a href="#perform-inference-with-yi-chat-model">pip - Perform inference with Yi chat model</a>.</p>
<p><strong>Note</strong> that the only difference is to set <code>model_path = '<your-model-mount-path>'</code> instead of <code>model_path = '<your-model-path>'</code>.</p>
<h5>Perform inference with Yi base model</h5>
<p>The steps are similar to <a href="#perform-inference-with-yi-base-model">pip - Perform inference with Yi base model</a>.</p>
<p><strong>Note</strong> that the only difference is to set <code>--model <your-model-mount-path>'</code> instead of <code>model <your-model-path></code>.</p>
</details>
### Quick start - conda-lock
<details>
<summary>You can use <code><a href="https://github.com/conda/conda-lock">conda-lock</a></code> to generate fully reproducible lock files for conda environments. ⬇️</summary>
<br>
You can refer to <a href="https://github.com/01-ai/Yi/blob/ebba23451d780f35e74a780987ad377553134f68/conda-lock.yml">conda-lock.yml</a> for the exact versions of the dependencies. Additionally, you can utilize <code><a href="https://mamba.readthedocs.io/en/latest/user_guide/micromamba.html">micromamba</a></code> for installing these dependencies.
<br>
To install the dependencies, follow these steps:
1. Install micromamba by following the instructions available <a href="https://mamba.readthedocs.io/en/latest/installation/micromamba-installation.html">here</a>.
2. Execute <code>micromamba install -y -n yi -f conda-lock.yml</code> to create a conda environment named <code>yi</code> and install the necessary dependencies.
</details>
### Quick start - llama.cpp
<a href="https://github.com/01-ai/Yi/blob/main/docs/README_llama.cpp.md">The following tutorial </a> will guide you through every step of running a quantized model (<a href="https://huggingface.co/XeIaso/yi-chat-6B-GGUF/tree/main">Yi-chat-6B-2bits</a>) locally and then performing inference.
<details>
<summary> Run Yi-chat-6B-2bits locally with llama.cpp: a step-by-step guide. ⬇️</summary>
<br><a href="https://github.com/01-ai/Yi/blob/main/docs/README_llama.cpp.md">This tutorial</a> guides you through every step of running a quantized model (<a href="https://huggingface.co/XeIaso/yi-chat-6B-GGUF/tree/main">Yi-chat-6B-2bits</a>) locally and then performing inference.</p>
- [Step 0: Prerequisites](#step-0-prerequisites)
- [Step 1: Download llama.cpp](#step-1-download-llamacpp)
- [Step 2: Download Yi model](#step-2-download-yi-model)
- [Step 3: Perform inference](#step-3-perform-inference)
#### Step 0: Prerequisites
- This tutorial assumes you use a MacBook Pro with 16GB of memory and an Apple M2 Pro chip.
- Make sure [`git-lfs`](https://git-lfs.com/) is installed on your machine.
#### Step 1: Download `llama.cpp`
To clone the [`llama.cpp`](https://github.com/ggerganov/llama.cpp) repository, run the following command.
```bash
git clone [email protected]:ggerganov/llama.cpp.git
```
#### Step 2: Download Yi model
2.1 To clone [XeIaso/yi-chat-6B-GGUF](https://huggingface.co/XeIaso/yi-chat-6B-GGUF/tree/main) with just pointers, run the following command.
```bash
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/XeIaso/yi-chat-6B-GGUF
```
2.2 To download a quantized Yi model ([yi-chat-6b.Q2_K.gguf](https://huggingface.co/XeIaso/yi-chat-6B-GGUF/blob/main/yi-chat-6b.Q2_K.gguf)), run the following command.
```bash
git-lfs pull --include yi-chat-6b.Q2_K.gguf
```
#### Step 3: Perform inference
To perform inference with the Yi model, you can use one of the following methods.
- [Method 1: Perform inference in terminal](#method-1-perform-inference-in-terminal)
- [Method 2: Perform inference in web](#method-2-perform-inference-in-web)
##### Method 1: Perform inference in terminal
To compile `llama.cpp` using 4 threads and then conduct inference, navigate to the `llama.cpp` directory, and run the following command.
> ##### Tips
>
> - Replace `/Users/yu/yi-chat-6B-GGUF/yi-chat-6b.Q2_K.gguf` with the actual path of your model.
>
> - By default, the model operates in completion mode.
>
> - For additional output customization options (for example, system prompt, temperature, repetition penalty, etc.), run `./main -h` to check detailed descriptions and usage.
```bash
make -j4 && ./main -m /Users/yu/yi-chat-6B-GGUF/yi-chat-6b.Q2_K.gguf -p "How do you feed your pet fox? Please answer this question in 6 simple steps:\nStep 1:" -n 384 -e
...
How do you feed your pet fox? Please answer this question in 6 simple steps:
Step 1: Select the appropriate food for your pet fox. You should choose high-quality, balanced prey items that are suitable for their unique dietary needs. These could include live or frozen mice, rats, pigeons, or other small mammals, as well as fresh fruits and vegetables.
Step 2: Feed your pet fox once or twice a day, depending on the species and its individual preferences. Always ensure that they have access to fresh water throughout the day.
Step 3: Provide an appropriate environment for your pet fox. Ensure it has a comfortable place to rest, plenty of space to move around, and opportunities to play and exercise.
Step 4: Socialize your pet with other animals if possible. Interactions with other creatures can help them develop social skills and prevent boredom or stress.
Step 5: Regularly check for signs of illness or discomfort in your fox. Be prepared to provide veterinary care as needed, especially for common issues such as parasites, dental health problems, or infections.
Step 6: Educate yourself about the needs of your pet fox and be aware of any potential risks or concerns that could affect their well-being. Regularly consult with a veterinarian to ensure you are providing the best care.
...
```
Now you have successfully asked a question to the Yi model and got an answer! 🥳
##### Method 2: Perform inference in web
1. To initialize a lightweight and swift chatbot, run the following command.
```bash
cd llama.cpp
./server --ctx-size 2048 --host 0.0.0.0 --n-gpu-layers 64 --model /Users/yu/yi-chat-6B-GGUF/yi-chat-6b.Q2_K.gguf
```
Then you can get an output like this:
```bash
...
llama_new_context_with_model: n_ctx = 2048
llama_new_context_with_model: freq_base = 5000000.0
llama_new_context_with_model: freq_scale = 1
ggml_metal_init: allocating
ggml_metal_init: found device: Apple M2 Pro
ggml_metal_init: picking default device: Apple M2 Pro
ggml_metal_init: ggml.metallib not found, loading from source
ggml_metal_init: GGML_METAL_PATH_RESOURCES = nil
ggml_metal_init: loading '/Users/yu/llama.cpp/ggml-metal.metal'
ggml_metal_init: GPU name: Apple M2 Pro
ggml_metal_init: GPU family: MTLGPUFamilyApple8 (1008)
ggml_metal_init: hasUnifiedMemory = true
ggml_metal_init: recommendedMaxWorkingSetSize = 11453.25 MB
ggml_metal_init: maxTransferRate = built-in GPU
ggml_backend_metal_buffer_type_alloc_buffer: allocated buffer, size = 128.00 MiB, ( 2629.44 / 10922.67)
llama_new_context_with_model: KV self size = 128.00 MiB, K (f16): 64.00 MiB, V (f16): 64.00 MiB
ggml_backend_metal_buffer_type_alloc_buffer: allocated buffer, size = 0.02 MiB, ( 2629.45 / 10922.67)
llama_build_graph: non-view tensors processed: 676/676
llama_new_context_with_model: compute buffer total size = 159.19 MiB
ggml_backend_metal_buffer_type_alloc_buffer: allocated buffer, size = 156.02 MiB, ( 2785.45 / 10922.67)
Available slots:
-> Slot 0 - max context: 2048
llama server listening at http://0.0.0.0:8080
```
2. To access the chatbot interface, open your web browser and enter `http://0.0.0.0:8080` into the address bar.
3. Enter a question, such as "How do you feed your pet fox? Please answer this question in 6 simple steps" into the prompt window, and you will receive a corresponding answer.
</ul>
</details>
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</p>
### Web demo
You can build a web UI demo for Yi **chat** models (note that Yi base models are not supported in this senario).
[Step 1: Prepare your environment](#step-1-prepare-your-environment).
[Step 2: Download the Yi model](#step-2-download-the-yi-model).
Step 3. To start a web service locally, run the following command.
```bash
python demo/web_demo.py -c <your-model-path>
```
You can access the web UI by entering the address provided in the console into your browser.
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</p>
### Fine-tuning
```bash
bash finetune/scripts/run_sft_Yi_6b.sh
```
Once finished, you can compare the finetuned model and the base model with the following command:
```bash
bash finetune/scripts/run_eval.sh
```
<details style="display: inline;"><summary>For advanced usage (like fine-tuning based on your custom data), see the explanations below. ⬇️ </summary> <ul>
### Finetune code for Yi 6B and 34B
#### Preparation
##### From Image
By default, we use a small dataset from [BAAI/COIG](https://huggingface.co/datasets/BAAI/COIG) to finetune the base model.
You can also prepare your customized dataset in the following `jsonl` format:
```json
{ "prompt": "Human: Who are you? Assistant:", "chosen": "I'm Yi." }
```
And then mount them in the container to replace the default ones:
```bash
docker run -it \
-v /path/to/save/finetuned/model/:/finetuned-model \
-v /path/to/train.jsonl:/yi/finetune/data/train.json \
-v /path/to/eval.jsonl:/yi/finetune/data/eval.json \
ghcr.io/01-ai/yi:latest \
bash finetune/scripts/run_sft_Yi_6b.sh
```
##### From Local Server
Make sure you have conda. If not, use
```bash
mkdir -p ~/miniconda3
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh
bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
rm -rf ~/miniconda3/miniconda.sh
~/miniconda3/bin/conda init bash
source ~/.bashrc
```
Then, create a conda env:
```bash
conda create -n dev_env python=3.10 -y
conda activate dev_env
pip install torch==2.0.1 deepspeed==0.10 tensorboard transformers datasets sentencepiece accelerate ray==2.7
```
#### Hardware Setup
For the Yi-6B model, a node with 4 GPUs, each with GPU memory larger than 60GB, is recommended.
For the Yi-34B model, because the usage of the zero-offload technique consumes a lot of CPU memory, please be careful to limit the number of GPUs in the 34B finetune training. Please use CUDA_VISIBLE_DEVICES to limit the number of GPUs (as shown in scripts/run_sft_Yi_34b.sh).
A typical hardware setup for finetuning the 34B model is a node with 8 GPUs (limited to 4 in running by CUDA_VISIBLE_DEVICES=0,1,2,3), each with GPU memory larger than 80GB, and total CPU memory larger than 900GB.
#### Quick Start
Download a LLM-base model to MODEL_PATH (6B and 34B). A typical folder of models is like:
```bash
|-- $MODEL_PATH
| |-- config.json
| |-- pytorch_model-00001-of-00002.bin
| |-- pytorch_model-00002-of-00002.bin
| |-- pytorch_model.bin.index.json
| |-- tokenizer_config.json
| |-- tokenizer.model
| |-- ...
```
Download a dataset from huggingface to local storage DATA_PATH, e.g. Dahoas/rm-static.
```bash
|-- $DATA_PATH
| |-- data
| | |-- train-00000-of-00001-2a1df75c6bce91ab.parquet
| | |-- test-00000-of-00001-8c7c51afc6d45980.parquet
| |-- dataset_infos.json
| |-- README.md
```
`finetune/yi_example_dataset` has example datasets, which are modified from [BAAI/COIG](https://huggingface.co/datasets/BAAI/COIG)
```bash
|-- $DATA_PATH
|--data
|-- train.jsonl
|-- eval.jsonl
```
`cd` into the scripts folder, copy and paste the script, and run. For example:
```bash
cd finetune/scripts
bash run_sft_Yi_6b.sh
```
For the Yi-6B base model, setting training_debug_steps=20 and num_train_epochs=4 can output a chat model, which takes about 20 minutes.
For the Yi-34B base model, it takes a relatively long time for initialization. Please be patient.
#### Evaluation
```bash
cd finetune/scripts
bash run_eval.sh
```
Then you'll see the answer from both the base model and the finetuned model.
</ul>
</details>
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</p>
### Quantization
#### GPT-Q
```bash
python quantization/gptq/quant_autogptq.py \
--model /base_model \
--output_dir /quantized_model \
--trust_remote_code
```
Once finished, you can then evaluate the resulting model as follows:
```bash
python quantization/gptq/eval_quantized_model.py \
--model /quantized_model \
--trust_remote_code
```
<details style="display: inline;"><summary>For details, see the explanations below. ⬇️</summary> <ul>
#### GPT-Q quantization
[GPT-Q](https://github.com/IST-DASLab/gptq) is a PTQ (Post-Training Quantization)
method. It saves memory and provides potential speedups while retaining the accuracy
of the model.
Yi models can be GPT-Q quantized without a lot of efforts.
We provide a step-by-step tutorial below.
To run GPT-Q, we will use [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) and
[exllama](https://github.com/turboderp/exllama).
And the huggingface transformers has integrated optimum and auto-gptq to perform
GPTQ quantization on language models.
##### Do Quantization
The `quant_autogptq.py` script is provided for you to perform GPT-Q quantization:
```bash
python quant_autogptq.py --model /base_model \
--output_dir /quantized_model --bits 4 --group_size 128 --trust_remote_code
```
##### Run Quantized Model
You can run a quantized model using the `eval_quantized_model.py`:
```bash
python eval_quantized_model.py --model /quantized_model --trust_remote_code
```
</ul>
</details>
#### AWQ
```bash
python quantization/awq/quant_autoawq.py \
--model /base_model \
--output_dir /quantized_model \
--trust_remote_code
```
Once finished, you can then evaluate the resulting model as follows:
```bash
python quantization/awq/eval_quantized_model.py \
--model /quantized_model \
--trust_remote_code
```
<details style="display: inline;"><summary>For details, see the explanations below. ⬇️</summary> <ul>
#### AWQ quantization
[AWQ](https://github.com/mit-han-lab/llm-awq) is a PTQ (Post-Training Quantization)
method. It's an efficient and accurate low-bit weight quantization (INT3/4) for LLMs.
Yi models can be AWQ quantized without a lot of efforts.
We provide a step-by-step tutorial below.
To run AWQ, we will use [AutoAWQ](https://github.com/casper-hansen/AutoAWQ).
##### Do Quantization
The `quant_autoawq.py` script is provided for you to perform AWQ quantization:
```bash
python quant_autoawq.py --model /base_model \
--output_dir /quantized_model --bits 4 --group_size 128 --trust_remote_code
```
##### Run Quantized Model
You can run a quantized model using the `eval_quantized_model.py`:
```bash
python eval_quantized_model.py --model /quantized_model --trust_remote_code
```
</ul>
</details>
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</p>
### Deployment
If you want to deploy Yi models, make sure you meet the software and hardware requirements.
#### Software requirements
Before using Yi quantized models, make sure you've installed the correct software listed below.
| Model | Software
|---|---
Yi 4-bit quantized models | [AWQ and CUDA](https://github.com/casper-hansen/AutoAWQ?tab=readme-ov-file#install-from-pypi)
Yi 8-bit quantized models | [GPTQ and CUDA](https://github.com/PanQiWei/AutoGPTQ?tab=readme-ov-file#quick-installation)
#### Hardware requirements
Before deploying Yi in your environment, make sure your hardware meets the following requirements.
##### Chat models
| Model | Minimum VRAM | Recommended GPU Example |
|:----------------------|:--------------|:-------------------------------------:|
| Yi-6B-Chat | 15 GB | 1 x RTX 3090 (24 GB) <br> 1 x RTX 4090 (24 GB) <br> 1 x A10 (24 GB) <br> 1 x A30 (24 GB) |
| Yi-6B-Chat-4bits | 4 GB | 1 x RTX 3060 (12 GB)<br> 1 x RTX 4060 (8 GB) |
| Yi-6B-Chat-8bits | 8 GB | 1 x RTX 3070 (8 GB) <br> 1 x RTX 4060 (8 GB) |
| Yi-34B-Chat | 72 GB | 4 x RTX 4090 (24 GB)<br> 1 x A800 (80GB) |
| Yi-34B-Chat-4bits | 20 GB | 1 x RTX 3090 (24 GB) <br> 1 x RTX 4090 (24 GB) <br> 1 x A10 (24 GB) <br> 1 x A30 (24 GB) <br> 1 x A100 (40 GB) |
| Yi-34B-Chat-8bits | 38 GB | 2 x RTX 3090 (24 GB) <br> 2 x RTX 4090 (24 GB)<br> 1 x A800 (40 GB) |
Below are detailed minimum VRAM requirements under different batch use cases.
| Model | batch=1 | batch=4 | batch=16 | batch=32 |
| ----------------------- | ------- | ------- | -------- | -------- |
| Yi-6B-Chat | 12 GB | 13 GB | 15 GB | 18 GB |
| Yi-6B-Chat-4bits | 4 GB | 5 GB | 7 GB | 10 GB |
| Yi-6B-Chat-8bits | 7 GB | 8 GB | 10 GB | 14 GB |
| Yi-34B-Chat | 65 GB | 68 GB | 76 GB | > 80 GB |
| Yi-34B-Chat-4bits | 19 GB | 20 GB | 30 GB | 40 GB |
| Yi-34B-Chat-8bits | 35 GB | 37 GB | 46 GB | 58 GB |
##### Base models
| Model | Minimum VRAM | Recommended GPU Example |
|----------------------|--------------|:-------------------------------------:|
| Yi-6B | 15 GB | 1 x RTX 3090 (24 GB) <br> 1 x RTX 4090 (24 GB) <br> 1 x A10 (24 GB) <br> 1 x A30 (24 GB) |
| Yi-6B-200K | 50 GB | 1 x A800 (80 GB) |
| Yi-9B | 20 GB | 1 x RTX 4090 (24 GB) |
| Yi-34B | 72 GB | 4 x RTX 4090 (24 GB) <br> 1 x A800 (80 GB) |
| Yi-34B-200K | 200 GB | 4 x A800 (80 GB) |
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</p>
### FAQ
<details>
<summary> If you have any questions while using the Yi series models, the answers provided below could serve as a helpful reference for you. ⬇️</summary>
<br>
#### 💡Fine-tuning
- <strong>Base model or Chat model - which to fine-tune?</strong>
<br>The choice of pre-trained language model for fine-tuning hinges on the computational resources you have at your disposal and the particular demands of your task.
- If you are working with a substantial volume of fine-tuning data (say, over 10,000 samples), the Base model could be your go-to choice.
- On the other hand, if your fine-tuning data is not quite as extensive, opting for the Chat model might be a more fitting choice.
- It is generally advisable to fine-tune both the Base and Chat models, compare their performance, and then pick the model that best aligns with your specific requirements.
- <strong>Yi-34B versus Yi-34B-Chat for full-scale fine-tuning - what is the difference?</strong>
<br>
The key distinction between full-scale fine-tuning on `Yi-34B`and `Yi-34B-Chat` comes down to the fine-tuning approach and outcomes.
- Yi-34B-Chat employs a Special Fine-Tuning (SFT) method, resulting in responses that mirror human conversation style more closely.
- The Base model's fine-tuning is more versatile, with a relatively high performance potential.
- If you are confident in the quality of your data, fine-tuning with `Yi-34B` could be your go-to.
- If you are aiming for model-generated responses that better mimic human conversational style, or if you have doubts about your data quality, `Yi-34B-Chat` might be your best bet.
#### 💡Quantization
- <strong>Quantized model versus original model - what is the performance gap?</strong>
- The performance variance is largely contingent on the quantization method employed and the specific use cases of these models. For instance, when it comes to models provided by the AWQ official, from a Benchmark standpoint, quantization might result in a minor performance drop of a few percentage points.
- Subjectively speaking, in situations like logical reasoning, even a 1% performance shift could impact the accuracy of the output results.
#### 💡General
- <strong>Where can I source fine-tuning question answering datasets?</strong>
- You can find fine-tuning question answering datasets on platforms like Hugging Face, with datasets like [m-a-p/COIG-CQIA](https://huggingface.co/datasets/m-a-p/COIG-CQIA) readily available.
- Additionally, Github offers fine-tuning frameworks, such as [hiyouga/LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory), which integrates pre-made datasets.
- <strong>What is the GPU memory requirement for fine-tuning Yi-34B FP16?</strong>
<br>
The GPU memory needed for fine-tuning 34B FP16 hinges on the specific fine-tuning method employed. For full parameter fine-tuning, you'll need 8 GPUs each with 80 GB; however, more economical solutions like Lora require less. For more details, check out [hiyouga/LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory). Also, consider using BF16 instead of FP16 for fine-tuning to optimize performance.
- <strong>Are there any third-party platforms that support chat functionality for the Yi-34b-200k model?</strong>
<br>
If you're looking for third-party Chats, options include [fireworks.ai](https://fireworks.ai/login?callbackURL=https://fireworks.ai/models/fireworks/yi-34b-chat).
</details>
### Learning hub
<details>
<summary> If you want to learn Yi, you can find a wealth of helpful educational resources here. ⬇️</summary>
<br>
Welcome to the Yi learning hub!
Whether you're a seasoned developer or a newcomer, you can find a wealth of helpful educational resources to enhance your understanding and skills with Yi models, including insightful blog posts, comprehensive video tutorials, hands-on guides, and more.
The content you find here has been generously contributed by knowledgeable Yi experts and passionate enthusiasts. We extend our heartfelt gratitude for your invaluable contributions!
At the same time, we also warmly invite you to join our collaborative effort by contributing to Yi. If you have already made contributions to Yi, please don't hesitate to showcase your remarkable work in the table below.
With all these resources at your fingertips, you're ready to start your exciting journey with Yi. Happy learning! 🥳
#### Tutorials
##### Blog tutorials
| Deliverable | Date | Author |
| ------------------------------------------------------------ | ---------- | ------------------------------------------------------------ |
| [使用 Dify、Meilisearch、零一万物模型实现最简单的 RAG 应用（三）：AI 电影推荐](https://mp.weixin.qq.com/s/Ri2ap9_5EMzdfiBhSSL_MQ) | 2024-05-20 | [苏洋](https://github.com/soulteary) |
| [使用autodl服务器，在A40显卡上运行， Yi-34B-Chat-int4模型，并使用vllm优化加速，显存占用42G，速度18 words-s](https://blog.csdn.net/freewebsys/article/details/134698597?ops_request_misc=%7B%22request%5Fid%22%3A%22171636168816800227489911%22%2C%22scm%22%3A%2220140713.130102334.pc%5Fblog.%22%7D&request_id=171636168816800227489911&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_ecpm_v1~times_rank-17-134698597-null-null.nonecase&utm_term=Yi大模型&spm=1018.2226.3001.4450) | 2024-05-20 | [fly-iot](https://gitee.com/fly-iot) |
| [Yi-VL 最佳实践](https://modelscope.cn/docs/yi-vl最佳实践) | 2024-05-20 | [ModelScope](https://github.com/modelscope) |
| [一键运行零一万物新鲜出炉Yi-1.5-9B-Chat大模型](https://mp.weixin.qq.com/s/ntMs2G_XdWeM3I6RUOBJrA) | 2024-05-13 | [Second State](https://github.com/second-state) |
| [零一万物开源Yi-1.5系列大模型](https://mp.weixin.qq.com/s/d-ogq4hcFbsuL348ExJxpA) | 2024-05-13 | [刘聪](https://github.com/liucongg) |
| [零一万物Yi-1.5系列模型发布并开源！ 34B-9B-6B 多尺寸，魔搭社区推理微调最佳实践教程来啦！](https://mp.weixin.qq.com/s/3wD-0dCgXB646r720o8JAg) | 2024-05-13 | [ModelScope](https://github.com/modelscope) |
| [Yi-34B 本地部署简单测试](https://blog.csdn.net/arkohut/article/details/135331469?ops_request_misc=%7B%22request%5Fid%22%3A%22171636390616800185813639%22%2C%22scm%22%3A%2220140713.130102334.pc%5Fblog.%22%7D&request_id=171636390616800185813639&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_ecpm_v1~times_rank-10-135331469-null-null.nonecase&utm_term=Yi大模型&spm=1018.2226.3001.4450) | 2024-05-13 | [漆妮妮](https://space.bilibili.com/1262370256) |
| [驾辰龙跨Llama持Wasm，玩转Yi模型迎新春过大年（上）](https://blog.csdn.net/weixin_53443275/article/details/136091398?ops_request_misc=%7B%22request%5Fid%22%3A%22171636390616800185813639%22%2C%22scm%22%3A%2220140713.130102334.pc%5Fblog.%22%7D&request_id=171636390616800185813639&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_ecpm_v1~times_rank-5-136091398-null-null.nonecase&utm_term=Yi大模型&spm=1018.2226.3001.4450) | 2024-05-13 | [Words worth](https://blog.csdn.net/weixin_53443275?type=blog) |
| [驾辰龙跨Llama持Wasm，玩转Yi模型迎新春过大年（下篇）](https://blog.csdn.net/weixin_53443275/article/details/136096309) | 2024-05-13 | [Words worth](https://blog.csdn.net/weixin_53443275?type=blog) |
| [Ollama新增两个命令，开始支持零一万物Yi-1.5系列模型](https://mp.weixin.qq.com/s/bBgzGJvUqIohodcy9U-pFw) | 2024-05-13 | AI工程师笔记 |
| [使用零一万物 200K 模型和 Dify 快速搭建模型应用](https://zhuanlan.zhihu.com/p/686774859) | 2024-05-13 | [苏洋](https://github.com/soulteary) |
| [(持更) 零一万物模型折腾笔记：社区 Yi-34B 微调模型使用](https://zhuanlan.zhihu.com/p/671549900) | 2024-05-13 | [苏洋](https://github.com/soulteary) |
| [Python+ERNIE-4.0-8K-Yi-34B-Chat大模型初探](https://mp.weixin.qq.com/s/WaygSfn5T8ZPB1mPdGADEQ) | 2024-05-11 | 江湖评谈 |
| [技术布道 Vue及Python调用零一万物模型和Prompt模板（通过百度千帆大模型平台）](https://blog.csdn.net/ucloud2012/article/details/137187469) | 2024-05-11 | [MumuLab](https://blog.csdn.net/ucloud2012?type=blog) |
| [多模态大模型Yi-VL-plus体验 效果很棒](https://zhuanlan.zhihu.com/p/694736111) | 2024-04-27 | [大家好我是爱因](https://www.zhihu.com/people/iamein) |
| [使用autodl服务器，两个3090显卡上运行， Yi-34B-Chat-int4模型，并使用vllm优化加速，显存占用42G，速度23 words-s](https://blog.csdn.net/freewebsys/article/details/134725765?ops_request_misc=%7B%22request%5Fid%22%3A%22171636356716800211598950%22%2C%22scm%22%3A%2220140713.130102334.pc%5Fblog.%22%7D&request_id=171636356716800211598950&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_ecpm_v1~times_rank-9-134725765-null-null.nonecase&utm_term=Yi大模型&spm=1018.2226.3001.4450) | 2024-04-27 | [fly-iot](https://gitee.com/fly-iot) |
| [Getting Started with Yi-1.5-9B-Chat](https://www.secondstate.io/articles/yi-1.5-9b-chat/) | 2024-04-27 | [Second State](https://github.com/second-state) |
| [基于零一万物yi-vl-plus大模型简单几步就能批量生成Anki图片笔记](https://mp.weixin.qq.com/s/_ea6g0pzzeO4WyYtuWycWQ) | 2024-04-24 | [正经人王同学](https://github.com/zjrwtx) |
| [【AI开发：语言】一、Yi-34B超大模型本地部署CPU和GPU版](https://blog.csdn.net/alarey/article/details/137769471?ops_request_misc=%7B%22request%5Fid%22%3A%22171636168816800227489911%22%2C%22scm%22%3A%2220140713.130102334.pc%5Fblog.%22%7D&request_id=171636168816800227489911&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_ecpm_v1~times_rank-16-137769471-null-null.nonecase&utm_term=Yi大模型&spm=1018.2226.3001.4450) | 2024-04-21 | [My的梦想已实现](https://blog.csdn.net/alarey?type=blog) |
| [【Yi-34B-Chat-Int4】使用4个2080Ti显卡11G版本，运行Yi-34B模型，5年前老显卡是支持的，可以正常运行，速度 21 words-s，vllm要求算力在7以上的显卡就可以](https://blog.csdn.net/freewebsys/article/details/134754086) | 2024-03-22 | [fly-iot](https://gitee.com/fly-iot) |
| [零一万物大模型部署+微调总结](https://blog.csdn.net/v_wus/article/details/135704126?ops_request_misc=%7B%22request%5Fid%22%3A%22171636168816800227489911%22%2C%22scm%22%3A%2220140713.130102334.pc%5Fblog.%22%7D&request_id=171636168816800227489911&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_ecpm_v1~times_rank-18-135704126-null-null.nonecase&utm_term=Yi大模型&spm=1018.2226.3001.4450) | 2024-03-22 | [v_wus](https://blog.csdn.net/v_wus?type=blog) |
| [零一万物Yi大模型vllm推理时Yi-34B或Yi-6bchat重复输出的解决方案](https://blog.csdn.net/qq_39667443/article/details/136028776?ops_request_misc=%7B%22request%5Fid%22%3A%22171636168816800227489911%22%2C%22scm%22%3A%2220140713.130102334.pc%5Fblog.%22%7D&request_id=171636168816800227489911&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_ecpm_v1~times_rank-6-136028776-null-null.nonecase&utm_term=Yi大模型&spm=1018.2226.3001.4450) | 2024-03-02 | [郝铠锋](https://blog.csdn.net/qq_39667443?type=blog) |
| [Yi-34B微调训练](https://blog.csdn.net/lsjlnd/article/details/135336984?ops_request_misc=%7B%22request%5Fid%22%3A%22171636343416800188513953%22%2C%22scm%22%3A%2220140713.130102334.pc%5Fblog.%22%7D&request_id=171636343416800188513953&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_ecpm_v1~times_rank-12-135336984-null-null.nonecase&utm_term=Yi大模型&spm=1018.2226.3001.4450) | 2024-03-02 | [lsjlnd](https://blog.csdn.net/lsjlnd?type=blog) |
| [实测零一万物Yi-VL多模态语言模型：能准确“识图吃瓜”](https://mp.weixin.qq.com/s/fu4O9XvJ03JhimsEyI-SsQ) | 2024-02-02 | [苏洋](https://github.com/soulteary) |
| [零一万物开源Yi-VL多模态大模型，魔搭社区推理&微调最佳实践来啦！](https://zhuanlan.zhihu.com/p/680098411) | 2024-01-26 | [ModelScope](https://github.com/modelscope) |
| [单卡 3 小时训练 Yi-6B 大模型 Agent：基于 Llama Factory 实战](https://zhuanlan.zhihu.com/p/678989191) | 2024-01-22 | [郑耀威](https://github.com/hiyouga) |
| [零一科技Yi-34B Chat大模型环境搭建&推理](https://blog.csdn.net/zzq1989_/article/details/135597181?ops_request_misc=%7B%22request%5Fid%22%3A%22171636168816800227489911%22%2C%22scm%22%3A%2220140713.130102334.pc%5Fblog.%22%7D&request_id=171636168816800227489911&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_ecpm_v1~times_rank-8-135597181-null-null.nonecase&utm_term=Yi大模型&spm=1018.2226.3001.4450) | 2024-01-15 | [要养家的程序员](https://blog.csdn.net/zzq1989_?type=blog) |
| [基于LLaMA Factory，单卡3小时训练专属大模型 Agent](https://blog.csdn.net/m0_59596990/article/details/135760285?ops_request_misc=%7B%22request%5Fid%22%3A%22171636343416800188513953%22%2C%22scm%22%3A%2220140713.130102334.pc%5Fblog.%22%7D&request_id=171636343416800188513953&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_ecpm_v1~times_rank-10-135760285-null-null.nonecase&utm_term=Yi大模型&spm=1018.2226.3001.4450) | 2024-01-15 | [机器学习社区](https://blog.csdn.net/m0_59596990?type=blog) |
| [双卡 3080ti 部署 Yi-34B 大模型 - Gradio + vLLM 踩坑全记录](https://blog.csdn.net/arkohut/article/details/135321242?ops_request_misc=%7B%22request%5Fid%22%3A%22171636168816800227489911%22%2C%22scm%22%3A%2220140713.130102334.pc%5Fblog.%22%7D&request_id=171636168816800227489911&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_ecpm_v1~times_rank-10-135321242-null-null.nonecase&utm_term=Yi大模型&spm=1018.2226.3001.4450) | 2024-01-02 | [漆妮妮](https://space.bilibili.com/1262370256) |
| [【大模型部署实践-3】3个能在3090上跑起来的4bits量化Chat模型（baichuan2-13b、InternLM-20b、Yi-34b）](https://blog.csdn.net/qq_40302568/article/details/135040985?ops_request_misc=%7B%22request%5Fid%22%3A%22171636168816800227489911%22%2C%22scm%22%3A%2220140713.130102334.pc%5Fblog.%22%7D&request_id=171636168816800227489911&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_ecpm_v1~times_rank-30-135040985-null-null.nonecase&utm_term=Yi大模型&spm=1018.2226.3001.4450) | 2024-01-02 | [aq_Seabiscuit](https://blog.csdn.net/qq_40302568?type=blog) |
| [只需 24G 显存，用 vllm 跑起来 Yi-34B 中英双语大模型](https://blog.csdn.net/arkohut/article/details/135274973) | 2023-12-28 | [漆妮妮](https://space.bilibili.com/1262370256) |
| [零一万物模型官方 Yi-34B 模型本地离线运行部署使用笔记（物理机和docker两种部署方式），200K 超长文本内容，34B 干翻一众 70B 模型，打榜分数那么高，这模型到底行不行？](https://blog.csdn.net/u014374009/article/details/136327696) | 2023-12-28 | [代码讲故事](https://blog.csdn.net/u014374009?type=blog) |
| [LLM - 大模型速递之 Yi-34B 入门与 LoRA 微调](https://blog.csdn.net/BIT_666/article/details/134990402) | 2023-12-18 | [BIT_666](https://bitddd.blog.csdn.net/?type=blog) |
| [通过vllm框架进行大模型推理](https://blog.csdn.net/weixin_45920955/article/details/135300561?ops_request_misc=%7B%22request%5Fid%22%3A%22171636343416800188513953%22%2C%22scm%22%3A%2220140713.130102334.pc%5Fblog.%22%7D&request_id=171636343416800188513953&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_ecpm_v1~times_rank-13-135300561-null-null.nonecase&utm_term=Yi大模型&spm=1018.2226.3001.4450) | 2023-12-18 | [土山炮](https://blog.csdn.net/weixin_45920955?type=blog) |
| [CPU 混合推理，非常见大模型量化方案：“二三五六” 位量化方案](https://zhuanlan.zhihu.com/p/671698216) | 2023-12-12 | [苏洋](https://github.com/soulteary) |
| [零一万物模型折腾笔记：官方 Yi-34B 模型基础使用](https://zhuanlan.zhihu.com/p/671387298) | 2023-12-10 | [苏洋](https://github.com/soulteary) |
| [Running Yi-34B-Chat locally using LlamaEdge](https://www.secondstate.io/articles/yi-34b/) | 2023-11-30 | [Second State](https://github.com/second-state) |
| [本地运行零一万物 34B 大模型，使用 Llama.cpp & 21G 显存](https://zhuanlan.zhihu.com/p/668921042) | 2023-11-26 | [苏洋](https://github.com/soulteary) |
##### GitHub Project
| Deliverable | Date | Author |
| ------------------------------------------------------------ | ---------- | ------------------------------------------- |
| [yi-openai-proxy](https://github.com/soulteary/yi-openai-proxy) | 2024-05-11 | [苏洋](https://github.com/soulteary) |
| [基于零一万物 Yi 模型和 B 站构建大语言模型高质量训练数据集](https://github.com/zjrwtx/bilibiliQA_databuilder) | 2024-04-29 | [正经人王同学](https://github.com/zjrwtx) |
| [基于视频网站和零一万物大模型构建大语言模型高质量训练数据集](https://github.com/zjrwtx/VideoQA_databuilder) | 2024-04-25 | [正经人王同学](https://github.com/zjrwtx) |
| [基于零一万物yi-34b-chat-200k输入任意文章地址，点击按钮即可生成无广告或推广内容的简要笔记，并生成分享图给好友](https://github.com/zjrwtx/open_summary) | 2024-04-24 | [正经人王同学](https://github.com/zjrwtx) |
| [Food-GPT-Yi-model](https://github.com/ThisisHubert/FoodGPT-Yi-model) | 2024-04-21 | [Hubert S](https://github.com/ThisisHubert) |
##### Video tutorials
| Deliverable | Date | Author |
| ------------------------------------------------------------ | ---------- | ------------------------------------------------------------ |
| [Run dolphin-2.2-yi-34b on IoT Devices](https://www.youtube.com/watch?v=NJ89T5mO25Y) | 2023-11-30 | [Second State](https://github.com/second-state) |
| [只需 24G 显存，用 vllm 跑起来 Yi-34B 中英双语大模型](https://www.bilibili.com/video/BV17t4y1f7Ee/) | 2023-12-28 | [漆妮妮](https://space.bilibili.com/1262370256) |
| [Install Yi 34B Locally - Chinese English Bilingual LLM](https://www.youtube.com/watch?v=CVQvj4Wrh4w&t=476s) | 2023-11-05 | [Fahd Mirza](https://www.youtube.com/@fahdmirza) |
| [Dolphin Yi 34b - Brand New Foundational Model TESTED](https://www.youtube.com/watch?v=On3Zuv27V3k&t=85s) | 2023-11-27 | [Matthew Berman](https://www.youtube.com/@matthew_berman) |
| [Yi-VL-34B 多模态大模型 - 用两张 A40 显卡跑起来](https://www.bilibili.com/video/BV1Q5411y7AG/) | 2024-01-28 | [漆妮妮](https://space.bilibili.com/1262370256) |
| [4060Ti 16G显卡安装零一万物最新开源的Yi-1.5版大语言模型](https://www.bilibili.com/video/BV16i421X7Jx/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-05-14 | [titan909](https://space.bilibili.com/526393761) |
| [Yi-1.5: True Apache 2.0 Competitor to LLAMA-3](https://www.youtube.com/watch?v=KCDYrfWeTRc) | 2024-05-13 | [Prompt Engineering](https://www.youtube.com/@engineerprompt) |
| [Install Yi-1.5 Model Locally - Beats Llama 3 in Various Benchmarks](https://www.youtube.com/watch?v=Ba-G7Il0UkA) | 2024-05-13 | [Fahd Mirza](https://www.youtube.com/@fahdmirza) |
| [how to install Ollama and run Yi 6B](https://www.youtube.com/watch?v=4Jnar7OUHqQ) | 2024-05-13 | [Ridaa Davids](https://www.youtube.com/@quantanovabusiness) |
| [地表最强混合智能AI助手：llama3_70B+Yi_34B+Qwen1.5_110B](https://www.bilibili.com/video/BV1Xm411C7V1/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-05-04 | [朱扎特](https://space.bilibili.com/494512200?spm_id_from=333.788.0.0) |
| [ChatDoc学术论文辅助--基于Yi-34B和langchain进行PDF知识库问答](https://www.bilibili.com/video/BV11i421C7B5/?spm_id_from=333.999.0.0&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-05-03 | [朱扎特](https://space.bilibili.com/494512200?spm_id_from=333.788.0.0) |
| [基于Yi-34B的领域知识问答项目演示](https://www.bilibili.com/video/BV1zZ42177ZA/?spm_id_from=333.999.0.0&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-05-02 | [朱扎特](https://space.bilibili.com/494512200?spm_id_from=333.788.0.0) |
| [使用RTX4090+GaLore算法 全参微调Yi-6B大模型](https://www.bilibili.com/video/BV1ax4y1U7Ep/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-03-24 | [小工蚂创始人](https://space.bilibili.com/478674499?spm_id_from=333.788.0.0) |
| [无内容审查NSFW大语言模型Yi-34B-Chat蒸馏版测试,RolePlay,《天龙八部》马夫人康敏,本地GPU,CPU运行](https://www.youtube.com/watch?v=VL-W0TnLCns) | 2024-03-20 | [刘悦的技术博客](https://v3u.cn/) |
| [无内容审查NSFW大语言模型整合包,Yi-34B-Chat,本地CPU运行,角色扮演潘金莲](https://www.youtube.com/watch?v=rBvbgwz3oHM) | 2024-03-16 | [刘悦的技术博客](https://v3u.cn/) |
| [量化 Yi-34B-Chat 并在单卡 RTX 4090 使用 vLLM 部署](https://www.bilibili.com/video/BV1jx421y7xj/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-03-05 | [白鸽巢](https://space.bilibili.com/138938660?spm_id_from=333.788.0.0) |
| [Yi-VL-34B（5）：使用3个3090显卡24G版本，运行Yi-VL-34B模型，支持命令行和web界面方式，理解图片的内容转换成文字](https://www.bilibili.com/video/BV1BB421z7oA/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-02-27 | [fly-iot](https://gitee.com/fly-iot) |
| [Win环境KoboldCpp本地部署大语言模型进行各种角色扮演游戏](https://www.bilibili.com/video/BV14J4m1e77f/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-02-25 | [魚蟲蟲](https://space.bilibili.com/431981179?spm_id_from=333.788.0.0) |
| [无需显卡本地部署Yi-34B-Chat进行角色扮演游戏 P2](https://www.bilibili.com/video/BV19v421677y/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-02-23 | [魚蟲蟲](https://space.bilibili.com/431981179?spm_id_from=333.788.0.0) |
| [【wails】（2）：使用go-llama.cpp 运行 yi-01-6b大模型，使用本地CPU运行，速度还可以，等待下一版本更新](https://www.bilibili.com/video/BV194421F7Fy/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-02-20 | [fly-iot](https://gitee.com/fly-iot) |
| [【xinference】（6）：在autodl上，使用xinference部署yi-vl-chat和qwen-vl-chat模型，可以使用openai调用成功](https://www.bilibili.com/video/BV19Z421z7cv/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-02-06 | [fly-iot](https://gitee.com/fly-iot) |
| [无需显卡本地部署Yi-34B-Chat进行角色扮演游戏 P1](https://www.bilibili.com/video/BV1tU421o7Co/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-02-05 | [魚蟲蟲](https://space.bilibili.com/431981179?spm_id_from=333.788.0.0) |
| [2080Ti部署YI-34B大模型 xinference-oneapi-fastGPT本地知识库使用指南](https://www.bilibili.com/video/BV1hC411z7xu/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-01-30 | [小饭护法要转码](https://space.bilibili.com/39486865?spm_id_from=333.788.0.0) |
| [Best Story Writing AI Model - Install Yi 6B 200K Locally on Windows](https://www.youtube.com/watch?v=cZs2jRtl0bs) | 2024-01-22 | [Fahd Mirza](https://www.youtube.com/@fahdmirza) |
| [Mac 本地运行大语言模型方法与常见问题指南（Yi 34B 模型+32 GB 内存测试）](https://www.bilibili.com/video/BV1VT4y1b7Th/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-01-21 | [小吴苹果机器人](https://space.bilibili.com/1732749682?spm_id_from=333.788.0.0) |
| [【Dify知识库】（11）：Dify0.4.9改造支持MySQL，成功接入yi-6b 做对话，本地使用fastchat启动，占8G显存，完成知识库配置](https://www.bilibili.com/video/BV1ia4y1y7JH/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-01-21 | [fly-iot](https://gitee.com/fly-iot) |
| [这位LLM先生有点暴躁,用的是YI-6B的某个量化版,#LLM #大语言模型 #暴躁老哥](https://www.youtube.com/watch?v=eahXJrdtQuc) | 2024-01-20 | [晓漫吧](https://www.youtube.com/@xiaomanba) |
| [大模型推理 NvLink 桥接器有用吗｜双卡 A6000 测试一下](https://www.bilibili.com/video/BV1AW4y1w7DC/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-01-17 | [漆妮妮](https://space.bilibili.com/1262370256) |
| [大模型推理 A40 vs A6000 谁更强 - 对比 Yi-34B 的单、双卡推理性能](https://www.bilibili.com/video/BV1aK4y1z7GF/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-01-15 | [漆妮妮](https://space.bilibili.com/1262370256) |
| [C-Eval 大语言模型评测基准- 用 LM Evaluation Harness + vLLM 跑起来](https://www.bilibili.com/video/BV1Yw411g7ZL/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-01-11 | [漆妮妮](https://space.bilibili.com/1262370256) |
| [双显卡部署 Yi-34B 大模型 - vLLM + Gradio 踩坑记录](https://www.bilibili.com/video/BV1p94y1c7ak/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-01-01 | [漆妮妮](https://space.bilibili.com/1262370256) |
| [手把手教学！使用 vLLM 快速部署 Yi-34B-Chat](https://www.bilibili.com/video/BV1ew41157Mk/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2023-12-26 | [白鸽巢](https://space.bilibili.com/138938660?spm_id_from=333.788.0.0) |
| [如何训练企业自己的大语言模型？Yi-6B LORA微调演示 #小工蚁](https://www.bilibili.com/video/BV1uc41117zz/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2023-12-21 | [小工蚂创始人](https://space.bilibili.com/478674499?spm_id_from=333.788.0.0) |
| [Yi-34B（4）：使用4个2080Ti显卡11G版本，运行Yi-34B模型，5年前老显卡是支持的，可以正常运行，速度 21 words/s](https://www.bilibili.com/video/BV1nj41157L3/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2023-12-02 | [fly-iot](https://gitee.com/fly-iot) |
| [使用autodl服务器，RTX 3090 * 3 显卡上运行， Yi-34B-Chat模型，显存占用60G](https://www.bilibili.com/video/BV1BM411R7ae/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2023-12-01 | [fly-iot](https://gitee.com/fly-iot) |
| [使用autodl服务器，两个3090显卡上运行， Yi-34B-Chat-int4模型，用vllm优化，增加 --num-gpu 2，速度23 words/s](https://www.bilibili.com/video/BV1Hu4y1L7BH/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2023-12-01 | [fly-iot](https://gitee.com/fly-iot) |
| [Yi大模型一键本地部署 技术小白玩转AI](https://www.bilibili.com/video/BV16H4y117md/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2023-12-01 | [技术小白玩转AI](https://space.bilibili.com/3546586137234288?spm_id_from=333.788.0.0) |
| [01.AI's Yi-6B: Overview and Fine-Tuning](https://www.youtube.com/watch?v=mye-UOkAliQ) | 2023-11-28 | [AI Makerspace](https://www.youtube.com/@AI-Makerspace) |
| [Yi 34B Chat LLM outperforms Llama 70B](https://www.youtube.com/watch?v=RYtrF-R5jDc) | 2023-11-27 | [DLExplorer](https://www.youtube.com/@DLExplorers-lg7dt) |
| [How to run open source models on mac Yi 34b on m3 Max](https://www.youtube.com/watch?v=GAo-dopkgjI) | 2023-11-26 | [TECHNO PREMIUM](https://www.youtube.com/@technopremium91) |
| [Yi-34B - 200K - The BEST & NEW CONTEXT WINDOW KING ](https://www.youtube.com/watch?v=7WBojwwv5Qo) | 2023-11-24 | [Prompt Engineering](https://www.youtube.com/@engineerprompt) |
| [Yi 34B : The Rise of Powerful Mid-Sized Models - Base,200k & Chat](https://www.youtube.com/watch?v=bWCjwtu_tHs) | 2023-11-24 | [Sam Witteveen](https://www.youtube.com/@samwitteveenai) |
| [在IoT设备运行破解版李开复大模型dolphin-2.2-yi-34b（还可作为私有OpenAI API服务器）](https://www.bilibili.com/video/BV1SQ4y18744/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2023-11-15 | [Second State](https://github.com/second-state) |
| [Run dolphin-2.2-yi-34b on IoT Devices (Also works as a Private OpenAI API Server)](https://www.youtube.com/watch?v=NJ89T5mO25Y) | 2023-11-14 | [Second State](https://github.com/second-state) |
| [How to Install Yi 34B 200K Llamafied on Windows Laptop](https://www.youtube.com/watch?v=enoha4K4HkQ) | 2023-11-11 | [Fahd Mirza](https://www.youtube.com/@fahdmirza) |
</details>
# Why Yi?
- [Ecosystem](#ecosystem)
- [Upstream](#upstream)
- [Downstream](#downstream)
- [Serving](#serving)
- [Quantization](#quantization-1)
- [Fine-tuning](#fine-tuning-1)
- [API](#api)
- [Benchmarks](#benchmarks)
- [Chat model performance](#chat-model-performance)
- [Base model performance](#base-model-performance)
- [Yi-34B and Yi-34B-200K](#yi-34b-and-yi-34b-200k)
- [Yi-9B](#yi-9b)
## Ecosystem
Yi has a comprehensive ecosystem, offering a range of tools, services, and models to enrich your experiences and maximize productivity.
- [Upstream](#upstream)
- [Downstream](#downstream)
- [Serving](#serving)
- [Quantization](#quantization-1)
- [Fine-tuning](#fine-tuning-1)
- [API](#api)
### Upstream
The Yi series models follow the same model architecture as Llama. By choosing Yi, you can leverage existing tools, libraries, and resources within the Llama ecosystem, eliminating the need to create new tools and enhancing development efficiency.
For example, the Yi series models are saved in the format of the Llama model. You can directly use `LlamaForCausalLM` and `LlamaTokenizer` to load the model. For more information, see [Use the chat model](#31-use-the-chat-model).
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("01-ai/Yi-34b", use_fast=False)
model = AutoModelForCausalLM.from_pretrained("01-ai/Yi-34b", device_map="auto")
```
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### Downstream
> 💡 Tip
>
> - Feel free to create a PR and share the fantastic work you've built using the Yi series models.
>
> - To help others quickly understand your work, it is recommended to use the format of `<model-name>: <model-intro> + <model-highlights>`.
#### Serving
If you want to get up with Yi in a few minutes, you can use the following services built upon Yi.
- Yi-34B-Chat: you can chat with Yi using one of the following platforms:
- [Yi-34B-Chat | Hugging Face](https://huggingface.co/spaces/01-ai/Yi-34B-Chat)
- [Yi-34B-Chat | Yi Platform](https://platform.lingyiwanwu.com/): **Note** that currently it's available through a whitelist. Welcome to apply (fill out a form in [English](https://cn.mikecrm.com/l91ODJf) or [Chinese](https://cn.mikecrm.com/gnEZjiQ)) and experience it firsthand!
- [Yi-6B-Chat (Replicate)](https://replicate.com/01-ai): you can use this model with more options by setting additional parameters and calling APIs.
- [ScaleLLM](https://github.com/vectorch-ai/ScaleLLM#supported-models): you can use this service to run Yi models locally with added flexibility and customization.
#### Quantization
If you have limited computational capabilities, you can use Yi's quantized models as follows.
These quantized models have reduced precision but offer increased efficiency, such as faster inference speed and smaller RAM usage.
- [TheBloke/Yi-34B-GPTQ](https://huggingface.co/TheBloke/Yi-34B-GPTQ)
- [TheBloke/Yi-34B-GGUF](https://huggingface.co/TheBloke/Yi-34B-GGUF)
- [TheBloke/Yi-34B-AWQ](https://huggingface.co/TheBloke/Yi-34B-AWQ)
#### Fine-tuning
If you're seeking to explore the diverse capabilities within Yi's thriving family, you can delve into Yi's fine-tuned models as below.
- [TheBloke Models](https://huggingface.co/TheBloke): this site hosts numerous fine-tuned models derived from various LLMs including Yi.
This is not an exhaustive list for Yi, but to name a few sorted on downloads:
- [TheBloke/dolphin-2_2-yi-34b-AWQ](https://huggingface.co/TheBloke/dolphin-2_2-yi-34b-AWQ)
- [TheBloke/Yi-34B-Chat-AWQ](https://huggingface.co/TheBloke/Yi-34B-Chat-AWQ)
- [TheBloke/Yi-34B-Chat-GPTQ](https://huggingface.co/TheBloke/Yi-34B-Chat-GPTQ)
- [SUSTech/SUS-Chat-34B](https://huggingface.co/SUSTech/SUS-Chat-34B): this model ranked first among all models below 70B and outperformed the twice larger deepseek-llm-67b-chat. You can check the result on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
- [OrionStarAI/OrionStar-Yi-34B-Chat-Llama](https://huggingface.co/OrionStarAI/OrionStar-Yi-34B-Chat-Llama): this model excelled beyond other models (such as GPT-4, Qwen-14B-Chat, Baichuan2-13B-Chat) in C-Eval and CMMLU evaluations on the [OpenCompass LLM Leaderboard](https://opencompass.org.cn/leaderboard-llm).
- [NousResearch/Nous-Capybara-34B](https://huggingface.co/NousResearch/Nous-Capybara-34B): this model is trained with 200K context length and 3 epochs on the Capybara dataset.
#### API
- [amazing-openai-api](https://github.com/soulteary/amazing-openai-api): this tool converts Yi model APIs into the OpenAI API format out of the box.
- [LlamaEdge](https://www.secondstate.io/articles/yi-34b/#create-an-openai-compatible-api-service-for-the-yi-34b-chat-model): this tool builds an OpenAI-compatible API server for Yi-34B-Chat using a portable Wasm (WebAssembly) file, powered by Rust.
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## Tech report
For detailed capabilities of the Yi series model, see [Yi: Open Foundation Models by 01.AI](https://arxiv.org/abs/2403.04652).
### Citation
```
@misc{ai2024yi,
title={Yi: Open Foundation Models by 01.AI},
author={01. AI and : and Alex Young and Bei Chen and Chao Li and Chengen Huang and Ge Zhang and Guanwei Zhang and Heng Li and Jiangcheng Zhu and Jianqun Chen and Jing Chang and Kaidong Yu and Peng Liu and Qiang Liu and Shawn Yue and Senbin Yang and Shiming Yang and Tao Yu and Wen Xie and Wenhao Huang and Xiaohui Hu and Xiaoyi Ren and Xinyao Niu and Pengcheng Nie and Yuchi Xu and Yudong Liu and Yue Wang and Yuxuan Cai and Zhenyu Gu and Zhiyuan Liu and Zonghong Dai},
year={2024},
eprint={2403.04652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## Benchmarks
- [Chat model performance](#chat-model-performance)
- [Base model performance](#base-model-performance)
### Chat model performance
Yi-34B-Chat model demonstrates exceptional performance, ranking first among all existing open-source models in the benchmarks including MMLU, CMMLU, BBH, GSM8k, and more.
<details>
<summary> Evaluation methods and challenges. ⬇️ </summary>
- **Evaluation methods**: we evaluated various benchmarks using both zero-shot and few-shot methods, except for TruthfulQA.
- **Zero-shot vs. few-shot**: in chat models, the zero-shot approach is more commonly employed.
- **Evaluation strategy**: our evaluation strategy involves generating responses while following instructions explicitly or implicitly (such as using few-shot examples). We then isolate relevant answers from the generated text.
- **Challenges faced**: some models are not well-suited to produce output in the specific format required by instructions in few datasets, which leads to suboptimal results.
<strong>*</strong>: C-Eval results are evaluated on the validation datasets
</details>
### Base model performance
#### Yi-34B and Yi-34B-200K
The Yi-34B and Yi-34B-200K models stand out as the top performers among open-source models, especially excelling in MMLU, CMMLU, common-sense reasoning, reading comprehension, and more.
<details>
<summary> Evaluation methods. ⬇️</summary>
- **Disparity in results**: while benchmarking open-source models, a disparity has been noted between results from our pipeline and those reported by public sources like OpenCompass.
- **Investigation findings**: a deeper investigation reveals that variations in prompts, post-processing strategies, and sampling techniques across models may lead to significant outcome differences.
- **Uniform benchmarking process**: our methodology aligns with the original benchmarks—consistent prompts and post-processing strategies are used, and greedy decoding is applied during evaluations without any post-processing for the generated content.
- **Efforts to retrieve unreported scores**: for scores that were not reported by the original authors (including scores reported with different settings), we try to get results with our pipeline.
- **Extensive model evaluation**: to evaluate the model’s capability extensively, we adopted the methodology outlined in Llama2. Specifically, we included PIQA, SIQA, HellaSwag, WinoGrande, ARC, OBQA, and CSQA to assess common sense reasoning. SquAD, QuAC, and BoolQ were incorporated to evaluate reading comprehension.
- **Special configurations**: CSQA was exclusively tested using a 7-shot setup, while all other tests were conducted with a 0-shot configuration. Additionally, we introduced GSM8K (8-shot@1), MATH (4-shot@1), HumanEval (0-shot@1), and MBPP (3-shot@1) under the category "Math & Code".
- **Falcon-180B caveat**: Falcon-180B was not tested on QuAC and OBQA due to technical constraints. Its performance score is an average from other tasks, and considering the generally lower scores of these two tasks, Falcon-180B's capabilities are likely not underestimated.
</details>
#### Yi-9B
Yi-9B is almost the best among a range of similar-sized open-source models (including Mistral-7B, SOLAR-10.7B, Gemma-7B, DeepSeek-Coder-7B-Base-v1.5 and more), particularly excelling in code, math, common-sense reasoning, and reading comprehension.
- In terms of **overall** ability (Mean-All), Yi-9B performs the best among similarly sized open-source models, surpassing DeepSeek-Coder, DeepSeek-Math, Mistral-7B, SOLAR-10.7B, and Gemma-7B.
- In terms of **coding** ability (Mean-Code), Yi-9B's performance is second only to DeepSeek-Coder-7B, surpassing Yi-34B, SOLAR-10.7B, Mistral-7B, and Gemma-7B.
- In terms of **math** ability (Mean-Math), Yi-9B's performance is second only to DeepSeek-Math-7B, surpassing SOLAR-10.7B, Mistral-7B, and Gemma-7B.
- In terms of **common sense and reasoning** ability (Mean-Text), Yi-9B's performance is on par with Mistral-7B, SOLAR-10.7B, and Gemma-7B.
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# Who can use Yi?
Everyone! 🙌 ✅
The code and weights of the Yi series models are distributed under the [Apache 2.0 license](https://github.com/01-ai/Yi/blob/main/LICENSE), which means the Yi series models are free for personal usage, academic purposes, and commercial use.
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# Misc.
### Acknowledgments
A heartfelt thank you to each of you who have made contributions to the Yi community! You have helped Yi not just a project, but a vibrant, growing home for innovation.
[](https://github.com/01-ai/yi/graphs/contributors)
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### Disclaimer
We use data compliance checking algorithms during the training process, to
ensure the compliance of the trained model to the best of our ability. Due to
complex data and the diversity of language model usage scenarios, we cannot
guarantee that the model will generate correct, and reasonable output in all
scenarios. Please be aware that there is still a risk of the model producing
problematic outputs. We will not be responsible for any risks and issues
resulting from misuse, misguidance, illegal usage, and related misinformation,
as well as any associated data security concerns.
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### License
The code and weights of the Yi-1.5 series models are distributed under the [Apache 2.0 license](https://github.com/01-ai/Yi/blob/main/LICENSE).
If you create derivative works based on this model, please include the following attribution in your derivative works:
This work is a derivative of [The Yi Series Model You Base On] by 01.AI, used under the Apache 2.0 License.
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|
mdarefin/learn_hf_food_not_food_text_classifier-distilbert-base-uncased | mdarefin | 2024-10-16T04:11:37Z | 106 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-14T04:50:46Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: learn_hf_food_not_food_text_classifier-distilbert-base-uncased
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# learn_hf_food_not_food_text_classifier-distilbert-base-uncased
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0005
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3977 | 1.0 | 7 | 0.0869 | 1.0 |
| 0.0418 | 2.0 | 14 | 0.0067 | 1.0 |
| 0.0048 | 3.0 | 21 | 0.0020 | 1.0 |
| 0.0017 | 4.0 | 28 | 0.0011 | 1.0 |
| 0.001 | 5.0 | 35 | 0.0008 | 1.0 |
| 0.0008 | 6.0 | 42 | 0.0006 | 1.0 |
| 0.0007 | 7.0 | 49 | 0.0006 | 1.0 |
| 0.0006 | 8.0 | 56 | 0.0005 | 1.0 |
| 0.0006 | 9.0 | 63 | 0.0005 | 1.0 |
| 0.0006 | 10.0 | 70 | 0.0005 | 1.0 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.4.1
- Datasets 3.0.1
- Tokenizers 0.20.1
|
WhoingYang/model_output | WhoingYang | 2024-10-16T03:56:19Z | 106 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"HHD",
"10_class",
"multi_labels",
"generated_from_trainer",
"base_model:beomi/kcbert-base",
"base_model:finetune:beomi/kcbert-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-16T03:55:59Z | ---
library_name: transformers
license: apache-2.0
base_model: beomi/kcbert-base
tags:
- HHD
- 10_class
- multi_labels
- generated_from_trainer
model-index:
- name: model_output
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# model_output
This model is a fine-tuned version of [beomi/kcbert-base](https://huggingface.co/beomi/kcbert-base) on the unsmile_data dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1654
- Lrap: 0.8747
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Lrap |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 235 | 0.1476 | 0.8689 |
| No log | 2.0 | 470 | 0.1430 | 0.8768 |
| 0.0421 | 3.0 | 705 | 0.1532 | 0.8777 |
| 0.0421 | 4.0 | 940 | 0.1622 | 0.8746 |
| 0.0235 | 5.0 | 1175 | 0.1654 | 0.8747 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
|
zstanjj/SlimPLM-Retrieval-Necessity-Judgment | zstanjj | 2024-10-16T03:55:38Z | 38 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:2402.12052",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-25T03:32:18Z | ---
license: llama2
---
<!-- markdownlint-disable first-line-h1 -->
<!-- markdownlint-disable html -->
<div align="center">
<h1>
SlimPLM
</h1>
</div>
<p align="center">
📝 <a href="https://arxiv.org/abs/2402.12052" target="_blank">Paper</a> • 🤗 <a href="https://huggingface.co/zstanjj/SlimPLM-Retrieval-Necessity-Judgment/" target="_blank">Hugging Face</a> • 🧩 <a href="https://github.com/plageon/SlimPLM" target="_blank">Github</a>
</p>
<div align="center">
</div>
🌹 If you use this model, please star our **[GitHub repository](https://github.com/plageon/SlimPlm)** to support us. Your star means a lot!
## ✨ Latest News
- [1/25/2024]: Retrieval Necessity Judgment Model released in [Hugging Face](https://huggingface.co/zstanjj/SlimPLM-Retrieval-Necessity-Judgment/).
- [2/20/2024]: Query Rewriting Model released in [Hugging Face](https://huggingface.co/zstanjj/SlimPLM-Query-Rewriting/).
- [5/19/2024]: Our new work, **[Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs](https://aclanthology.org/2024.acl-long.242/)**, has been accepted by **ACL 2024 main** conference.
## 🎬 Get Started
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# construct prompt
question = "Who voices Darth Vader in Star Wars Episodes III-VI, IX Rogue One, and Rebels?"
heuristic_answer = "The voice of Darth Vader in Star Wars is provided by British actor James Earl Jones. He first voiced the character in the 1977 film \"Star Wars: Episode IV - A New Hope\", and his performance has been used in all subsequent Star Wars films, including the prequels and sequels."
prompt = (f"<s>[INST] <<SYS>>\nYou are a helpful assistant. Your task is to parse user input into"
f" structured formats according to the coarse answer. Current datatime is 2023-12-20 9:47:28"
f" <</SYS>>\n Course answer: (({heuristic_answer}))\nQuestion: (({question})) [/INST]")
params_query_rewrite = {"repetition_penalty": 1.05, "temperature": 0.01, "top_k": 1, "top_p": 0.85,
"max_new_tokens": 512, "do_sample": False, "seed": 2023}
# deploy model
model = AutoModelForCausalLM.from_pretrained("zstanjj/SlimPLM-Retrieval-Necessity-Judgment").eval()
if torch.cuda.is_available():
model.cuda()
tokenizer = AutoTokenizer.from_pretrained("zstanjj/SlimPLM-Retrieval-Necessity-Judgment")
# run inference
input_ids = tokenizer.encode(prompt.format(question=question, answer=heuristic_answer), return_tensors="pt")
len_input_ids = len(input_ids[0])
if torch.cuda.is_available():
input_ids = input_ids.cuda()
outputs = model.generate(input_ids)
res = tokenizer.decode(outputs[0][len_input_ids:], skip_special_tokens=True)
print(res)
```
## ✏️ Citation
```
@inproceedings{Tan2024SmallMB,
title={Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs},
author={Jiejun Tan and Zhicheng Dou and Yutao Zhu and Peidong Guo and Kun Fang and Ji-Rong Wen},
year={2024},
url={https://arxiv.org/abs/2402.12052}
}
``` |
Gnider/roberta_dist_rat | Gnider | 2024-10-16T03:44:18Z | 120 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-16T03:43:34Z | ---
library_name: transformers
tags: []
---
# 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] |
coin-cidence/model_output | coin-cidence | 2024-10-16T03:40:39Z | 106 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"HHD",
"10_class",
"multi_labels",
"generated_from_trainer",
"base_model:beomi/kcbert-base",
"base_model:finetune:beomi/kcbert-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-16T03:40:21Z | ---
library_name: transformers
license: apache-2.0
base_model: beomi/kcbert-base
tags:
- HHD
- 10_class
- multi_labels
- generated_from_trainer
model-index:
- name: model_output
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# model_output
This model is a fine-tuned version of [beomi/kcbert-base](https://huggingface.co/beomi/kcbert-base) on the unsmile_data dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1311
- Lrap: 0.8779
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Lrap |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 235 | 0.1467 | 0.8596 |
| No log | 2.0 | 470 | 0.1291 | 0.8725 |
| 0.17 | 3.0 | 705 | 0.1251 | 0.8776 |
| 0.17 | 4.0 | 940 | 0.1298 | 0.8794 |
| 0.0776 | 5.0 | 1175 | 0.1311 | 0.8779 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
|
mradermacher/LexiMaid-L3-8B-i1-GGUF | mradermacher | 2024-10-16T03:40:38Z | 140 | 3 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:Arkana08/LexiMaid-L3-8B",
"base_model:quantized:Arkana08/LexiMaid-L3-8B",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-10-13T17:41:48Z | ---
base_model: Arkana08/LexiMaid-L3-8B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Arkana08/LexiMaid-L3-8B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/LexiMaid-L3-8B-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/LexiMaid-L3-8B-i1-GGUF/resolve/main/LexiMaid-L3-8B.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/LexiMaid-L3-8B-i1-GGUF/resolve/main/LexiMaid-L3-8B.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/LexiMaid-L3-8B-i1-GGUF/resolve/main/LexiMaid-L3-8B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/LexiMaid-L3-8B-i1-GGUF/resolve/main/LexiMaid-L3-8B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/LexiMaid-L3-8B-i1-GGUF/resolve/main/LexiMaid-L3-8B.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/LexiMaid-L3-8B-i1-GGUF/resolve/main/LexiMaid-L3-8B.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/LexiMaid-L3-8B-i1-GGUF/resolve/main/LexiMaid-L3-8B.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/LexiMaid-L3-8B-i1-GGUF/resolve/main/LexiMaid-L3-8B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/LexiMaid-L3-8B-i1-GGUF/resolve/main/LexiMaid-L3-8B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/LexiMaid-L3-8B-i1-GGUF/resolve/main/LexiMaid-L3-8B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/LexiMaid-L3-8B-i1-GGUF/resolve/main/LexiMaid-L3-8B.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/LexiMaid-L3-8B-i1-GGUF/resolve/main/LexiMaid-L3-8B.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/LexiMaid-L3-8B-i1-GGUF/resolve/main/LexiMaid-L3-8B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/LexiMaid-L3-8B-i1-GGUF/resolve/main/LexiMaid-L3-8B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/LexiMaid-L3-8B-i1-GGUF/resolve/main/LexiMaid-L3-8B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/LexiMaid-L3-8B-i1-GGUF/resolve/main/LexiMaid-L3-8B.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 4.8 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/LexiMaid-L3-8B-i1-GGUF/resolve/main/LexiMaid-L3-8B.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 4.8 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/LexiMaid-L3-8B-i1-GGUF/resolve/main/LexiMaid-L3-8B.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 4.8 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/LexiMaid-L3-8B-i1-GGUF/resolve/main/LexiMaid-L3-8B.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/LexiMaid-L3-8B-i1-GGUF/resolve/main/LexiMaid-L3-8B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/LexiMaid-L3-8B-i1-GGUF/resolve/main/LexiMaid-L3-8B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/LexiMaid-L3-8B-i1-GGUF/resolve/main/LexiMaid-L3-8B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/LexiMaid-L3-8B-i1-GGUF/resolve/main/LexiMaid-L3-8B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/LexiMaid-L3-8B-i1-GGUF/resolve/main/LexiMaid-L3-8B.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | 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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
LookingforEasyMoney/model_output | LookingforEasyMoney | 2024-10-16T03:39:37Z | 106 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"HHD",
"10_class",
"multi_labels",
"generated_from_trainer",
"base_model:beomi/kcbert-base",
"base_model:finetune:beomi/kcbert-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-16T03:39:04Z | ---
library_name: transformers
license: apache-2.0
base_model: beomi/kcbert-base
tags:
- HHD
- 10_class
- multi_labels
- generated_from_trainer
model-index:
- name: model_output
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# model_output
This model is a fine-tuned version of [beomi/kcbert-base](https://huggingface.co/beomi/kcbert-base) on the unsmile_data dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1285
- Lrap: 0.8805
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Lrap |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 235 | 0.1511 | 0.8522 |
| No log | 2.0 | 470 | 0.1293 | 0.8750 |
| 0.1745 | 3.0 | 705 | 0.1228 | 0.8803 |
| 0.1745 | 4.0 | 940 | 0.1266 | 0.8792 |
| 0.08 | 5.0 | 1175 | 0.1285 | 0.8805 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
|
sandeepaffine/meta-llama-Llama-2-7b-chat-hf-base-cpt-domain-cpt-1L-ift-irdro-dpo | sandeepaffine | 2024-10-16T03:35:26Z | 76 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"dpo",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-10-16T03:33:24Z | ---
library_name: transformers
tags:
- trl
- dpo
---
# 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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## Bias, Risks, and Limitations
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## How to Get Started with the Model
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#### Preprocessing [optional]
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#### 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
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#### 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]
- **Compute Region:** [More Information Needed]
- **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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## Glossary [optional]
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GoldenLlama/krx_qwen2.5_7b_it | GoldenLlama | 2024-10-16T03:31:05Z | 7 | 0 | null | [
"pytorch",
"qwen2",
"krx",
"unsloth",
"trl",
"sft",
"text-generation",
"conversational",
"ko",
"dataset:amphora/krx-sample-instructions",
"base_model:unsloth/Qwen2.5-7B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"region:us"
] | text-generation | 2024-10-15T06:31:48Z | ---
base_model:
- unsloth/Qwen2.5-7B-Instruct
language:
- ko
license: apache-2.0
pipeline_tag: text-generation
tags:
- krx
- unsloth
- trl
- sft
datasets:
- amphora/krx-sample-instructions
---
<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/made with unsloth.png" height="100" width="200" align="center" />
|
luaqi/sn29_back_v7 | luaqi | 2024-10-16T03:24:09Z | 40 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-16T03:20:07Z | ---
library_name: transformers
tags: []
---
# 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
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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
<!-- 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]
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## Model Card Contact
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Inabia-AI/ark_botox_claims_standalone_lora_3.1_10152024 | Inabia-AI | 2024-10-16T03:24:00Z | 13 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-16T03:16:15Z | ---
base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
---
# Uploaded model
- **Developed by:** Inabia-AI
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
zbxxx/phi3-mini-rq | zbxxx | 2024-10-16T03:23:35Z | 9 | 0 | null | [
"safetensors",
"gguf",
"phi3",
"custom_code",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-16T02:58:05Z | ---
license: apache-2.0
---
|
voxinexcelso/model_output | voxinexcelso | 2024-10-16T03:21:09Z | 106 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"HHD",
"10_class",
"multi_labels",
"generated_from_trainer",
"base_model:beomi/kcbert-base",
"base_model:finetune:beomi/kcbert-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-16T03:20:42Z | ---
library_name: transformers
license: apache-2.0
base_model: beomi/kcbert-base
tags:
- HHD
- 10_class
- multi_labels
- generated_from_trainer
model-index:
- name: model_output
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# model_output
This model is a fine-tuned version of [beomi/kcbert-base](https://huggingface.co/beomi/kcbert-base) on the unsmile_data dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1301
- Lrap: 0.8800
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Lrap |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 235 | 0.1458 | 0.8595 |
| No log | 2.0 | 470 | 0.1284 | 0.8745 |
| 0.1701 | 3.0 | 705 | 0.1245 | 0.8774 |
| 0.1701 | 4.0 | 940 | 0.1286 | 0.8799 |
| 0.0787 | 5.0 | 1175 | 0.1301 | 0.8800 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
|
RylanSchaeffer/collapse_gemma-2-2b_hs2_accumulate_iter19_sftsd2 | RylanSchaeffer | 2024-10-16T03:18:46Z | 10 | 0 | null | [
"safetensors",
"gemma2",
"trl",
"sft",
"generated_from_trainer",
"base_model:google/gemma-2-2b",
"base_model:finetune:google/gemma-2-2b",
"license:gemma",
"region:us"
] | null | 2024-10-16T03:14:35Z | ---
license: gemma
base_model: google/gemma-2-2b
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: collapse_gemma-2-2b_hs2_accumulate_iter19_sftsd2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# collapse_gemma-2-2b_hs2_accumulate_iter19_sftsd2
This model is a fine-tuned version of [google/gemma-2-2b](https://huggingface.co/google/gemma-2-2b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1080
- Num Input Tokens Seen: 98464968
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 8e-06
- train_batch_size: 8
- eval_batch_size: 16
- seed: 2
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:------:|:----:|:---------------:|:-----------------:|
| No log | 0 | 0 | 1.3909 | 0 |
| 1.566 | 0.0027 | 5 | 1.3903 | 269240 |
| 1.6034 | 0.0055 | 10 | 1.3837 | 534920 |
| 1.5271 | 0.0082 | 15 | 1.3636 | 803440 |
| 1.5903 | 0.0109 | 20 | 1.3339 | 1075112 |
| 1.4414 | 0.0137 | 25 | 1.2941 | 1347136 |
| 1.3672 | 0.0164 | 30 | 1.2546 | 1608088 |
| 1.3486 | 0.0191 | 35 | 1.2329 | 1883384 |
| 1.2328 | 0.0219 | 40 | 1.2053 | 2147208 |
| 1.0687 | 0.0246 | 45 | 1.2000 | 2413576 |
| 1.0229 | 0.0274 | 50 | 1.2220 | 2678528 |
| 1.0549 | 0.0301 | 55 | 1.2386 | 2944424 |
| 0.8001 | 0.0328 | 60 | 1.2753 | 3212016 |
| 0.827 | 0.0356 | 65 | 1.2962 | 3480872 |
| 0.6043 | 0.0383 | 70 | 1.2797 | 3745240 |
| 0.5734 | 0.0410 | 75 | 1.3074 | 4008872 |
| 0.5398 | 0.0438 | 80 | 1.2996 | 4283816 |
| 0.3939 | 0.0465 | 85 | 1.2942 | 4560864 |
| 0.4721 | 0.0492 | 90 | 1.2616 | 4834608 |
| 0.3256 | 0.0520 | 95 | 1.2648 | 5110216 |
| 0.3049 | 0.0547 | 100 | 1.2537 | 5380104 |
| 0.3462 | 0.0574 | 105 | 1.2385 | 5655128 |
| 0.33 | 0.0602 | 110 | 1.2168 | 5924072 |
| 0.2754 | 0.0629 | 115 | 1.2392 | 6191600 |
| 0.2427 | 0.0657 | 120 | 1.2336 | 6461192 |
| 0.2005 | 0.0684 | 125 | 1.2499 | 6729224 |
| 0.2823 | 0.0711 | 130 | 1.2266 | 7001096 |
| 0.2541 | 0.0739 | 135 | 1.2273 | 7267936 |
| 0.2096 | 0.0766 | 140 | 1.2258 | 7534808 |
| 0.3232 | 0.0793 | 145 | 1.2267 | 7807176 |
| 0.1664 | 0.0821 | 150 | 1.2066 | 8075744 |
| 0.1768 | 0.0848 | 155 | 1.2219 | 8346440 |
| 0.295 | 0.0875 | 160 | 1.2019 | 8614872 |
| 0.2027 | 0.0903 | 165 | 1.2076 | 8884480 |
| 0.1438 | 0.0930 | 170 | 1.2002 | 9150992 |
| 0.2161 | 0.0957 | 175 | 1.2000 | 9419696 |
| 0.152 | 0.0985 | 180 | 1.2019 | 9687968 |
| 0.1512 | 0.1012 | 185 | 1.2031 | 9960504 |
| 0.2432 | 0.1039 | 190 | 1.1993 | 10226056 |
| 0.1829 | 0.1067 | 195 | 1.2022 | 10496824 |
| 0.2622 | 0.1094 | 200 | 1.1973 | 10767928 |
| 0.1414 | 0.1122 | 205 | 1.2021 | 11032952 |
| 0.16 | 0.1149 | 210 | 1.1940 | 11305872 |
| 0.1348 | 0.1176 | 215 | 1.1937 | 11576432 |
| 0.1244 | 0.1204 | 220 | 1.1973 | 11851016 |
| 0.1393 | 0.1231 | 225 | 1.1868 | 12122248 |
| 0.1372 | 0.1258 | 230 | 1.1894 | 12391544 |
| 0.2017 | 0.1286 | 235 | 1.1933 | 12659424 |
| 0.2157 | 0.1313 | 240 | 1.1891 | 12935120 |
| 0.1222 | 0.1340 | 245 | 1.1908 | 13209320 |
| 0.1636 | 0.1368 | 250 | 1.1943 | 13487384 |
| 0.1724 | 0.1395 | 255 | 1.1907 | 13749784 |
| 0.1335 | 0.1422 | 260 | 1.1872 | 14021680 |
| 0.143 | 0.1450 | 265 | 1.1853 | 14289216 |
| 0.0801 | 0.1477 | 270 | 1.1805 | 14557664 |
| 0.1594 | 0.1504 | 275 | 1.1833 | 14821848 |
| 0.096 | 0.1532 | 280 | 1.1829 | 15088088 |
| 0.1399 | 0.1559 | 285 | 1.1820 | 15362192 |
| 0.1576 | 0.1587 | 290 | 1.1823 | 15635952 |
| 0.1494 | 0.1614 | 295 | 1.1823 | 15903016 |
| 0.0803 | 0.1641 | 300 | 1.1824 | 16170560 |
| 0.1924 | 0.1669 | 305 | 1.1808 | 16433512 |
| 0.153 | 0.1696 | 310 | 1.1789 | 16703256 |
| 0.1633 | 0.1723 | 315 | 1.1771 | 16969712 |
| 0.2378 | 0.1751 | 320 | 1.1752 | 17232240 |
| 0.1494 | 0.1778 | 325 | 1.1740 | 17497112 |
| 0.1492 | 0.1805 | 330 | 1.1671 | 17756264 |
| 0.1529 | 0.1833 | 335 | 1.1758 | 18025664 |
| 0.102 | 0.1860 | 340 | 1.1801 | 18289112 |
| 0.1487 | 0.1887 | 345 | 1.1672 | 18557120 |
| 0.1233 | 0.1915 | 350 | 1.1746 | 18822168 |
| 0.1628 | 0.1942 | 355 | 1.1735 | 19093248 |
| 0.2002 | 0.1970 | 360 | 1.1701 | 19364880 |
| 0.1004 | 0.1997 | 365 | 1.1704 | 19622184 |
| 0.1483 | 0.2024 | 370 | 1.1693 | 19896912 |
| 0.1489 | 0.2052 | 375 | 1.1689 | 20167288 |
| 0.1008 | 0.2079 | 380 | 1.1702 | 20430640 |
| 0.1688 | 0.2106 | 385 | 1.1733 | 20691032 |
| 0.1465 | 0.2134 | 390 | 1.1709 | 20966304 |
| 0.1459 | 0.2161 | 395 | 1.1658 | 21240608 |
| 0.0888 | 0.2188 | 400 | 1.1679 | 21510808 |
| 0.0849 | 0.2216 | 405 | 1.1704 | 21780056 |
| 0.1339 | 0.2243 | 410 | 1.1663 | 22048176 |
| 0.1438 | 0.2270 | 415 | 1.1653 | 22318760 |
| 0.1227 | 0.2298 | 420 | 1.1686 | 22588136 |
| 0.1704 | 0.2325 | 425 | 1.1675 | 22858288 |
| 0.1241 | 0.2352 | 430 | 1.1626 | 23127464 |
| 0.0752 | 0.2380 | 435 | 1.1648 | 23395536 |
| 0.102 | 0.2407 | 440 | 1.1658 | 23663200 |
| 0.1354 | 0.2435 | 445 | 1.1626 | 23931888 |
| 0.1178 | 0.2462 | 450 | 1.1594 | 24207088 |
| 0.1556 | 0.2489 | 455 | 1.1623 | 24477104 |
| 0.1249 | 0.2517 | 460 | 1.1635 | 24748184 |
| 0.135 | 0.2544 | 465 | 1.1580 | 25015840 |
| 0.0997 | 0.2571 | 470 | 1.1613 | 25274800 |
| 0.183 | 0.2599 | 475 | 1.1565 | 25538784 |
| 0.1524 | 0.2626 | 480 | 1.1608 | 25811528 |
| 0.1299 | 0.2653 | 485 | 1.1596 | 26081360 |
| 0.1025 | 0.2681 | 490 | 1.1542 | 26348112 |
| 0.1945 | 0.2708 | 495 | 1.1577 | 26607088 |
| 0.1138 | 0.2735 | 500 | 1.1553 | 26879376 |
| 0.1872 | 0.2763 | 505 | 1.1565 | 27151544 |
| 0.1329 | 0.2790 | 510 | 1.1523 | 27416848 |
| 0.1458 | 0.2817 | 515 | 1.1539 | 27684560 |
| 0.1452 | 0.2845 | 520 | 1.1551 | 27954048 |
| 0.1423 | 0.2872 | 525 | 1.1534 | 28220112 |
| 0.0822 | 0.2900 | 530 | 1.1515 | 28494712 |
| 0.1274 | 0.2927 | 535 | 1.1528 | 28761360 |
| 0.1398 | 0.2954 | 540 | 1.1513 | 29029520 |
| 0.1851 | 0.2982 | 545 | 1.1519 | 29301128 |
| 0.1634 | 0.3009 | 550 | 1.1573 | 29575880 |
| 0.0755 | 0.3036 | 555 | 1.1507 | 29842672 |
| 0.1418 | 0.3064 | 560 | 1.1509 | 30111712 |
| 0.1712 | 0.3091 | 565 | 1.1518 | 30379128 |
| 0.1914 | 0.3118 | 570 | 1.1454 | 30647232 |
| 0.1385 | 0.3146 | 575 | 1.1479 | 30905808 |
| 0.1112 | 0.3173 | 580 | 1.1553 | 31173776 |
| 0.114 | 0.3200 | 585 | 1.1492 | 31445496 |
| 0.1453 | 0.3228 | 590 | 1.1466 | 31710376 |
| 0.1367 | 0.3255 | 595 | 1.1467 | 31983840 |
| 0.1817 | 0.3283 | 600 | 1.1466 | 32260416 |
| 0.0955 | 0.3310 | 605 | 1.1466 | 32524816 |
| 0.1501 | 0.3337 | 610 | 1.1478 | 32796840 |
| 0.1512 | 0.3365 | 615 | 1.1445 | 33060472 |
| 0.1882 | 0.3392 | 620 | 1.1483 | 33327176 |
| 0.086 | 0.3419 | 625 | 1.1461 | 33596136 |
| 0.0982 | 0.3447 | 630 | 1.1438 | 33864384 |
| 0.1159 | 0.3474 | 635 | 1.1442 | 34132792 |
| 0.1253 | 0.3501 | 640 | 1.1473 | 34409424 |
| 0.1011 | 0.3529 | 645 | 1.1501 | 34679192 |
| 0.0758 | 0.3556 | 650 | 1.1444 | 34951400 |
| 0.1218 | 0.3583 | 655 | 1.1435 | 35227760 |
| 0.1123 | 0.3611 | 660 | 1.1459 | 35502112 |
| 0.1567 | 0.3638 | 665 | 1.1445 | 35771360 |
| 0.1027 | 0.3665 | 670 | 1.1425 | 36035184 |
| 0.1843 | 0.3693 | 675 | 1.1420 | 36301448 |
| 0.1262 | 0.3720 | 680 | 1.1451 | 36569816 |
| 0.2147 | 0.3748 | 685 | 1.1414 | 36840840 |
| 0.1026 | 0.3775 | 690 | 1.1389 | 37109896 |
| 0.148 | 0.3802 | 695 | 1.1421 | 37380912 |
| 0.1004 | 0.3830 | 700 | 1.1412 | 37651504 |
| 0.1456 | 0.3857 | 705 | 1.1399 | 37915944 |
| 0.1328 | 0.3884 | 710 | 1.1408 | 38187424 |
| 0.1516 | 0.3912 | 715 | 1.1409 | 38452472 |
| 0.1423 | 0.3939 | 720 | 1.1401 | 38725544 |
| 0.0693 | 0.3966 | 725 | 1.1411 | 39002936 |
| 0.145 | 0.3994 | 730 | 1.1369 | 39283776 |
| 0.0908 | 0.4021 | 735 | 1.1383 | 39556048 |
| 0.1767 | 0.4048 | 740 | 1.1386 | 39827376 |
| 0.0885 | 0.4076 | 745 | 1.1348 | 40095824 |
| 0.1309 | 0.4103 | 750 | 1.1362 | 40365560 |
| 0.1427 | 0.4130 | 755 | 1.1353 | 40639216 |
| 0.1133 | 0.4158 | 760 | 1.1353 | 40905800 |
| 0.0964 | 0.4185 | 765 | 1.1383 | 41184040 |
| 0.1247 | 0.4213 | 770 | 1.1381 | 41449464 |
| 0.1036 | 0.4240 | 775 | 1.1375 | 41718976 |
| 0.0943 | 0.4267 | 780 | 1.1362 | 41988192 |
| 0.1213 | 0.4295 | 785 | 1.1392 | 42252704 |
| 0.0691 | 0.4322 | 790 | 1.1395 | 42515528 |
| 0.0992 | 0.4349 | 795 | 1.1395 | 42778600 |
| 0.1536 | 0.4377 | 800 | 1.1385 | 43050368 |
| 0.1415 | 0.4404 | 805 | 1.1359 | 43317456 |
| 0.107 | 0.4431 | 810 | 1.1363 | 43579336 |
| 0.0727 | 0.4459 | 815 | 1.1364 | 43851016 |
| 0.1084 | 0.4486 | 820 | 1.1350 | 44119640 |
| 0.1327 | 0.4513 | 825 | 1.1331 | 44388024 |
| 0.1569 | 0.4541 | 830 | 1.1323 | 44658024 |
| 0.0889 | 0.4568 | 835 | 1.1370 | 44926688 |
| 0.1194 | 0.4596 | 840 | 1.1365 | 45197880 |
| 0.1314 | 0.4623 | 845 | 1.1336 | 45473008 |
| 0.0597 | 0.4650 | 850 | 1.1368 | 45745296 |
| 0.055 | 0.4678 | 855 | 1.1364 | 46015736 |
| 0.1205 | 0.4705 | 860 | 1.1365 | 46293592 |
| 0.1283 | 0.4732 | 865 | 1.1349 | 46568376 |
| 0.1195 | 0.4760 | 870 | 1.1309 | 46840344 |
| 0.1391 | 0.4787 | 875 | 1.1355 | 47110488 |
| 0.1324 | 0.4814 | 880 | 1.1324 | 47377704 |
| 0.1026 | 0.4842 | 885 | 1.1300 | 47635920 |
| 0.1301 | 0.4869 | 890 | 1.1327 | 47908560 |
| 0.124 | 0.4896 | 895 | 1.1341 | 48177136 |
| 0.1325 | 0.4924 | 900 | 1.1297 | 48446816 |
| 0.1446 | 0.4951 | 905 | 1.1292 | 48714528 |
| 0.1525 | 0.4978 | 910 | 1.1325 | 48984416 |
| 0.1612 | 0.5006 | 915 | 1.1309 | 49255328 |
| 0.1277 | 0.5033 | 920 | 1.1285 | 49522720 |
| 0.141 | 0.5061 | 925 | 1.1295 | 49794632 |
| 0.1233 | 0.5088 | 930 | 1.1309 | 50059784 |
| 0.0937 | 0.5115 | 935 | 1.1296 | 50334856 |
| 0.1243 | 0.5143 | 940 | 1.1282 | 50606240 |
| 0.1368 | 0.5170 | 945 | 1.1296 | 50873896 |
| 0.1006 | 0.5197 | 950 | 1.1287 | 51146576 |
| 0.0868 | 0.5225 | 955 | 1.1274 | 51419272 |
| 0.1008 | 0.5252 | 960 | 1.1267 | 51691160 |
| 0.1 | 0.5279 | 965 | 1.1308 | 51958064 |
| 0.0645 | 0.5307 | 970 | 1.1296 | 52226760 |
| 0.0955 | 0.5334 | 975 | 1.1296 | 52497824 |
| 0.13 | 0.5361 | 980 | 1.1304 | 52770296 |
| 0.1249 | 0.5389 | 985 | 1.1275 | 53040968 |
| 0.1615 | 0.5416 | 990 | 1.1263 | 53307232 |
| 0.0752 | 0.5443 | 995 | 1.1275 | 53579368 |
| 0.131 | 0.5471 | 1000 | 1.1288 | 53851264 |
| 0.0688 | 0.5498 | 1005 | 1.1296 | 54118360 |
| 0.1028 | 0.5526 | 1010 | 1.1283 | 54388792 |
| 0.1286 | 0.5553 | 1015 | 1.1263 | 54649048 |
| 0.1078 | 0.5580 | 1020 | 1.1243 | 54911104 |
| 0.1046 | 0.5608 | 1025 | 1.1260 | 55177560 |
| 0.1033 | 0.5635 | 1030 | 1.1274 | 55450744 |
| 0.1 | 0.5662 | 1035 | 1.1249 | 55720440 |
| 0.0748 | 0.5690 | 1040 | 1.1250 | 55995040 |
| 0.1346 | 0.5717 | 1045 | 1.1253 | 56272760 |
| 0.1358 | 0.5744 | 1050 | 1.1248 | 56548912 |
| 0.1207 | 0.5772 | 1055 | 1.1265 | 56816872 |
| 0.1846 | 0.5799 | 1060 | 1.1280 | 57079520 |
| 0.1064 | 0.5826 | 1065 | 1.1259 | 57346080 |
| 0.0912 | 0.5854 | 1070 | 1.1232 | 57613104 |
| 0.0811 | 0.5881 | 1075 | 1.1243 | 57877976 |
| 0.1331 | 0.5909 | 1080 | 1.1235 | 58157936 |
| 0.0908 | 0.5936 | 1085 | 1.1234 | 58427112 |
| 0.1493 | 0.5963 | 1090 | 1.1230 | 58696168 |
| 0.0947 | 0.5991 | 1095 | 1.1224 | 58964768 |
| 0.0883 | 0.6018 | 1100 | 1.1225 | 59233552 |
| 0.1224 | 0.6045 | 1105 | 1.1237 | 59508416 |
| 0.0844 | 0.6073 | 1110 | 1.1243 | 59780056 |
| 0.1231 | 0.6100 | 1115 | 1.1219 | 60053512 |
| 0.0704 | 0.6127 | 1120 | 1.1228 | 60323992 |
| 0.1217 | 0.6155 | 1125 | 1.1247 | 60591480 |
| 0.1333 | 0.6182 | 1130 | 1.1247 | 60860808 |
| 0.1773 | 0.6209 | 1135 | 1.1233 | 61129280 |
| 0.0739 | 0.6237 | 1140 | 1.1230 | 61396264 |
| 0.1076 | 0.6264 | 1145 | 1.1237 | 61676200 |
| 0.1018 | 0.6291 | 1150 | 1.1227 | 61939504 |
| 0.0889 | 0.6319 | 1155 | 1.1217 | 62206840 |
| 0.0848 | 0.6346 | 1160 | 1.1220 | 62479304 |
| 0.1288 | 0.6374 | 1165 | 1.1210 | 62744128 |
| 0.1336 | 0.6401 | 1170 | 1.1189 | 63009680 |
| 0.1311 | 0.6428 | 1175 | 1.1220 | 63283488 |
| 0.0721 | 0.6456 | 1180 | 1.1224 | 63551544 |
| 0.0833 | 0.6483 | 1185 | 1.1202 | 63828120 |
| 0.159 | 0.6510 | 1190 | 1.1216 | 64097744 |
| 0.1364 | 0.6538 | 1195 | 1.1222 | 64365456 |
| 0.122 | 0.6565 | 1200 | 1.1217 | 64639512 |
| 0.0556 | 0.6592 | 1205 | 1.1209 | 64917008 |
| 0.0958 | 0.6620 | 1210 | 1.1224 | 65186600 |
| 0.1396 | 0.6647 | 1215 | 1.1221 | 65457240 |
| 0.1406 | 0.6674 | 1220 | 1.1219 | 65724384 |
| 0.183 | 0.6702 | 1225 | 1.1201 | 65991472 |
| 0.1442 | 0.6729 | 1230 | 1.1202 | 66247368 |
| 0.1432 | 0.6756 | 1235 | 1.1193 | 66519352 |
| 0.1047 | 0.6784 | 1240 | 1.1177 | 66793464 |
| 0.1665 | 0.6811 | 1245 | 1.1211 | 67057776 |
| 0.1044 | 0.6839 | 1250 | 1.1204 | 67336200 |
| 0.1104 | 0.6866 | 1255 | 1.1177 | 67599384 |
| 0.1749 | 0.6893 | 1260 | 1.1179 | 67867712 |
| 0.0799 | 0.6921 | 1265 | 1.1200 | 68128816 |
| 0.0796 | 0.6948 | 1270 | 1.1188 | 68394968 |
| 0.1661 | 0.6975 | 1275 | 1.1176 | 68670640 |
| 0.0956 | 0.7003 | 1280 | 1.1166 | 68940800 |
| 0.1501 | 0.7030 | 1285 | 1.1181 | 69209672 |
| 0.1093 | 0.7057 | 1290 | 1.1189 | 69479976 |
| 0.0632 | 0.7085 | 1295 | 1.1171 | 69747392 |
| 0.1077 | 0.7112 | 1300 | 1.1172 | 70015144 |
| 0.0881 | 0.7139 | 1305 | 1.1179 | 70285856 |
| 0.0972 | 0.7167 | 1310 | 1.1200 | 70554584 |
| 0.107 | 0.7194 | 1315 | 1.1186 | 70823800 |
| 0.1226 | 0.7222 | 1320 | 1.1184 | 71086808 |
| 0.1196 | 0.7249 | 1325 | 1.1198 | 71361488 |
| 0.088 | 0.7276 | 1330 | 1.1188 | 71640264 |
| 0.102 | 0.7304 | 1335 | 1.1177 | 71912832 |
| 0.1277 | 0.7331 | 1340 | 1.1172 | 72182800 |
| 0.1844 | 0.7358 | 1345 | 1.1174 | 72448048 |
| 0.1159 | 0.7386 | 1350 | 1.1191 | 72715976 |
| 0.1276 | 0.7413 | 1355 | 1.1187 | 72990168 |
| 0.0715 | 0.7440 | 1360 | 1.1169 | 73255144 |
| 0.142 | 0.7468 | 1365 | 1.1169 | 73516344 |
| 0.158 | 0.7495 | 1370 | 1.1182 | 73794232 |
| 0.1622 | 0.7522 | 1375 | 1.1167 | 74065848 |
| 0.1652 | 0.7550 | 1380 | 1.1155 | 74333144 |
| 0.1542 | 0.7577 | 1385 | 1.1164 | 74599680 |
| 0.1395 | 0.7604 | 1390 | 1.1150 | 74868184 |
| 0.0988 | 0.7632 | 1395 | 1.1163 | 75131992 |
| 0.0901 | 0.7659 | 1400 | 1.1163 | 75396456 |
| 0.1068 | 0.7687 | 1405 | 1.1165 | 75659952 |
| 0.1644 | 0.7714 | 1410 | 1.1169 | 75936512 |
| 0.1194 | 0.7741 | 1415 | 1.1158 | 76201496 |
| 0.1356 | 0.7769 | 1420 | 1.1134 | 76478872 |
| 0.1126 | 0.7796 | 1425 | 1.1144 | 76746472 |
| 0.0919 | 0.7823 | 1430 | 1.1150 | 77017520 |
| 0.1469 | 0.7851 | 1435 | 1.1155 | 77284784 |
| 0.1433 | 0.7878 | 1440 | 1.1149 | 77551680 |
| 0.0743 | 0.7905 | 1445 | 1.1138 | 77821136 |
| 0.1669 | 0.7933 | 1450 | 1.1142 | 78093368 |
| 0.1076 | 0.7960 | 1455 | 1.1145 | 78362664 |
| 0.0973 | 0.7987 | 1460 | 1.1113 | 78635256 |
| 0.0809 | 0.8015 | 1465 | 1.1130 | 78904024 |
| 0.0797 | 0.8042 | 1470 | 1.1152 | 79176312 |
| 0.1293 | 0.8069 | 1475 | 1.1165 | 79444528 |
| 0.1529 | 0.8097 | 1480 | 1.1143 | 79718240 |
| 0.1049 | 0.8124 | 1485 | 1.1136 | 79991048 |
| 0.0838 | 0.8152 | 1490 | 1.1137 | 80259104 |
| 0.1662 | 0.8179 | 1495 | 1.1144 | 80530856 |
| 0.1528 | 0.8206 | 1500 | 1.1128 | 80796672 |
| 0.0886 | 0.8234 | 1505 | 1.1138 | 81055744 |
| 0.0816 | 0.8261 | 1510 | 1.1160 | 81325704 |
| 0.1051 | 0.8288 | 1515 | 1.1127 | 81598344 |
| 0.1674 | 0.8316 | 1520 | 1.1123 | 81866048 |
| 0.1408 | 0.8343 | 1525 | 1.1113 | 82139456 |
| 0.1149 | 0.8370 | 1530 | 1.1134 | 82424128 |
| 0.1034 | 0.8398 | 1535 | 1.1147 | 82693032 |
| 0.0741 | 0.8425 | 1540 | 1.1131 | 82962848 |
| 0.1875 | 0.8452 | 1545 | 1.1126 | 83233240 |
| 0.1677 | 0.8480 | 1550 | 1.1145 | 83504368 |
| 0.1579 | 0.8507 | 1555 | 1.1140 | 83775296 |
| 0.13 | 0.8535 | 1560 | 1.1137 | 84048072 |
| 0.0949 | 0.8562 | 1565 | 1.1117 | 84320136 |
| 0.1196 | 0.8589 | 1570 | 1.1126 | 84584048 |
| 0.126 | 0.8617 | 1575 | 1.1116 | 84856504 |
| 0.0681 | 0.8644 | 1580 | 1.1104 | 85127256 |
| 0.1589 | 0.8671 | 1585 | 1.1110 | 85391472 |
| 0.1435 | 0.8699 | 1590 | 1.1120 | 85670424 |
| 0.1237 | 0.8726 | 1595 | 1.1115 | 85933648 |
| 0.1618 | 0.8753 | 1600 | 1.1128 | 86205312 |
| 0.0772 | 0.8781 | 1605 | 1.1142 | 86474080 |
| 0.138 | 0.8808 | 1610 | 1.1137 | 86747368 |
| 0.1592 | 0.8835 | 1615 | 1.1122 | 87022320 |
| 0.1275 | 0.8863 | 1620 | 1.1115 | 87281000 |
| 0.0641 | 0.8890 | 1625 | 1.1114 | 87544016 |
| 0.1032 | 0.8917 | 1630 | 1.1112 | 87813640 |
| 0.1413 | 0.8945 | 1635 | 1.1123 | 88083872 |
| 0.1243 | 0.8972 | 1640 | 1.1095 | 88363088 |
| 0.1232 | 0.9000 | 1645 | 1.1105 | 88638376 |
| 0.1382 | 0.9027 | 1650 | 1.1098 | 88906088 |
| 0.1171 | 0.9054 | 1655 | 1.1087 | 89179912 |
| 0.1533 | 0.9082 | 1660 | 1.1117 | 89448520 |
| 0.1143 | 0.9109 | 1665 | 1.1124 | 89718944 |
| 0.1055 | 0.9136 | 1670 | 1.1099 | 89987376 |
| 0.111 | 0.9164 | 1675 | 1.1090 | 90265384 |
| 0.0757 | 0.9191 | 1680 | 1.1111 | 90531896 |
| 0.1295 | 0.9218 | 1685 | 1.1122 | 90801128 |
| 0.1262 | 0.9246 | 1690 | 1.1090 | 91080224 |
| 0.1637 | 0.9273 | 1695 | 1.1088 | 91346424 |
| 0.0961 | 0.9300 | 1700 | 1.1094 | 91620224 |
| 0.1968 | 0.9328 | 1705 | 1.1078 | 91891560 |
| 0.1257 | 0.9355 | 1710 | 1.1094 | 92164248 |
| 0.1734 | 0.9382 | 1715 | 1.1109 | 92435768 |
| 0.1424 | 0.9410 | 1720 | 1.1101 | 92704608 |
| 0.1874 | 0.9437 | 1725 | 1.1099 | 92983192 |
| 0.1318 | 0.9465 | 1730 | 1.1084 | 93243792 |
| 0.1153 | 0.9492 | 1735 | 1.1086 | 93517248 |
| 0.1283 | 0.9519 | 1740 | 1.1081 | 93791240 |
| 0.1191 | 0.9547 | 1745 | 1.1075 | 94059424 |
| 0.1424 | 0.9574 | 1750 | 1.1089 | 94326944 |
| 0.1458 | 0.9601 | 1755 | 1.1093 | 94594848 |
| 0.1361 | 0.9629 | 1760 | 1.1081 | 94861000 |
| 0.1238 | 0.9656 | 1765 | 1.1060 | 95141936 |
| 0.1341 | 0.9683 | 1770 | 1.1077 | 95409544 |
| 0.2192 | 0.9711 | 1775 | 1.1079 | 95681696 |
| 0.1611 | 0.9738 | 1780 | 1.1059 | 95946160 |
| 0.088 | 0.9765 | 1785 | 1.1061 | 96209080 |
| 0.1224 | 0.9793 | 1790 | 1.1095 | 96476168 |
| 0.109 | 0.9820 | 1795 | 1.1088 | 96749632 |
| 0.0898 | 0.9848 | 1800 | 1.1047 | 97023680 |
| 0.0734 | 0.9875 | 1805 | 1.1051 | 97298760 |
| 0.0984 | 0.9902 | 1810 | 1.1070 | 97563008 |
| 0.1193 | 0.9930 | 1815 | 1.1065 | 97826504 |
| 0.1278 | 0.9957 | 1820 | 1.1063 | 98091392 |
| 0.1163 | 0.9984 | 1825 | 1.1074 | 98363120 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
maplecloud/AiProjectNick | maplecloud | 2024-10-16T03:17:39Z | 76 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-10-16T02:38:59Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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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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MathGenie/MathCoder2-DeepSeekMath-7B | MathGenie | 2024-10-16T03:01:35Z | 33 | 5 | null | [
"safetensors",
"llama",
"math",
"text-generation",
"en",
"dataset:MathGenie/MathCode-Pile",
"arxiv:2410.08196",
"base_model:deepseek-ai/deepseek-math-7b-base",
"base_model:finetune:deepseek-ai/deepseek-math-7b-base",
"license:apache-2.0",
"region:us"
] | text-generation | 2024-09-30T06:35:51Z | ---
license: apache-2.0
datasets:
- MathGenie/MathCode-Pile
language:
- en
metrics:
- accuracy
base_model:
- deepseek-ai/deepseek-math-7b-base
pipeline_tag: text-generation
tags:
- math
---
# MathCoder2
### Introduction
The MathCoder2 models are created by conducting continued pretraining on [MathCode-Pile](https://huggingface.co/datasets/MathGenie/MathCode-Pile). They are introduced in the paper [MathCoder2: Better Math Reasoning from Continued Pretraining on Model-translated Mathematical Code](https://arxiv.org/abs/2410.08196).
The mathematical pretraining dataset includes mathematical code accompanied with natural language reasoning steps, making it a superior resource for models aimed at performing advanced mathematical reasoning tasks.
### Evaluation
### Citation
If you find this repository helpful, please consider citing our papers:
```
@misc{lu2024mathcoder2bettermathreasoning,
title={MathCoder2: Better Math Reasoning from Continued Pretraining on Model-translated Mathematical Code},
author={Zimu Lu and Aojun Zhou and Ke Wang and Houxing Ren and Weikang Shi and Junting Pan and Mingjie Zhan and Hongsheng Li},
year={2024},
eprint={2410.08196},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2410.08196},
}
```
```
@inproceedings{
wang2024mathcoder,
title={MathCoder: Seamless Code Integration in {LLM}s for Enhanced Mathematical Reasoning},
author={Zimu Lu and Aojun Zhou and Zimu Lu and Sichun Luo and Weikang Shi and Renrui Zhang and Linqi Song and Mingjie Zhan and Hongsheng Li},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=z8TW0ttBPp}
}
```
|
MathGenie/MathCoder2-CodeLlama-7B | MathGenie | 2024-10-16T03:00:35Z | 37 | 5 | null | [
"safetensors",
"llama",
"math",
"text-generation",
"en",
"dataset:MathGenie/MathCode-Pile",
"arxiv:2410.08196",
"base_model:codellama/CodeLlama-7b-hf",
"base_model:finetune:codellama/CodeLlama-7b-hf",
"license:apache-2.0",
"region:us"
] | text-generation | 2024-09-30T03:13:52Z | ---
license: apache-2.0
datasets:
- MathGenie/MathCode-Pile
language:
- en
metrics:
- accuracy
base_model:
- codellama/CodeLlama-7b-hf
pipeline_tag: text-generation
tags:
- math
---
# MathCoder2
### Introduction
The MathCoder2 models are created by conducting continued pretraining on [MathCode-Pile](https://huggingface.co/datasets/MathGenie/MathCode-Pile). They are introduced in the paper [MathCoder2: Better Math Reasoning from Continued Pretraining on Model-translated Mathematical Code](https://arxiv.org/abs/2410.08196).
The mathematical pretraining dataset includes mathematical code accompanied with natural language reasoning steps, making it a superior resource for models aimed at performing advanced mathematical reasoning tasks.
### Evaluation
### Citation
If you find this repository helpful, please consider citing our papers:
```
@misc{lu2024mathcoder2bettermathreasoning,
title={MathCoder2: Better Math Reasoning from Continued Pretraining on Model-translated Mathematical Code},
author={Zimu Lu and Aojun Zhou and Ke Wang and Houxing Ren and Weikang Shi and Junting Pan and Mingjie Zhan and Hongsheng Li},
year={2024},
eprint={2410.08196},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2410.08196},
}
```
```
@inproceedings{
wang2024mathcoder,
title={MathCoder: Seamless Code Integration in {LLM}s for Enhanced Mathematical Reasoning},
author={Zimu Lu and Aojun Zhou and Zimu Lu and Sichun Luo and Weikang Shi and Renrui Zhang and Linqi Song and Mingjie Zhan and Hongsheng Li},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=z8TW0ttBPp}
}
``` |
MathGenie/MathCoder2-Mistral-7B | MathGenie | 2024-10-16T03:00:11Z | 19 | 2 | null | [
"safetensors",
"mistral",
"math",
"text-generation",
"en",
"dataset:MathGenie/MathCode-Pile",
"arxiv:2410.08196",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:finetune:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | text-generation | 2024-09-30T03:12:48Z | ---
license: apache-2.0
datasets:
- MathGenie/MathCode-Pile
language:
- en
metrics:
- accuracy
base_model:
- mistralai/Mistral-7B-v0.1
pipeline_tag: text-generation
tags:
- math
---
# MathCoder2
### Introduction
The MathCoder2 models are created by conducting continued pretraining on [MathCode-Pile](https://huggingface.co/datasets/MathGenie/MathCode-Pile). They are introduced in the paper [MathCoder2: Better Math Reasoning from Continued Pretraining on Model-translated Mathematical Code](https://arxiv.org/abs/2410.08196).
The mathematical pretraining dataset includes mathematical code accompanied with natural language reasoning steps, making it a superior resource for models aimed at performing advanced mathematical reasoning tasks.
### Evaluation
### Citation
If you find this repository helpful, please consider citing our papers:
```
@misc{lu2024mathcoder2bettermathreasoning,
title={MathCoder2: Better Math Reasoning from Continued Pretraining on Model-translated Mathematical Code},
author={Zimu Lu and Aojun Zhou and Ke Wang and Houxing Ren and Weikang Shi and Junting Pan and Mingjie Zhan and Hongsheng Li},
year={2024},
eprint={2410.08196},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2410.08196},
}
```
```
@inproceedings{
wang2024mathcoder,
title={MathCoder: Seamless Code Integration in {LLM}s for Enhanced Mathematical Reasoning},
author={Zimu Lu and Aojun Zhou and Zimu Lu and Sichun Luo and Weikang Shi and Renrui Zhang and Linqi Song and Mingjie Zhan and Hongsheng Li},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=z8TW0ttBPp}
}
``` |
MathGenie/MathCoder2-Llama-3-8B | MathGenie | 2024-10-16T02:59:33Z | 274 | 7 | null | [
"safetensors",
"llama",
"math",
"text-generation",
"conversational",
"en",
"dataset:MathGenie/MathCode-Pile",
"arxiv:2410.08196",
"base_model:meta-llama/Meta-Llama-3-8B",
"base_model:finetune:meta-llama/Meta-Llama-3-8B",
"license:apache-2.0",
"region:us"
] | text-generation | 2024-09-30T03:11:41Z | ---
license: apache-2.0
datasets:
- MathGenie/MathCode-Pile
language:
- en
metrics:
- accuracy
base_model:
- meta-llama/Meta-Llama-3-8B
pipeline_tag: text-generation
tags:
- math
---
# MathCoder2
### Introduction
The MathCoder2 models are created by conducting continued pretraining on [MathCode-Pile](https://huggingface.co/datasets/MathGenie/MathCode-Pile). They are introduced in the paper [MathCoder2: Better Math Reasoning from Continued Pretraining on Model-translated Mathematical Code](https://arxiv.org/abs/2410.08196).
The mathematical pretraining dataset includes mathematical code accompanied with natural language reasoning steps, making it a superior resource for models aimed at performing advanced mathematical reasoning tasks.
### Evaluation
### Citation
If you find this repository helpful, please consider citing our papers:
```
@misc{lu2024mathcoder2bettermathreasoning,
title={MathCoder2: Better Math Reasoning from Continued Pretraining on Model-translated Mathematical Code},
author={Zimu Lu and Aojun Zhou and Ke Wang and Houxing Ren and Weikang Shi and Junting Pan and Mingjie Zhan and Hongsheng Li},
year={2024},
eprint={2410.08196},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2410.08196},
}
```
```
@inproceedings{
wang2024mathcoder,
title={MathCoder: Seamless Code Integration in {LLM}s for Enhanced Mathematical Reasoning},
author={Zimu Lu and Aojun Zhou and Zimu Lu and Sichun Luo and Weikang Shi and Renrui Zhang and Linqi Song and Mingjie Zhan and Hongsheng Li},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=z8TW0ttBPp}
}
``` |
RichardErkhov/martimfasantos_-_tinyllama-1.1b-mt-sft-full-gguf | RichardErkhov | 2024-10-16T02:29:00Z | 5 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us"
] | null | 2024-10-16T02:00:48Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
tinyllama-1.1b-mt-sft-full - GGUF
- Model creator: https://huggingface.co/martimfasantos/
- Original model: https://huggingface.co/martimfasantos/tinyllama-1.1b-mt-sft-full/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [tinyllama-1.1b-mt-sft-full.Q2_K.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-mt-sft-full-gguf/blob/main/tinyllama-1.1b-mt-sft-full.Q2_K.gguf) | Q2_K | 0.4GB |
| [tinyllama-1.1b-mt-sft-full.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-mt-sft-full-gguf/blob/main/tinyllama-1.1b-mt-sft-full.IQ3_XS.gguf) | IQ3_XS | 0.44GB |
| [tinyllama-1.1b-mt-sft-full.IQ3_S.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-mt-sft-full-gguf/blob/main/tinyllama-1.1b-mt-sft-full.IQ3_S.gguf) | IQ3_S | 0.47GB |
| [tinyllama-1.1b-mt-sft-full.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-mt-sft-full-gguf/blob/main/tinyllama-1.1b-mt-sft-full.Q3_K_S.gguf) | Q3_K_S | 0.47GB |
| [tinyllama-1.1b-mt-sft-full.IQ3_M.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-mt-sft-full-gguf/blob/main/tinyllama-1.1b-mt-sft-full.IQ3_M.gguf) | IQ3_M | 0.48GB |
| [tinyllama-1.1b-mt-sft-full.Q3_K.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-mt-sft-full-gguf/blob/main/tinyllama-1.1b-mt-sft-full.Q3_K.gguf) | Q3_K | 0.51GB |
| [tinyllama-1.1b-mt-sft-full.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-mt-sft-full-gguf/blob/main/tinyllama-1.1b-mt-sft-full.Q3_K_M.gguf) | Q3_K_M | 0.51GB |
| [tinyllama-1.1b-mt-sft-full.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-mt-sft-full-gguf/blob/main/tinyllama-1.1b-mt-sft-full.Q3_K_L.gguf) | Q3_K_L | 0.55GB |
| [tinyllama-1.1b-mt-sft-full.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-mt-sft-full-gguf/blob/main/tinyllama-1.1b-mt-sft-full.IQ4_XS.gguf) | IQ4_XS | 0.57GB |
| [tinyllama-1.1b-mt-sft-full.Q4_0.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-mt-sft-full-gguf/blob/main/tinyllama-1.1b-mt-sft-full.Q4_0.gguf) | Q4_0 | 0.59GB |
| [tinyllama-1.1b-mt-sft-full.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-mt-sft-full-gguf/blob/main/tinyllama-1.1b-mt-sft-full.IQ4_NL.gguf) | IQ4_NL | 0.6GB |
| [tinyllama-1.1b-mt-sft-full.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-mt-sft-full-gguf/blob/main/tinyllama-1.1b-mt-sft-full.Q4_K_S.gguf) | Q4_K_S | 0.6GB |
| [tinyllama-1.1b-mt-sft-full.Q4_K.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-mt-sft-full-gguf/blob/main/tinyllama-1.1b-mt-sft-full.Q4_K.gguf) | Q4_K | 0.62GB |
| [tinyllama-1.1b-mt-sft-full.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-mt-sft-full-gguf/blob/main/tinyllama-1.1b-mt-sft-full.Q4_K_M.gguf) | Q4_K_M | 0.62GB |
| [tinyllama-1.1b-mt-sft-full.Q4_1.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-mt-sft-full-gguf/blob/main/tinyllama-1.1b-mt-sft-full.Q4_1.gguf) | Q4_1 | 0.65GB |
| [tinyllama-1.1b-mt-sft-full.Q5_0.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-mt-sft-full-gguf/blob/main/tinyllama-1.1b-mt-sft-full.Q5_0.gguf) | Q5_0 | 0.71GB |
| [tinyllama-1.1b-mt-sft-full.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-mt-sft-full-gguf/blob/main/tinyllama-1.1b-mt-sft-full.Q5_K_S.gguf) | Q5_K_S | 0.71GB |
| [tinyllama-1.1b-mt-sft-full.Q5_K.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-mt-sft-full-gguf/blob/main/tinyllama-1.1b-mt-sft-full.Q5_K.gguf) | Q5_K | 0.73GB |
| [tinyllama-1.1b-mt-sft-full.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-mt-sft-full-gguf/blob/main/tinyllama-1.1b-mt-sft-full.Q5_K_M.gguf) | Q5_K_M | 0.73GB |
| [tinyllama-1.1b-mt-sft-full.Q5_1.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-mt-sft-full-gguf/blob/main/tinyllama-1.1b-mt-sft-full.Q5_1.gguf) | Q5_1 | 0.77GB |
| [tinyllama-1.1b-mt-sft-full.Q6_K.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-mt-sft-full-gguf/blob/main/tinyllama-1.1b-mt-sft-full.Q6_K.gguf) | Q6_K | 0.84GB |
| [tinyllama-1.1b-mt-sft-full.Q8_0.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-mt-sft-full-gguf/blob/main/tinyllama-1.1b-mt-sft-full.Q8_0.gguf) | Q8_0 | 1.09GB |
Original model description:
---
license: apache-2.0
base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
- trl
- sft
- generated_from_trainer
datasets:
- haoranxu/ALMA-Human-Parallel
model-index:
- name: tinyllama-1.1b-mt-sft-full
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tinyllama-1.1b-mt-sft-full
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) on the haoranxu/ALMA-Human-Parallel dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6920
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5838 | 1.0 | 739 | 1.6892 |
| 1.5051 | 2.0 | 1478 | 1.6920 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.19.1
|
bigstorm/Llama-3.1-Nemotron-70B-Instruct-HF-7.0bpw-8hb-exl2 | bigstorm | 2024-10-16T02:19:04Z | 7 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"nvidia",
"llama3.1",
"conversational",
"en",
"dataset:nvidia/HelpSteer2",
"arxiv:2410.01257",
"arxiv:2406.08673",
"arxiv:2310.05344",
"arxiv:2311.09528",
"base_model:meta-llama/Llama-3.1-70B-Instruct",
"base_model:quantized:meta-llama/Llama-3.1-70B-Instruct",
"license:llama3.1",
"autotrain_compatible",
"text-generation-inference",
"7-bit",
"exl2",
"region:us"
] | text-generation | 2024-10-16T02:00:42Z | ---
license: llama3.1
language:
- en
inference: false
fine-tuning: false
tags:
- nvidia
- llama3.1
datasets:
- nvidia/HelpSteer2
base_model: meta-llama/Llama-3.1-70B-Instruct
pipeline_tag: text-generation
library_name: transformers
---
# BigStorm - ExLLamaV2 (Exl2) Quantization
- 7.0 bpw target
- 8 head bits
Enjoy! Raise an issue if you'd like other BPW levels.
**Base Model Card Follows:**
---
# Model Overview
## Description:
Llama-3.1-Nemotron-70B-Instruct is a large language model customized by NVIDIA to improve the helpfulness of LLM generated responses to user queries.
This model reaches [Arena Hard](https://github.com/lmarena/arena-hard-auto) of 85.0, [AlpacaEval 2 LC](https://tatsu-lab.github.io/alpaca_eval/) of 57.6 and [GPT-4-Turbo MT-Bench](https://github.com/lm-sys/FastChat/pull/3158) of 8.98, which are known to be predictive of [LMSys Chatbot Arena Elo](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard)
As of 1 Oct 2024, this model is #1 on all three automatic alignment benchmarks (verified tab for AlpacaEval 2 LC), edging out strong frontier models such as GPT-4o and Claude 3.5 Sonnet.
This model was trained using RLHF (specifically, REINFORCE), [Llama-3.1-Nemotron-70B-Reward](https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Reward) and [HelpSteer2-Preference prompts](https://huggingface.co/datasets/nvidia/HelpSteer2) on a Llama-3.1-70B-Instruct model as the initial policy.
Llama-3.1-Nemotron-70B-Instruct-HF has been converted from [Llama-3.1-Nemotron-70B-Instruct](https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct) to support it in the HuggingFace Transformers codebase. Please note that evaluation results might be slightly different from the [Llama-3.1-Nemotron-70B-Instruct](https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct) as evaluated in NeMo-Aligner, which the evaluation results below are based on.
Try hosted inference for free at [build.nvidia.com](https://build.nvidia.com/nvidia/llama-3_1-nemotron-70b-instruct) - it comes with an OpenAI-compatible API interface.
See details on our paper at [https://arxiv.org/abs/2410.01257](https://arxiv.org/abs/2410.01257) - as a preview, this model can correctly the question ```How many r in strawberry?``` without specialized prompting or additional reasoning tokens:
```
A sweet question!
Let’s count the “R”s in “strawberry”:
1. S
2. T
3. R
4. A
5. W
6. B
7. E
8. R
9. R
10. Y
There are **3 “R”s** in the word “strawberry”.
```
Note: This model is a demonstration of our techniques for improving helpfulness in general-domain instruction following. It has not been tuned for performance in specialized domains such as math.
## Terms of use
By accessing this model, you are agreeing to the LLama 3.1 terms and conditions of the [license](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE), [acceptable use policy](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/USE_POLICY.md) and [Meta’s privacy policy](https://www.facebook.com/privacy/policy/)
## Evaluation Metrics
As of 1 Oct 2024, Llama-3.1-Nemotron-70B-Instruct performs best on Arena Hard, AlpacaEval 2 LC (verified tab) and MT Bench (GPT-4-Turbo)
| Model | Arena Hard | AlpacaEval | MT-Bench | Mean Response Length |
|:-----------------------------|:----------------|:-----|:----------|:-------|
|Details | (95% CI) | 2 LC (SE) | (GPT-4-Turbo) | (# of Characters for MT-Bench)|
| _**Llama-3.1-Nemotron-70B-Instruct**_ | **85.0** (-1.5, 1.5) | **57.6** (1.65) | **8.98** | 2199.8 |
| Llama-3.1-70B-Instruct | 55.7 (-2.9, 2.7) | 38.1 (0.90) | 8.22 | 1728.6 |
| Llama-3.1-405B-Instruct | 69.3 (-2.4, 2.2) | 39.3 (1.43) | 8.49 | 1664.7 |
| Claude-3-5-Sonnet-20240620 | 79.2 (-1.9, 1.7) | 52.4 (1.47) | 8.81 | 1619.9 |
| GPT-4o-2024-05-13 | 79.3 (-2.1, 2.0) | 57.5 (1.47) | 8.74 | 1752.2 |
## Usage:
You can use the model using HuggingFace Transformers library with 2 or more 80GB GPUs (NVIDIA Ampere or newer) with at least 150GB of free disk space to accomodate the download.
This code has been tested on Transformers v4.44.0, torch v2.4.0 and 2 A100 80GB GPUs, but any setup that supports ```meta-llama/Llama-3.1-70B-Instruct``` should support this model as well. If you run into problems, you can consider doing ```pip install -U transformers```.
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "nvidia/Llama-3.1-Nemotron-70B-Instruct-HF"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "How many r in strawberry?"
messages = [{"role": "user", "content": prompt}]
tokenized_message = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True)
response_token_ids = model.generate(tokenized_message['input_ids'].cuda(),attention_mask=tokenized_message['attention_mask'].cuda(), max_new_tokens=4096, pad_token_id = tokenizer.eos_token_id)
generated_tokens =response_token_ids[:, len(tokenized_message['input_ids'][0]):]
generated_text = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
print(generated_text)
# See response at top of model card
```
## Contact
E-Mail: [Zhilin Wang](mailto:[email protected])
## Citation
If you find this model useful, please cite the following works
```bibtex
@misc{wang2024helpsteer2preferencecomplementingratingspreferences,
title={HelpSteer2-Preference: Complementing Ratings with Preferences},
author={Zhilin Wang and Alexander Bukharin and Olivier Delalleau and Daniel Egert and Gerald Shen and Jiaqi Zeng and Oleksii Kuchaiev and Yi Dong},
year={2024},
eprint={2410.01257},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2410.01257},
}
@misc{wang2024helpsteer2,
title={HelpSteer2: Open-source dataset for training top-performing reward models},
author={Zhilin Wang and Yi Dong and Olivier Delalleau and Jiaqi Zeng and Gerald Shen and Daniel Egert and Jimmy J. Zhang and Makesh Narsimhan Sreedhar and Oleksii Kuchaiev},
year={2024},
eprint={2406.08673},
archivePrefix={arXiv},
primaryClass={id='cs.CL' full_name='Computation and Language' is_active=True alt_name='cmp-lg' in_archive='cs' is_general=False description='Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.'}
}
```
## References(s):
* [HelpSteer2-Preference](https://arxiv.org/abs/2410.01257)
* [SteerLM method](https://arxiv.org/abs/2310.05344)
* [HelpSteer](https://arxiv.org/abs/2311.09528)
* [HelpSteer2](https://arxiv.org/abs/2406.08673)
* [Introducing Llama 3.1: Our most capable models to date](https://ai.meta.com/blog/meta-llama-3-1/)
* [Meta's Llama 3.1 Webpage](https://www.llama.com/docs/model-cards-and-prompt-formats/llama3_1)
* [Meta's Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md)
## Model Architecture:
**Architecture Type:** Transformer <br>
**Network Architecture:** Llama 3.1 <br>
## Input:
**Input Type(s):** Text <br>
**Input Format:** String <br>
**Input Parameters:** One Dimensional (1D) <br>
**Other Properties Related to Input:** Max of 128k tokens<br>
## Output:
**Output Type(s):** Text <br>
**Output Format:** String <br>
**Output Parameters:** One Dimensional (1D) <br>
**Other Properties Related to Output:** Max of 4k tokens <br>
## Software Integration:
**Supported Hardware Microarchitecture Compatibility:** <br>
* NVIDIA Ampere <br>
* NVIDIA Hopper <br>
* NVIDIA Turing <br>
**Supported Operating System(s):** Linux <br>
## Model Version:
v1.0
# Training & Evaluation:
## Datasets:
**Data Collection Method by dataset** <br>
* [Hybrid: Human, Synthetic] <br>
**Labeling Method by dataset** <br>
* [Human] <br>
**Link:**
* [HelpSteer2](https://huggingface.co/datasets/nvidia/HelpSteer2)
**Properties (Quantity, Dataset Descriptions, Sensor(s)):** <br>
* 21, 362 prompt-responses built to make more models more aligned with human preference - specifically more helpful, factually-correct, coherent, and customizable based on complexity and verbosity.
* 20, 324 prompt-responses used for training and 1, 038 used for validation.
# Inference:
**Engine:** [Triton](https://developer.nvidia.com/triton-inference-server) <br>
**Test Hardware:** H100, A100 80GB, A100 40GB <br>
## Ethical Considerations:
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their supporting model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/). |
RichardErkhov/as-cle-bert_-_tiny-fungal-llama-gguf | RichardErkhov | 2024-10-16T02:17:16Z | 31 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-16T01:50:40Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
tiny-fungal-llama - GGUF
- Model creator: https://huggingface.co/as-cle-bert/
- Original model: https://huggingface.co/as-cle-bert/tiny-fungal-llama/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [tiny-fungal-llama.Q2_K.gguf](https://huggingface.co/RichardErkhov/as-cle-bert_-_tiny-fungal-llama-gguf/blob/main/tiny-fungal-llama.Q2_K.gguf) | Q2_K | 0.4GB |
| [tiny-fungal-llama.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/as-cle-bert_-_tiny-fungal-llama-gguf/blob/main/tiny-fungal-llama.IQ3_XS.gguf) | IQ3_XS | 0.44GB |
| [tiny-fungal-llama.IQ3_S.gguf](https://huggingface.co/RichardErkhov/as-cle-bert_-_tiny-fungal-llama-gguf/blob/main/tiny-fungal-llama.IQ3_S.gguf) | IQ3_S | 0.47GB |
| [tiny-fungal-llama.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/as-cle-bert_-_tiny-fungal-llama-gguf/blob/main/tiny-fungal-llama.Q3_K_S.gguf) | Q3_K_S | 0.47GB |
| [tiny-fungal-llama.IQ3_M.gguf](https://huggingface.co/RichardErkhov/as-cle-bert_-_tiny-fungal-llama-gguf/blob/main/tiny-fungal-llama.IQ3_M.gguf) | IQ3_M | 0.48GB |
| [tiny-fungal-llama.Q3_K.gguf](https://huggingface.co/RichardErkhov/as-cle-bert_-_tiny-fungal-llama-gguf/blob/main/tiny-fungal-llama.Q3_K.gguf) | Q3_K | 0.51GB |
| [tiny-fungal-llama.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/as-cle-bert_-_tiny-fungal-llama-gguf/blob/main/tiny-fungal-llama.Q3_K_M.gguf) | Q3_K_M | 0.51GB |
| [tiny-fungal-llama.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/as-cle-bert_-_tiny-fungal-llama-gguf/blob/main/tiny-fungal-llama.Q3_K_L.gguf) | Q3_K_L | 0.55GB |
| [tiny-fungal-llama.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/as-cle-bert_-_tiny-fungal-llama-gguf/blob/main/tiny-fungal-llama.IQ4_XS.gguf) | IQ4_XS | 0.57GB |
| [tiny-fungal-llama.Q4_0.gguf](https://huggingface.co/RichardErkhov/as-cle-bert_-_tiny-fungal-llama-gguf/blob/main/tiny-fungal-llama.Q4_0.gguf) | Q4_0 | 0.59GB |
| [tiny-fungal-llama.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/as-cle-bert_-_tiny-fungal-llama-gguf/blob/main/tiny-fungal-llama.IQ4_NL.gguf) | IQ4_NL | 0.6GB |
| [tiny-fungal-llama.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/as-cle-bert_-_tiny-fungal-llama-gguf/blob/main/tiny-fungal-llama.Q4_K_S.gguf) | Q4_K_S | 0.6GB |
| [tiny-fungal-llama.Q4_K.gguf](https://huggingface.co/RichardErkhov/as-cle-bert_-_tiny-fungal-llama-gguf/blob/main/tiny-fungal-llama.Q4_K.gguf) | Q4_K | 0.62GB |
| [tiny-fungal-llama.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/as-cle-bert_-_tiny-fungal-llama-gguf/blob/main/tiny-fungal-llama.Q4_K_M.gguf) | Q4_K_M | 0.62GB |
| [tiny-fungal-llama.Q4_1.gguf](https://huggingface.co/RichardErkhov/as-cle-bert_-_tiny-fungal-llama-gguf/blob/main/tiny-fungal-llama.Q4_1.gguf) | Q4_1 | 0.65GB |
| [tiny-fungal-llama.Q5_0.gguf](https://huggingface.co/RichardErkhov/as-cle-bert_-_tiny-fungal-llama-gguf/blob/main/tiny-fungal-llama.Q5_0.gguf) | Q5_0 | 0.71GB |
| [tiny-fungal-llama.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/as-cle-bert_-_tiny-fungal-llama-gguf/blob/main/tiny-fungal-llama.Q5_K_S.gguf) | Q5_K_S | 0.71GB |
| [tiny-fungal-llama.Q5_K.gguf](https://huggingface.co/RichardErkhov/as-cle-bert_-_tiny-fungal-llama-gguf/blob/main/tiny-fungal-llama.Q5_K.gguf) | Q5_K | 0.73GB |
| [tiny-fungal-llama.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/as-cle-bert_-_tiny-fungal-llama-gguf/blob/main/tiny-fungal-llama.Q5_K_M.gguf) | Q5_K_M | 0.73GB |
| [tiny-fungal-llama.Q5_1.gguf](https://huggingface.co/RichardErkhov/as-cle-bert_-_tiny-fungal-llama-gguf/blob/main/tiny-fungal-llama.Q5_1.gguf) | Q5_1 | 0.77GB |
| [tiny-fungal-llama.Q6_K.gguf](https://huggingface.co/RichardErkhov/as-cle-bert_-_tiny-fungal-llama-gguf/blob/main/tiny-fungal-llama.Q6_K.gguf) | Q6_K | 0.84GB |
| [tiny-fungal-llama.Q8_0.gguf](https://huggingface.co/RichardErkhov/as-cle-bert_-_tiny-fungal-llama-gguf/blob/main/tiny-fungal-llama.Q8_0.gguf) | Q8_0 | 1.09GB |
Original model description:
---
license: apache-2.0
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
tags:
- generated_from_trainer
model-index:
- name: tiny-fungal-llama
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tiny-fungal-llama
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0136
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 27 | 1.7950 |
| No log | 2.0 | 54 | 1.8646 |
| No log | 3.0 | 81 | 2.0136 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.2+cpu
- Datasets 2.18.0
- Tokenizers 0.15.2
|
kristiannordby/text-generation-v1 | kristiannordby | 2024-10-16T02:14:32Z | 203 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-16T02:14:15Z | ---
library_name: transformers
tags: []
---
# 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] |
RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf | RichardErkhov | 2024-10-16T02:13:47Z | 18 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us"
] | null | 2024-10-15T01:48:32Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Llama-3-70B-Special-Tokens-Adjusted - GGUF
- Model creator: https://huggingface.co/astronomer/
- Original model: https://huggingface.co/astronomer/Llama-3-70B-Special-Tokens-Adjusted/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Llama-3-70B-Special-Tokens-Adjusted.Q2_K.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/blob/main/Llama-3-70B-Special-Tokens-Adjusted.Q2_K.gguf) | Q2_K | 24.56GB |
| [Llama-3-70B-Special-Tokens-Adjusted.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/blob/main/Llama-3-70B-Special-Tokens-Adjusted.IQ3_XS.gguf) | IQ3_XS | 27.29GB |
| [Llama-3-70B-Special-Tokens-Adjusted.IQ3_S.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/blob/main/Llama-3-70B-Special-Tokens-Adjusted.IQ3_S.gguf) | IQ3_S | 28.79GB |
| [Llama-3-70B-Special-Tokens-Adjusted.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/blob/main/Llama-3-70B-Special-Tokens-Adjusted.Q3_K_S.gguf) | Q3_K_S | 28.79GB |
| [Llama-3-70B-Special-Tokens-Adjusted.IQ3_M.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/blob/main/Llama-3-70B-Special-Tokens-Adjusted.IQ3_M.gguf) | IQ3_M | 29.74GB |
| [Llama-3-70B-Special-Tokens-Adjusted.Q3_K.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/blob/main/Llama-3-70B-Special-Tokens-Adjusted.Q3_K.gguf) | Q3_K | 31.91GB |
| [Llama-3-70B-Special-Tokens-Adjusted.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/blob/main/Llama-3-70B-Special-Tokens-Adjusted.Q3_K_M.gguf) | Q3_K_M | 31.91GB |
| [Llama-3-70B-Special-Tokens-Adjusted.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/blob/main/Llama-3-70B-Special-Tokens-Adjusted.Q3_K_L.gguf) | Q3_K_L | 34.59GB |
| [Llama-3-70B-Special-Tokens-Adjusted.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/blob/main/Llama-3-70B-Special-Tokens-Adjusted.IQ4_XS.gguf) | IQ4_XS | 35.64GB |
| [Llama-3-70B-Special-Tokens-Adjusted.Q4_0.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/blob/main/Llama-3-70B-Special-Tokens-Adjusted.Q4_0.gguf) | Q4_0 | 37.22GB |
| [Llama-3-70B-Special-Tokens-Adjusted.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/tree/main/) | IQ4_NL | 37.58GB |
| [Llama-3-70B-Special-Tokens-Adjusted.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/tree/main/) | Q4_K_S | 37.58GB |
| [Llama-3-70B-Special-Tokens-Adjusted.Q4_K.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/tree/main/) | Q4_K | 39.6GB |
| [Llama-3-70B-Special-Tokens-Adjusted.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/tree/main/) | Q4_K_M | 39.6GB |
| [Llama-3-70B-Special-Tokens-Adjusted.Q4_1.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/tree/main/) | Q4_1 | 41.27GB |
| [Llama-3-70B-Special-Tokens-Adjusted.Q5_0.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/tree/main/) | Q5_0 | 45.32GB |
| [Llama-3-70B-Special-Tokens-Adjusted.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/tree/main/) | Q5_K_S | 45.32GB |
| [Llama-3-70B-Special-Tokens-Adjusted.Q5_K.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/tree/main/) | Q5_K | 46.52GB |
| [Llama-3-70B-Special-Tokens-Adjusted.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/tree/main/) | Q5_K_M | 46.52GB |
| [Llama-3-70B-Special-Tokens-Adjusted.Q5_1.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/tree/main/) | Q5_1 | 49.36GB |
| [Llama-3-70B-Special-Tokens-Adjusted.Q6_K.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/tree/main/) | Q6_K | 53.91GB |
| [Llama-3-70B-Special-Tokens-Adjusted.Q8_0.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/tree/main/) | Q8_0 | 69.83GB |
Original model description:
---
base_model: meta-llama/Meta-Llama-3-70B
inference: false
model_creator: astronomer-io
model_name: Meta-Llama-3-70B
model_type: llama
pipeline_tag: text-generation
license: other
license_name: llama-3
license_link: https://huggingface.co/meta-llama/Meta-Llama-3-70B/blob/main/README.md
tags:
- llama
- llama-3
- facebook
- meta
- astronomer
- pretrained
- finetuned
- autotrain_compatible
- endpoints_compatible
---
<!-- header start -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://www.astronomer.io/logo/astronomer-logo-RGB-standard-1200px.png" alt="Astronomer" style="width: 60%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="margin-top: 1.0em; margin-bottom: 1.0em;"></div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">This model is generously created and made open source by <a href="https://astronomer.io">Astronomer</a>.</p></div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">Astronomer is the de facto company for <a href="https://airflow.apache.org/">Apache Airflow</a>, the most trusted open-source framework for data orchestration and MLOps.</p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Llama-3-70B-Special-Tokens-Adjusted
- Ideal and stable Llama-3-70B for fine-tuning.
- Original Model creator: [Meta](https://huggingface.co/meta-llama)
- Original model: [meta-llama/Meta-Llama-3-70B](https://huggingface.co/meta-llama/Meta-Llama-3-70B)
- The usage of this model must abide by the [Llama 3 Community License](https://huggingface.co/meta-llama/Meta-Llama-3-70B/blob/main/LICENSE).
- Built with Meta Llama 3
- Created by [David Xue](https://www.linkedin.com/in/david-xue-uva/) from [Astronomer](https://astronomer.io)
## Description
This is the exact same model ([meta-llama/Meta-Llama-3-70B](https://huggingface.co/meta-llama/Meta-Llama-3-70B)) with the weights for the input and output embeddings from lm head and embedding matrix adjusted using the mean of the trained tokens for certain tokens that were untrained, which caused widespread issues for people attempting to fine-tune this base model with either adding their own tokens or using existing special tokens.
## Why We Made This Model
The Llama 3 base (non-instruct) model, while powerful, came with a significant oversight that some special tokens for instruction following within its architecture were left untrained, potentially derailing further fine-tuning processes. This was first noted by [Daniel Han on X](https://twitter.com/danielhanchen/status/1781395882925343058), highlighting a critical but fixable flaw in a widely used model.
<img src="https://cdn-uploads.huggingface.co/production/uploads/655ad0f8727df37c77a09cb9/1U2rRrx60p1pNeeAZw8Rd.png" alt="graph" width="400"/>
The primary goal of releasing a patched version of this model was to address this issue so that the community can utilize the Llama 3 model without facing training instabilities, such as sudden gradient explosions or `NaN` gradients, or having to go through complicated processes to fix the model themselves before fine-tuning.
Note: specifically for the 70B model, the untrained special tokens did not have all zero values for the embedding weights. So the significance of this problem may not be as severe as it is on the base 8B model. This model was made anyway by the request of the community, though in theory directly fine-tuning should be ok.
## Details of the Adjustment
The [meta-llama/Meta-Llama-3-70B](https://huggingface.co/meta-llama/Meta-Llama-3-70B) model was pulled directly from HuggingFace and loaded using transformers. Then, the input embedding and output embedding values are retrieved using `model.get_input_embeddings().weight.data` and `model.get_output_embeddings().weight.data`. These 2 matrics are identical in shape, with each row representing a token id, and each column representing an embedding feature.
The special (untrained & problematic) tokens can be found by locating the rows where the entire row of the embedding values are ~~~all zeros~~~ less than 9e-7 (for the 70B model, no row had all zeros, so thresholding using 9e-7 was done to fine under-trained tokens), which imply they were not trained during the pretraining phase of the model from Meta. Such untrained tokens could lead to heavy computational issues, like gradient explosions or `NaN` gradients, during downstream fine-tuning on specific tasks.
<details>
<summary>See here for a list of the tokens we found that has fit the "untrained" profile described:</summary>
['À',
'Á',
'õ',
'ö',
'÷',
'ø',
'ù',
'ú',
'û',
'ü',
'ý',
'þ',
'ÿ',
'">ččĊ',
';čččĊ',
'ĉTokenNameIdentifier',
'ĠForCanBeConverted',
'ĠForCanBeConvertedToF',
'PostalCodesNL',
'$PostalCodesNL',
'useRalative',
'Û±Û',
'аÑĢакÑĤ',
'аÑĤиÑģÑı',
'иÑĤиÑģÑı',
'ávajÃŃcÃŃ',
'Ä°TESÄ°',
'илакÑĤи',
'илаÑģÑı',
'ÑĭÑŁN',
'ÐİÑĭÑŁN',
'ılmaktadır',
'ÐİÑĭÑŁNÐİÑĭÑŁN',
'ıldıģında',
'<|reserved_special_token_0|>',
'<|reserved_special_token_1|>',
'<|reserved_special_token_2|>',
'<|reserved_special_token_3|>',
'<|start_header_id|>',
'<|end_header_id|>',
'<|reserved_special_token_4|>',
'<|eot_id|>',
'<|reserved_special_token_5|>',
'<|reserved_special_token_6|>',
'<|reserved_special_token_7|>',
'<|reserved_special_token_8|>',
'<|reserved_special_token_9|>',
'<|reserved_special_token_10|>',
'<|reserved_special_token_11|>',
'<|reserved_special_token_12|>',
'<|reserved_special_token_13|>',
'<|reserved_special_token_14|>',
'<|reserved_special_token_15|>',
'<|reserved_special_token_16|>',
'<|reserved_special_token_17|>',
'<|reserved_special_token_18|>',
'<|reserved_special_token_19|>',
'<|reserved_special_token_20|>',
'<|reserved_special_token_21|>',
'<|reserved_special_token_22|>',
'<|reserved_special_token_23|>',
'<|reserved_special_token_24|>',
'<|reserved_special_token_25|>',
'<|reserved_special_token_26|>',
'<|reserved_special_token_27|>',
'<|reserved_special_token_28|>',
'<|reserved_special_token_29|>',
'<|reserved_special_token_30|>',
'<|reserved_special_token_31|>',
'<|reserved_special_token_32|>',
'<|reserved_special_token_33|>',
'<|reserved_special_token_34|>',
'<|reserved_special_token_35|>',
'<|reserved_special_token_36|>',
'<|reserved_special_token_37|>',
'<|reserved_special_token_38|>',
'<|reserved_special_token_39|>',
'<|reserved_special_token_40|>',
'<|reserved_special_token_41|>',
'<|reserved_special_token_42|>',
'<|reserved_special_token_43|>',
'<|reserved_special_token_44|>',
'<|reserved_special_token_45|>',
'<|reserved_special_token_46|>',
'<|reserved_special_token_47|>',
'<|reserved_special_token_48|>',
'<|reserved_special_token_49|>',
'<|reserved_special_token_50|>',
'<|reserved_special_token_51|>',
'<|reserved_special_token_52|>',
'<|reserved_special_token_53|>',
'<|reserved_special_token_54|>',
'<|reserved_special_token_55|>',
'<|reserved_special_token_56|>',
'<|reserved_special_token_57|>',
'<|reserved_special_token_58|>',
'<|reserved_special_token_59|>',
'<|reserved_special_token_60|>',
'<|reserved_special_token_61|>',
'<|reserved_special_token_62|>',
'<|reserved_special_token_63|>',
'<|reserved_special_token_64|>',
'<|reserved_special_token_65|>',
'<|reserved_special_token_66|>',
'<|reserved_special_token_67|>',
'<|reserved_special_token_68|>',
'<|reserved_special_token_69|>',
'<|reserved_special_token_70|>',
'<|reserved_special_token_71|>',
'<|reserved_special_token_72|>',
'<|reserved_special_token_73|>',
'<|reserved_special_token_74|>',
'<|reserved_special_token_75|>',
'<|reserved_special_token_76|>',
'<|reserved_special_token_77|>',
'<|reserved_special_token_78|>',
'<|reserved_special_token_79|>',
'<|reserved_special_token_80|>',
'<|reserved_special_token_81|>',
'<|reserved_special_token_82|>',
'<|reserved_special_token_83|>',
'<|reserved_special_token_84|>',
'<|reserved_special_token_85|>',
'<|reserved_special_token_86|>',
'<|reserved_special_token_87|>',
'<|reserved_special_token_88|>',
'<|reserved_special_token_89|>',
'<|reserved_special_token_90|>',
'<|reserved_special_token_91|>',
'<|reserved_special_token_92|>',
'<|reserved_special_token_93|>',
'<|reserved_special_token_94|>',
'<|reserved_special_token_95|>',
'<|reserved_special_token_96|>',
'<|reserved_special_token_97|>',
'<|reserved_special_token_98|>',
'<|reserved_special_token_99|>',
'<|reserved_special_token_100|>',
'<|reserved_special_token_101|>',
'<|reserved_special_token_102|>',
'<|reserved_special_token_103|>',
'<|reserved_special_token_104|>',
'<|reserved_special_token_105|>',
'<|reserved_special_token_106|>',
'<|reserved_special_token_107|>',
'<|reserved_special_token_108|>',
'<|reserved_special_token_109|>',
'<|reserved_special_token_110|>',
'<|reserved_special_token_111|>',
'<|reserved_special_token_112|>',
'<|reserved_special_token_113|>',
'<|reserved_special_token_114|>',
'<|reserved_special_token_115|>',
'<|reserved_special_token_116|>',
'<|reserved_special_token_117|>',
'<|reserved_special_token_118|>',
'<|reserved_special_token_119|>',
'<|reserved_special_token_120|>',
'<|reserved_special_token_121|>',
'<|reserved_special_token_122|>',
'<|reserved_special_token_123|>',
'<|reserved_special_token_124|>',
'<|reserved_special_token_125|>',
'<|reserved_special_token_126|>',
'<|reserved_special_token_127|>',
'<|reserved_special_token_128|>',
'<|reserved_special_token_129|>',
'<|reserved_special_token_130|>',
'<|reserved_special_token_131|>',
'<|reserved_special_token_132|>',
'<|reserved_special_token_133|>',
'<|reserved_special_token_134|>',
'<|reserved_special_token_135|>',
'<|reserved_special_token_136|>',
'<|reserved_special_token_137|>',
'<|reserved_special_token_138|>',
'<|reserved_special_token_139|>',
'<|reserved_special_token_140|>',
'<|reserved_special_token_141|>',
'<|reserved_special_token_142|>',
'<|reserved_special_token_143|>',
'<|reserved_special_token_144|>',
'<|reserved_special_token_145|>',
'<|reserved_special_token_146|>',
'<|reserved_special_token_147|>',
'<|reserved_special_token_148|>',
'<|reserved_special_token_149|>',
'<|reserved_special_token_150|>',
'<|reserved_special_token_151|>',
'<|reserved_special_token_152|>',
'<|reserved_special_token_153|>',
'<|reserved_special_token_154|>',
'<|reserved_special_token_155|>',
'<|reserved_special_token_156|>',
'<|reserved_special_token_157|>',
'<|reserved_special_token_158|>',
'<|reserved_special_token_159|>',
'<|reserved_special_token_160|>',
'<|reserved_special_token_161|>',
'<|reserved_special_token_162|>',
'<|reserved_special_token_163|>',
'<|reserved_special_token_164|>',
'<|reserved_special_token_165|>',
'<|reserved_special_token_166|>',
'<|reserved_special_token_167|>',
'<|reserved_special_token_168|>',
'<|reserved_special_token_169|>',
'<|reserved_special_token_170|>',
'<|reserved_special_token_171|>',
'<|reserved_special_token_172|>',
'<|reserved_special_token_173|>',
'<|reserved_special_token_174|>',
'<|reserved_special_token_175|>',
'<|reserved_special_token_176|>',
'<|reserved_special_token_177|>',
'<|reserved_special_token_178|>',
'<|reserved_special_token_179|>',
'<|reserved_special_token_180|>',
'<|reserved_special_token_181|>',
'<|reserved_special_token_182|>',
'<|reserved_special_token_183|>',
'<|reserved_special_token_184|>',
'<|reserved_special_token_185|>',
'<|reserved_special_token_186|>',
'<|reserved_special_token_187|>',
'<|reserved_special_token_188|>',
'<|reserved_special_token_189|>',
'<|reserved_special_token_190|>',
'<|reserved_special_token_191|>',
'<|reserved_special_token_192|>',
'<|reserved_special_token_193|>',
'<|reserved_special_token_194|>',
'<|reserved_special_token_195|>',
'<|reserved_special_token_196|>',
'<|reserved_special_token_197|>',
'<|reserved_special_token_198|>',
'<|reserved_special_token_199|>',
'<|reserved_special_token_200|>',
'<|reserved_special_token_201|>',
'<|reserved_special_token_202|>',
'<|reserved_special_token_203|>',
'<|reserved_special_token_204|>',
'<|reserved_special_token_205|>',
'<|reserved_special_token_206|>',
'<|reserved_special_token_207|>',
'<|reserved_special_token_208|>',
'<|reserved_special_token_209|>',
'<|reserved_special_token_210|>',
'<|reserved_special_token_211|>',
'<|reserved_special_token_212|>',
'<|reserved_special_token_213|>',
'<|reserved_special_token_214|>',
'<|reserved_special_token_215|>',
'<|reserved_special_token_216|>',
'<|reserved_special_token_217|>',
'<|reserved_special_token_218|>',
'<|reserved_special_token_219|>',
'<|reserved_special_token_220|>',
'<|reserved_special_token_221|>',
'<|reserved_special_token_222|>',
'<|reserved_special_token_223|>',
'<|reserved_special_token_224|>',
'<|reserved_special_token_225|>',
'<|reserved_special_token_226|>',
'<|reserved_special_token_227|>',
'<|reserved_special_token_228|>',
'<|reserved_special_token_229|>',
'<|reserved_special_token_230|>',
'<|reserved_special_token_231|>',
'<|reserved_special_token_232|>',
'<|reserved_special_token_233|>',
'<|reserved_special_token_234|>',
'<|reserved_special_token_235|>',
'<|reserved_special_token_236|>',
'<|reserved_special_token_237|>',
'<|reserved_special_token_238|>',
'<|reserved_special_token_239|>',
'<|reserved_special_token_240|>',
'<|reserved_special_token_241|>',
'<|reserved_special_token_242|>',
'<|reserved_special_token_243|>',
'<|reserved_special_token_244|>',
'<|reserved_special_token_245|>',
'<|reserved_special_token_246|>',
'<|reserved_special_token_247|>',
'<|reserved_special_token_248|>',
'<|reserved_special_token_249|>',
'<|reserved_special_token_250|>']
</details>
Once these untrained tokens are identified, the average of trained tokens can be calculated by using the sums of embedding values of trained tokens for each feature/column and divided by the number of trained. This is done for both input and output matrices.
Lastly, the problematic token's rows in the 2 embedding matrics are set to the computed mean, thus completing the adjustment.
## Contributors
- [David Xue](https://www.linkedin.com/in/david-xue-uva/), Machine Learning Engineer from [Astronomer](https://astronomer.io)
|
Gnider/roberta_rat_dist_test | Gnider | 2024-10-16T02:06:03Z | 107 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-16T02:04:02Z | ---
library_name: transformers
tags: []
---
# 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. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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[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]
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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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## 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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## 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 Authors [optional]
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## Model Card Contact
[More Information Needed] |
openerotica/writing-roleplay-20k-context-nemo-12b-v1.0-gguf | openerotica | 2024-10-16T02:05:14Z | 3,300 | 18 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:openerotica/writing-roleplay-20k-context-nemo-12b-v1.0",
"base_model:quantized:openerotica/writing-roleplay-20k-context-nemo-12b-v1.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-13T02:57:18Z | ---
base_model: openerotica/writing-roleplay-20k-context-nemo-12b-v1.0
tags:
- llama-cpp
- gguf-my-repo
---
If you like this models, consider joining my discord to give feedback: https://discord.gg/QXdn8hWSkY
# basiliskinstitute/writing-roleplay-20k-context-nemo-12b-v1.0-Q4_K_M-GGUF
This model was converted to GGUF format from [`openerotica/writing-roleplay-20k-context-nemo-12b-v1.0`](https://huggingface.co/openerotica/writing-roleplay-20k-context-nemo-12b-v1.0) 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/openerotica/writing-roleplay-20k-context-nemo-12b-v1.0) 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 basiliskinstitute/writing-roleplay-20k-context-nemo-12b-v1.0-Q4_K_M-GGUF --hf-file writing-roleplay-20k-context-nemo-12b-v1.0-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo basiliskinstitute/writing-roleplay-20k-context-nemo-12b-v1.0-Q4_K_M-GGUF --hf-file writing-roleplay-20k-context-nemo-12b-v1.0-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 basiliskinstitute/writing-roleplay-20k-context-nemo-12b-v1.0-Q4_K_M-GGUF --hf-file writing-roleplay-20k-context-nemo-12b-v1.0-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo basiliskinstitute/writing-roleplay-20k-context-nemo-12b-v1.0-Q4_K_M-GGUF --hf-file writing-roleplay-20k-context-nemo-12b-v1.0-q4_k_m.gguf -c 2048
```
|
Purz/edm-festival-stage | Purz | 2024-10-16T02:02:05Z | 35 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"migrated",
"concept",
"festival",
"edm",
"stage",
"concert",
"purz",
"flux1.d",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2024-10-16T02:02:02Z | ---
license: other
license_name: bespoke-lora-trained-license
license_link: https://multimodal.art/civitai-licenses?allowNoCredit=True&allowCommercialUse=Image&allowDerivatives=True&allowDifferentLicense=True
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
- migrated
- concept
- festival
- edm
- stage
- concert
- purz
- flux1.d
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: 3dm_f35t1v47
widget:
- text: ' '
output:
url: >-
34690293.jpeg
- text: ' '
output:
url: >-
34690295.jpeg
- text: ' '
output:
url: >-
34690299.jpeg
- text: '3dm_f35t1v47, illuminated by colorful lights and fireworks in the sky. the stage is surrounded by trees and buildings'
output:
url: >-
34690642.jpeg
- text: '3dm_f35t1v47, a large crowd of people gathered at a music festival, the stage is shaped in the word "CIVITAI", night time'
output:
url: >-
34690660.jpeg
- text: '3dm_f35t1v47, illuminated by colorful lights and fireworks in the sky. the stage is surrounded by trees and buildings'
output:
url: >-
34690683.jpeg
---
# EDM Festival Stage
<Gallery />
## Model description
<p>EDM Festival Stage - LoRA (Flux.1 D)</p><p></p><p>Trained on photographs from EDM Festival Stages.</p><p></p><p>"3dm_f35t1v47, a large crowd of people gathered at a music festival, the stage is shaped in the word "PURZ", night time"</p><p></p><p>Purz</p><p>Website: <a target="_blank" rel="ugc" href="https://www.purz.xyz/">https://www.purz.xyz/</a><br />Creative Exploration /w Purz: <a target="_blank" rel="ugc" href="https://www.youtube.com/@PurzBeats">https://www.youtube.com/@PurzBeats</a><br />Patreon: <a target="_blank" rel="ugc" href="https://www.patreon.com/Purz">https://www.patreon.com/Purz</a><br />Twitter/X: <a target="_blank" rel="ugc" href="https://x.com/PurzBeats">https://x.com/PurzBeats</a><br />Instagram: <a target="_blank" rel="ugc" href="https://www.instagram.com/purzbeats/">https://www.instagram.com/purzbeats/</a></p>
## Trigger words
You should use `3dm_f35t1v47` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/Purz/edm-festival-stage/tree/main) them in the Files & versions tab.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to(device)
pipeline.load_lora_weights('Purz/edm-festival-stage', weight_name='purz-3dm_f35t1v47-edm-festival-stage.safetensors')
image = pipeline('3dm_f35t1v47, illuminated by colorful lights and fireworks in the sky. the stage is surrounded by trees and buildings').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
|
joshnader/Mistral-Nemo-Instruct-2407-Q6_K-GGUF | joshnader | 2024-10-16T02:00:07Z | 5 | 0 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"fr",
"de",
"es",
"it",
"pt",
"ru",
"zh",
"ja",
"base_model:mistralai/Mistral-Nemo-Instruct-2407",
"base_model:quantized:mistralai/Mistral-Nemo-Instruct-2407",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-16T01:59:25Z | ---
base_model: mistralai/Mistral-Nemo-Instruct-2407
language:
- en
- fr
- de
- es
- it
- pt
- ru
- zh
- ja
license: apache-2.0
tags:
- llama-cpp
- gguf-my-repo
extra_gated_description: If you want to learn more about how we process your personal
data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
---
# joshnader/Mistral-Nemo-Instruct-2407-Q6_K-GGUF
This model was converted to GGUF format from [`mistralai/Mistral-Nemo-Instruct-2407`](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407) 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-Nemo-Instruct-2407) 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 joshnader/Mistral-Nemo-Instruct-2407-Q6_K-GGUF --hf-file mistral-nemo-instruct-2407-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo joshnader/Mistral-Nemo-Instruct-2407-Q6_K-GGUF --hf-file mistral-nemo-instruct-2407-q6_k.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 joshnader/Mistral-Nemo-Instruct-2407-Q6_K-GGUF --hf-file mistral-nemo-instruct-2407-q6_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo joshnader/Mistral-Nemo-Instruct-2407-Q6_K-GGUF --hf-file mistral-nemo-instruct-2407-q6_k.gguf -c 2048
```
|
RichardErkhov/elvinmarkmv_-_tinyllama-15M-alpaca-finetuned-gguf | RichardErkhov | 2024-10-16T01:57:28Z | 7 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us"
] | null | 2024-10-16T01:56:29Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
tinyllama-15M-alpaca-finetuned - GGUF
- Model creator: https://huggingface.co/elvinmarkmv/
- Original model: https://huggingface.co/elvinmarkmv/tinyllama-15M-alpaca-finetuned/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [tinyllama-15M-alpaca-finetuned.Q2_K.gguf](https://huggingface.co/RichardErkhov/elvinmarkmv_-_tinyllama-15M-alpaca-finetuned-gguf/blob/main/tinyllama-15M-alpaca-finetuned.Q2_K.gguf) | Q2_K | 0.01GB |
| [tinyllama-15M-alpaca-finetuned.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/elvinmarkmv_-_tinyllama-15M-alpaca-finetuned-gguf/blob/main/tinyllama-15M-alpaca-finetuned.IQ3_XS.gguf) | IQ3_XS | 0.01GB |
| [tinyllama-15M-alpaca-finetuned.IQ3_S.gguf](https://huggingface.co/RichardErkhov/elvinmarkmv_-_tinyllama-15M-alpaca-finetuned-gguf/blob/main/tinyllama-15M-alpaca-finetuned.IQ3_S.gguf) | IQ3_S | 0.01GB |
| [tinyllama-15M-alpaca-finetuned.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/elvinmarkmv_-_tinyllama-15M-alpaca-finetuned-gguf/blob/main/tinyllama-15M-alpaca-finetuned.Q3_K_S.gguf) | Q3_K_S | 0.01GB |
| [tinyllama-15M-alpaca-finetuned.IQ3_M.gguf](https://huggingface.co/RichardErkhov/elvinmarkmv_-_tinyllama-15M-alpaca-finetuned-gguf/blob/main/tinyllama-15M-alpaca-finetuned.IQ3_M.gguf) | IQ3_M | 0.01GB |
| [tinyllama-15M-alpaca-finetuned.Q3_K.gguf](https://huggingface.co/RichardErkhov/elvinmarkmv_-_tinyllama-15M-alpaca-finetuned-gguf/blob/main/tinyllama-15M-alpaca-finetuned.Q3_K.gguf) | Q3_K | 0.01GB |
| [tinyllama-15M-alpaca-finetuned.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/elvinmarkmv_-_tinyllama-15M-alpaca-finetuned-gguf/blob/main/tinyllama-15M-alpaca-finetuned.Q3_K_M.gguf) | Q3_K_M | 0.01GB |
| [tinyllama-15M-alpaca-finetuned.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/elvinmarkmv_-_tinyllama-15M-alpaca-finetuned-gguf/blob/main/tinyllama-15M-alpaca-finetuned.Q3_K_L.gguf) | Q3_K_L | 0.01GB |
| [tinyllama-15M-alpaca-finetuned.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/elvinmarkmv_-_tinyllama-15M-alpaca-finetuned-gguf/blob/main/tinyllama-15M-alpaca-finetuned.IQ4_XS.gguf) | IQ4_XS | 0.01GB |
| [tinyllama-15M-alpaca-finetuned.Q4_0.gguf](https://huggingface.co/RichardErkhov/elvinmarkmv_-_tinyllama-15M-alpaca-finetuned-gguf/blob/main/tinyllama-15M-alpaca-finetuned.Q4_0.gguf) | Q4_0 | 0.01GB |
| [tinyllama-15M-alpaca-finetuned.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/elvinmarkmv_-_tinyllama-15M-alpaca-finetuned-gguf/blob/main/tinyllama-15M-alpaca-finetuned.IQ4_NL.gguf) | IQ4_NL | 0.01GB |
| [tinyllama-15M-alpaca-finetuned.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/elvinmarkmv_-_tinyllama-15M-alpaca-finetuned-gguf/blob/main/tinyllama-15M-alpaca-finetuned.Q4_K_S.gguf) | Q4_K_S | 0.01GB |
| [tinyllama-15M-alpaca-finetuned.Q4_K.gguf](https://huggingface.co/RichardErkhov/elvinmarkmv_-_tinyllama-15M-alpaca-finetuned-gguf/blob/main/tinyllama-15M-alpaca-finetuned.Q4_K.gguf) | Q4_K | 0.01GB |
| [tinyllama-15M-alpaca-finetuned.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/elvinmarkmv_-_tinyllama-15M-alpaca-finetuned-gguf/blob/main/tinyllama-15M-alpaca-finetuned.Q4_K_M.gguf) | Q4_K_M | 0.01GB |
| [tinyllama-15M-alpaca-finetuned.Q4_1.gguf](https://huggingface.co/RichardErkhov/elvinmarkmv_-_tinyllama-15M-alpaca-finetuned-gguf/blob/main/tinyllama-15M-alpaca-finetuned.Q4_1.gguf) | Q4_1 | 0.01GB |
| [tinyllama-15M-alpaca-finetuned.Q5_0.gguf](https://huggingface.co/RichardErkhov/elvinmarkmv_-_tinyllama-15M-alpaca-finetuned-gguf/blob/main/tinyllama-15M-alpaca-finetuned.Q5_0.gguf) | Q5_0 | 0.01GB |
| [tinyllama-15M-alpaca-finetuned.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/elvinmarkmv_-_tinyllama-15M-alpaca-finetuned-gguf/blob/main/tinyllama-15M-alpaca-finetuned.Q5_K_S.gguf) | Q5_K_S | 0.01GB |
| [tinyllama-15M-alpaca-finetuned.Q5_K.gguf](https://huggingface.co/RichardErkhov/elvinmarkmv_-_tinyllama-15M-alpaca-finetuned-gguf/blob/main/tinyllama-15M-alpaca-finetuned.Q5_K.gguf) | Q5_K | 0.01GB |
| [tinyllama-15M-alpaca-finetuned.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/elvinmarkmv_-_tinyllama-15M-alpaca-finetuned-gguf/blob/main/tinyllama-15M-alpaca-finetuned.Q5_K_M.gguf) | Q5_K_M | 0.01GB |
| [tinyllama-15M-alpaca-finetuned.Q5_1.gguf](https://huggingface.co/RichardErkhov/elvinmarkmv_-_tinyllama-15M-alpaca-finetuned-gguf/blob/main/tinyllama-15M-alpaca-finetuned.Q5_1.gguf) | Q5_1 | 0.01GB |
| [tinyllama-15M-alpaca-finetuned.Q6_K.gguf](https://huggingface.co/RichardErkhov/elvinmarkmv_-_tinyllama-15M-alpaca-finetuned-gguf/blob/main/tinyllama-15M-alpaca-finetuned.Q6_K.gguf) | Q6_K | 0.02GB |
| [tinyllama-15M-alpaca-finetuned.Q8_0.gguf](https://huggingface.co/RichardErkhov/elvinmarkmv_-_tinyllama-15M-alpaca-finetuned-gguf/blob/main/tinyllama-15M-alpaca-finetuned.Q8_0.gguf) | Q8_0 | 0.02GB |
Original model description:
---
datasets:
- tatsu-lab/alpaca
language:
- en
base_model:
- nickypro/tinyllama-15M
pipeline_tag: text-generation
library_name: transformers
metrics:
- accuracy
---
Loss: 2.371992
|
RichardErkhov/devlocalhost_-_hi-tinylama-f16-3e-gguf | RichardErkhov | 2024-10-16T01:44:14Z | 10 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us"
] | null | 2024-10-16T01:16:13Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
hi-tinylama-f16-3e - GGUF
- Model creator: https://huggingface.co/devlocalhost/
- Original model: https://huggingface.co/devlocalhost/hi-tinylama-f16-3e/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [hi-tinylama-f16-3e.Q2_K.gguf](https://huggingface.co/RichardErkhov/devlocalhost_-_hi-tinylama-f16-3e-gguf/blob/main/hi-tinylama-f16-3e.Q2_K.gguf) | Q2_K | 0.4GB |
| [hi-tinylama-f16-3e.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/devlocalhost_-_hi-tinylama-f16-3e-gguf/blob/main/hi-tinylama-f16-3e.IQ3_XS.gguf) | IQ3_XS | 0.44GB |
| [hi-tinylama-f16-3e.IQ3_S.gguf](https://huggingface.co/RichardErkhov/devlocalhost_-_hi-tinylama-f16-3e-gguf/blob/main/hi-tinylama-f16-3e.IQ3_S.gguf) | IQ3_S | 0.47GB |
| [hi-tinylama-f16-3e.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/devlocalhost_-_hi-tinylama-f16-3e-gguf/blob/main/hi-tinylama-f16-3e.Q3_K_S.gguf) | Q3_K_S | 0.47GB |
| [hi-tinylama-f16-3e.IQ3_M.gguf](https://huggingface.co/RichardErkhov/devlocalhost_-_hi-tinylama-f16-3e-gguf/blob/main/hi-tinylama-f16-3e.IQ3_M.gguf) | IQ3_M | 0.48GB |
| [hi-tinylama-f16-3e.Q3_K.gguf](https://huggingface.co/RichardErkhov/devlocalhost_-_hi-tinylama-f16-3e-gguf/blob/main/hi-tinylama-f16-3e.Q3_K.gguf) | Q3_K | 0.51GB |
| [hi-tinylama-f16-3e.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/devlocalhost_-_hi-tinylama-f16-3e-gguf/blob/main/hi-tinylama-f16-3e.Q3_K_M.gguf) | Q3_K_M | 0.51GB |
| [hi-tinylama-f16-3e.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/devlocalhost_-_hi-tinylama-f16-3e-gguf/blob/main/hi-tinylama-f16-3e.Q3_K_L.gguf) | Q3_K_L | 0.55GB |
| [hi-tinylama-f16-3e.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/devlocalhost_-_hi-tinylama-f16-3e-gguf/blob/main/hi-tinylama-f16-3e.IQ4_XS.gguf) | IQ4_XS | 0.57GB |
| [hi-tinylama-f16-3e.Q4_0.gguf](https://huggingface.co/RichardErkhov/devlocalhost_-_hi-tinylama-f16-3e-gguf/blob/main/hi-tinylama-f16-3e.Q4_0.gguf) | Q4_0 | 0.59GB |
| [hi-tinylama-f16-3e.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/devlocalhost_-_hi-tinylama-f16-3e-gguf/blob/main/hi-tinylama-f16-3e.IQ4_NL.gguf) | IQ4_NL | 0.6GB |
| [hi-tinylama-f16-3e.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/devlocalhost_-_hi-tinylama-f16-3e-gguf/blob/main/hi-tinylama-f16-3e.Q4_K_S.gguf) | Q4_K_S | 0.6GB |
| [hi-tinylama-f16-3e.Q4_K.gguf](https://huggingface.co/RichardErkhov/devlocalhost_-_hi-tinylama-f16-3e-gguf/blob/main/hi-tinylama-f16-3e.Q4_K.gguf) | Q4_K | 0.62GB |
| [hi-tinylama-f16-3e.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/devlocalhost_-_hi-tinylama-f16-3e-gguf/blob/main/hi-tinylama-f16-3e.Q4_K_M.gguf) | Q4_K_M | 0.62GB |
| [hi-tinylama-f16-3e.Q4_1.gguf](https://huggingface.co/RichardErkhov/devlocalhost_-_hi-tinylama-f16-3e-gguf/blob/main/hi-tinylama-f16-3e.Q4_1.gguf) | Q4_1 | 0.65GB |
| [hi-tinylama-f16-3e.Q5_0.gguf](https://huggingface.co/RichardErkhov/devlocalhost_-_hi-tinylama-f16-3e-gguf/blob/main/hi-tinylama-f16-3e.Q5_0.gguf) | Q5_0 | 0.71GB |
| [hi-tinylama-f16-3e.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/devlocalhost_-_hi-tinylama-f16-3e-gguf/blob/main/hi-tinylama-f16-3e.Q5_K_S.gguf) | Q5_K_S | 0.71GB |
| [hi-tinylama-f16-3e.Q5_K.gguf](https://huggingface.co/RichardErkhov/devlocalhost_-_hi-tinylama-f16-3e-gguf/blob/main/hi-tinylama-f16-3e.Q5_K.gguf) | Q5_K | 0.73GB |
| [hi-tinylama-f16-3e.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/devlocalhost_-_hi-tinylama-f16-3e-gguf/blob/main/hi-tinylama-f16-3e.Q5_K_M.gguf) | Q5_K_M | 0.73GB |
| [hi-tinylama-f16-3e.Q5_1.gguf](https://huggingface.co/RichardErkhov/devlocalhost_-_hi-tinylama-f16-3e-gguf/blob/main/hi-tinylama-f16-3e.Q5_1.gguf) | Q5_1 | 0.77GB |
| [hi-tinylama-f16-3e.Q6_K.gguf](https://huggingface.co/RichardErkhov/devlocalhost_-_hi-tinylama-f16-3e-gguf/blob/main/hi-tinylama-f16-3e.Q6_K.gguf) | Q6_K | 0.84GB |
| [hi-tinylama-f16-3e.Q8_0.gguf](https://huggingface.co/RichardErkhov/devlocalhost_-_hi-tinylama-f16-3e-gguf/blob/main/hi-tinylama-f16-3e.Q8_0.gguf) | Q8_0 | 1.09GB |
Original model description:
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- llama
- trl
widget:
- text: "### Instruction:
आपका नाम क्या है?
### Input:
### Response:"
example_title: "what is your name?"
base_model: unsloth/tinyllama-bnb-4bit
---
# Uploaded model
- **Developed by:** devlocalhost
- **License:** apache-2.0
- **Finetuned from model :** unsloth/tinyllama-bnb-4bit
|
RichardErkhov/yifangong_-_tinyllama_random-gguf | RichardErkhov | 2024-10-16T01:42:57Z | 5 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-16T01:13:13Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
tinyllama_random - GGUF
- Model creator: https://huggingface.co/yifangong/
- Original model: https://huggingface.co/yifangong/tinyllama_random/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [tinyllama_random.Q2_K.gguf](https://huggingface.co/RichardErkhov/yifangong_-_tinyllama_random-gguf/blob/main/tinyllama_random.Q2_K.gguf) | Q2_K | 0.4GB |
| [tinyllama_random.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/yifangong_-_tinyllama_random-gguf/blob/main/tinyllama_random.IQ3_XS.gguf) | IQ3_XS | 0.44GB |
| [tinyllama_random.IQ3_S.gguf](https://huggingface.co/RichardErkhov/yifangong_-_tinyllama_random-gguf/blob/main/tinyllama_random.IQ3_S.gguf) | IQ3_S | 0.47GB |
| [tinyllama_random.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/yifangong_-_tinyllama_random-gguf/blob/main/tinyllama_random.Q3_K_S.gguf) | Q3_K_S | 0.47GB |
| [tinyllama_random.IQ3_M.gguf](https://huggingface.co/RichardErkhov/yifangong_-_tinyllama_random-gguf/blob/main/tinyllama_random.IQ3_M.gguf) | IQ3_M | 0.48GB |
| [tinyllama_random.Q3_K.gguf](https://huggingface.co/RichardErkhov/yifangong_-_tinyllama_random-gguf/blob/main/tinyllama_random.Q3_K.gguf) | Q3_K | 0.51GB |
| [tinyllama_random.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/yifangong_-_tinyllama_random-gguf/blob/main/tinyllama_random.Q3_K_M.gguf) | Q3_K_M | 0.51GB |
| [tinyllama_random.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/yifangong_-_tinyllama_random-gguf/blob/main/tinyllama_random.Q3_K_L.gguf) | Q3_K_L | 0.55GB |
| [tinyllama_random.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/yifangong_-_tinyllama_random-gguf/blob/main/tinyllama_random.IQ4_XS.gguf) | IQ4_XS | 0.57GB |
| [tinyllama_random.Q4_0.gguf](https://huggingface.co/RichardErkhov/yifangong_-_tinyllama_random-gguf/blob/main/tinyllama_random.Q4_0.gguf) | Q4_0 | 0.59GB |
| [tinyllama_random.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/yifangong_-_tinyllama_random-gguf/blob/main/tinyllama_random.IQ4_NL.gguf) | IQ4_NL | 0.6GB |
| [tinyllama_random.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/yifangong_-_tinyllama_random-gguf/blob/main/tinyllama_random.Q4_K_S.gguf) | Q4_K_S | 0.6GB |
| [tinyllama_random.Q4_K.gguf](https://huggingface.co/RichardErkhov/yifangong_-_tinyllama_random-gguf/blob/main/tinyllama_random.Q4_K.gguf) | Q4_K | 0.62GB |
| [tinyllama_random.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/yifangong_-_tinyllama_random-gguf/blob/main/tinyllama_random.Q4_K_M.gguf) | Q4_K_M | 0.62GB |
| [tinyllama_random.Q4_1.gguf](https://huggingface.co/RichardErkhov/yifangong_-_tinyllama_random-gguf/blob/main/tinyllama_random.Q4_1.gguf) | Q4_1 | 0.65GB |
| [tinyllama_random.Q5_0.gguf](https://huggingface.co/RichardErkhov/yifangong_-_tinyllama_random-gguf/blob/main/tinyllama_random.Q5_0.gguf) | Q5_0 | 0.71GB |
| [tinyllama_random.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/yifangong_-_tinyllama_random-gguf/blob/main/tinyllama_random.Q5_K_S.gguf) | Q5_K_S | 0.71GB |
| [tinyllama_random.Q5_K.gguf](https://huggingface.co/RichardErkhov/yifangong_-_tinyllama_random-gguf/blob/main/tinyllama_random.Q5_K.gguf) | Q5_K | 0.73GB |
| [tinyllama_random.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/yifangong_-_tinyllama_random-gguf/blob/main/tinyllama_random.Q5_K_M.gguf) | Q5_K_M | 0.73GB |
| [tinyllama_random.Q5_1.gguf](https://huggingface.co/RichardErkhov/yifangong_-_tinyllama_random-gguf/blob/main/tinyllama_random.Q5_1.gguf) | Q5_1 | 0.77GB |
| [tinyllama_random.Q6_K.gguf](https://huggingface.co/RichardErkhov/yifangong_-_tinyllama_random-gguf/blob/main/tinyllama_random.Q6_K.gguf) | Q6_K | 0.84GB |
| [tinyllama_random.Q8_0.gguf](https://huggingface.co/RichardErkhov/yifangong_-_tinyllama_random-gguf/blob/main/tinyllama_random.Q8_0.gguf) | Q8_0 | 1.09GB |
Original model description:
---
library_name: transformers
tags: []
---
# 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]
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#### 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]
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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]
|
Blackroot/Llama3.1-Nemotron-70B-3.8BPW-6H | Blackroot | 2024-10-16T01:31:02Z | 6 | 1 | null | [
"safetensors",
"llama",
"base_model:nvidia/Llama-3.1-Nemotron-70B-Instruct",
"base_model:finetune:nvidia/Llama-3.1-Nemotron-70B-Instruct",
"region:us"
] | null | 2024-10-16T01:09:39Z | ---
base_model:
- nvidia/Llama-3.1-Nemotron-70B-Instruct
---
Original: https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct |
mradermacher/Mahou-1.5-llama3.1-70B-GGUF | mradermacher | 2024-10-16T01:25:37Z | 18 | 3 | transformers | [
"transformers",
"gguf",
"en",
"dataset:flammenai/MahouMix-v1",
"base_model:flammenai/Mahou-1.5-llama3.1-70B",
"base_model:quantized:flammenai/Mahou-1.5-llama3.1-70B",
"license:llama3.1",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-14T09:10:45Z | ---
base_model: flammenai/Mahou-1.5-llama3.1-70B
datasets:
- flammenai/MahouMix-v1
language:
- en
library_name: transformers
license: llama3.1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/flammenai/Mahou-1.5-llama3.1-70B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Mahou-1.5-llama3.1-70B-i1-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/Mahou-1.5-llama3.1-70B-GGUF/resolve/main/Mahou-1.5-llama3.1-70B.Q2_K.gguf) | Q2_K | 26.5 | |
| [GGUF](https://huggingface.co/mradermacher/Mahou-1.5-llama3.1-70B-GGUF/resolve/main/Mahou-1.5-llama3.1-70B.Q3_K_S.gguf) | Q3_K_S | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mahou-1.5-llama3.1-70B-GGUF/resolve/main/Mahou-1.5-llama3.1-70B.Q3_K_M.gguf) | Q3_K_M | 34.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mahou-1.5-llama3.1-70B-GGUF/resolve/main/Mahou-1.5-llama3.1-70B.Q3_K_L.gguf) | Q3_K_L | 37.2 | |
| [GGUF](https://huggingface.co/mradermacher/Mahou-1.5-llama3.1-70B-GGUF/resolve/main/Mahou-1.5-llama3.1-70B.IQ4_XS.gguf) | IQ4_XS | 38.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mahou-1.5-llama3.1-70B-GGUF/resolve/main/Mahou-1.5-llama3.1-70B.Q4_K_S.gguf) | Q4_K_S | 40.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mahou-1.5-llama3.1-70B-GGUF/resolve/main/Mahou-1.5-llama3.1-70B.Q4_K_M.gguf) | Q4_K_M | 42.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mahou-1.5-llama3.1-70B-GGUF/resolve/main/Mahou-1.5-llama3.1-70B.Q5_K_S.gguf) | Q5_K_S | 48.8 | |
| [GGUF](https://huggingface.co/mradermacher/Mahou-1.5-llama3.1-70B-GGUF/resolve/main/Mahou-1.5-llama3.1-70B.Q5_K_M.gguf) | Q5_K_M | 50.0 | |
| [PART 1](https://huggingface.co/mradermacher/Mahou-1.5-llama3.1-70B-GGUF/resolve/main/Mahou-1.5-llama3.1-70B.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Mahou-1.5-llama3.1-70B-GGUF/resolve/main/Mahou-1.5-llama3.1-70B.Q6_K.gguf.part2of2) | Q6_K | 58.0 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Mahou-1.5-llama3.1-70B-GGUF/resolve/main/Mahou-1.5-llama3.1-70B.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Mahou-1.5-llama3.1-70B-GGUF/resolve/main/Mahou-1.5-llama3.1-70B.Q8_0.gguf.part2of2) | Q8_0 | 75.1 | fast, best quality |
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 -->
|
benhaotang/nemo-math-science-philosophy-12B-Q8_0-GGUF | benhaotang | 2024-10-16T01:23:00Z | 14 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:benhaotang/nemo-math-science-philosophy-12B",
"base_model:quantized:benhaotang/nemo-math-science-philosophy-12B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-15T23:04:21Z | ---
base_model: benhaotang/nemo-math-science-philosophy-12B
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
---
# benhaotang/nemo-math-science-philosophy-12B-Q8_0-GGUF
This model was converted to GGUF format from [`benhaotang/nemo-math-science-philosophy-12B`](https://huggingface.co/benhaotang/nemo-math-science-philosophy-12B) 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/benhaotang/nemo-math-science-philosophy-12B) for more details on the model.
## Use with Ollama
```
TEMPLATE """{{ if .System }}[INST]system
{{ .System }}[\INST]
{{ end }}{{ if .Prompt }}[INST]user
{{ .Prompt }}[\INST]
{{ end }}[INST]assistant
{{ .Response }}[\INST]</s>
"""
PARAMETER stop "</s>"
PARAMETER stop "[\INST]"
```
## 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 benhaotang/nemo-math-science-philosophy-12B-Q8_0-GGUF --hf-file nemo-math-science-philosophy-12b-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo benhaotang/nemo-math-science-philosophy-12B-Q8_0-GGUF --hf-file nemo-math-science-philosophy-12b-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 benhaotang/nemo-math-science-philosophy-12B-Q8_0-GGUF --hf-file nemo-math-science-philosophy-12b-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo benhaotang/nemo-math-science-philosophy-12B-Q8_0-GGUF --hf-file nemo-math-science-philosophy-12b-q8_0.gguf -c 2048
```
|
benhaotang/nemo-math-science-philosophy-12B | benhaotang | 2024-10-16T01:19:04Z | 58 | 3 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2306.01708",
"base_model:EpistemeAI/Mistral-Nemo-Instruct-12B-Philosophy-Math",
"base_model:merge:EpistemeAI/Mistral-Nemo-Instruct-12B-Philosophy-Math",
"base_model:anthracite-org/magnum-v2.5-12b-kto",
"base_model:merge:anthracite-org/magnum-v2.5-12b-kto",
"base_model:nbeerbower/mistral-nemo-wissenschaft-12B",
"base_model:merge:nbeerbower/mistral-nemo-wissenschaft-12B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-15T22:48:03Z | ---
base_model:
- anthracite-org/magnum-v2.5-12b-kto
- nbeerbower/mistral-nemo-wissenschaft-12B
- EpistemeAI/Mistral-Nemo-Instruct-12B-Philosophy-Math
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Ollama template
```
TEMPLATE """{{ if .System }}[INST]system
{{ .System }}[\INST]
{{ end }}{{ if .Prompt }}[INST]user
{{ .Prompt }}[\INST]
{{ end }}[INST]assistant
{{ .Response }}[\INST]</s>
"""
PARAMETER stop "</s>"
PARAMETER stop "[\INST]"
```
## Merge Details
### Merge Method
This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [anthracite-org/magnum-v2.5-12b-kto](https://huggingface.co/anthracite-org/magnum-v2.5-12b-kto) as a base.
### Models Merged
The following models were included in the merge:
* [nbeerbower/mistral-nemo-wissenschaft-12B](https://huggingface.co/nbeerbower/mistral-nemo-wissenschaft-12B)
* [EpistemeAI/Mistral-Nemo-Instruct-12B-Philosophy-Math](https://huggingface.co/EpistemeAI/Mistral-Nemo-Instruct-12B-Philosophy-Math)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: anthracite-org/magnum-v2.5-12b-kto
#no parameters necessary for base model
- model: EpistemeAI/Mistral-Nemo-Instruct-12B-Philosophy-Math
parameters:
density: 0.5
weight: 0.5
- model: nbeerbower/mistral-nemo-wissenschaft-12B
parameters:
density: 0.5
weight: 0.5
merge_method: ties
base_model: anthracite-org/magnum-v2.5-12b-kto
parameters:
normalize: false
int8_mask: true
dtype: float16
```
|
nagolinc/nodelve_gemma_2_9b | nagolinc | 2024-10-16T01:17:48Z | 115 | 0 | transformers | [
"transformers",
"safetensors",
"gguf",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-15T15:42:23Z | ---
library_name: transformers
tags: []
---
# 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] |
seawolf2357/hanbok | seawolf2357 | 2024-10-16T01:07:22Z | 3,945 | 52 | diffusers | [
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"ai-toolkit",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2024-10-16T01:00:28Z | ---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- ai-toolkit
widget:
- text: a woman wearing a traditional Korean Hanbok, a long-sleeved blouse with
intricate embroidery and a high-waisted skirt. The blouse is a deep blue color
with a white collar and cuffs, and the skirt is a lighter shade of blue with
a pattern of small white flowers. The woman is standing in a graceful pose,
her hands clasped in front of her and her head tilted slightly to the side.
[trigger]
output:
url: samples/1729040425975__000001000_0.jpg
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: hanbok
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
# hanbok
<Gallery />
## Trigger words
You should use `hanbok` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, etc.
Weights for this model are available in Safetensors format.
[Download](/seawolf2357/hanbok/tree/main) them in the Files & versions tab.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('seawolf2357/hanbok', weight_name='hanbok.safetensors')
image = pipeline('a woman wearing a traditional Korean Hanbok, a long-sleeved blouse with intricate embroidery and a high-waisted skirt. The blouse is a deep blue color with a white collar and cuffs, and the skirt is a lighter shade of blue with a pattern of small white flowers. The woman is standing in a graceful pose, her hands clasped in front of her and her head tilted slightly to the side. [trigger]').images[0]
image.save("my_image.png")
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
|
jmercat/diffusion_sample_koch_feed_cat | jmercat | 2024-10-16T01:06:01Z | 12 | 0 | lerobot | [
"lerobot",
"safetensors",
"diffusion-policy",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"robotics",
"region:us"
] | robotics | 2024-10-16T01:01:55Z | ---
library_name: lerobot
tags:
- diffusion-policy
- model_hub_mixin
- pytorch_model_hub_mixin
- robotics
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Library: https://github.com/huggingface/lerobot
- Docs: [More Information Needed] |
RichardErkhov/iujinasena_-_tinyllama-1_1b-conv-gguf | RichardErkhov | 2024-10-16T00:57:18Z | 6 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-16T00:34:34Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
tinyllama-1_1b-conv - GGUF
- Model creator: https://huggingface.co/iujinasena/
- Original model: https://huggingface.co/iujinasena/tinyllama-1_1b-conv/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [tinyllama-1_1b-conv.Q2_K.gguf](https://huggingface.co/RichardErkhov/iujinasena_-_tinyllama-1_1b-conv-gguf/blob/main/tinyllama-1_1b-conv.Q2_K.gguf) | Q2_K | 0.4GB |
| [tinyllama-1_1b-conv.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/iujinasena_-_tinyllama-1_1b-conv-gguf/blob/main/tinyllama-1_1b-conv.IQ3_XS.gguf) | IQ3_XS | 0.44GB |
| [tinyllama-1_1b-conv.IQ3_S.gguf](https://huggingface.co/RichardErkhov/iujinasena_-_tinyllama-1_1b-conv-gguf/blob/main/tinyllama-1_1b-conv.IQ3_S.gguf) | IQ3_S | 0.47GB |
| [tinyllama-1_1b-conv.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/iujinasena_-_tinyllama-1_1b-conv-gguf/blob/main/tinyllama-1_1b-conv.Q3_K_S.gguf) | Q3_K_S | 0.47GB |
| [tinyllama-1_1b-conv.IQ3_M.gguf](https://huggingface.co/RichardErkhov/iujinasena_-_tinyllama-1_1b-conv-gguf/blob/main/tinyllama-1_1b-conv.IQ3_M.gguf) | IQ3_M | 0.48GB |
| [tinyllama-1_1b-conv.Q3_K.gguf](https://huggingface.co/RichardErkhov/iujinasena_-_tinyllama-1_1b-conv-gguf/blob/main/tinyllama-1_1b-conv.Q3_K.gguf) | Q3_K | 0.51GB |
| [tinyllama-1_1b-conv.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/iujinasena_-_tinyllama-1_1b-conv-gguf/blob/main/tinyllama-1_1b-conv.Q3_K_M.gguf) | Q3_K_M | 0.51GB |
| [tinyllama-1_1b-conv.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/iujinasena_-_tinyllama-1_1b-conv-gguf/blob/main/tinyllama-1_1b-conv.Q3_K_L.gguf) | Q3_K_L | 0.55GB |
| [tinyllama-1_1b-conv.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/iujinasena_-_tinyllama-1_1b-conv-gguf/blob/main/tinyllama-1_1b-conv.IQ4_XS.gguf) | IQ4_XS | 0.57GB |
| [tinyllama-1_1b-conv.Q4_0.gguf](https://huggingface.co/RichardErkhov/iujinasena_-_tinyllama-1_1b-conv-gguf/blob/main/tinyllama-1_1b-conv.Q4_0.gguf) | Q4_0 | 0.59GB |
| [tinyllama-1_1b-conv.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/iujinasena_-_tinyllama-1_1b-conv-gguf/blob/main/tinyllama-1_1b-conv.IQ4_NL.gguf) | IQ4_NL | 0.6GB |
| [tinyllama-1_1b-conv.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/iujinasena_-_tinyllama-1_1b-conv-gguf/blob/main/tinyllama-1_1b-conv.Q4_K_S.gguf) | Q4_K_S | 0.6GB |
| [tinyllama-1_1b-conv.Q4_K.gguf](https://huggingface.co/RichardErkhov/iujinasena_-_tinyllama-1_1b-conv-gguf/blob/main/tinyllama-1_1b-conv.Q4_K.gguf) | Q4_K | 0.62GB |
| [tinyllama-1_1b-conv.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/iujinasena_-_tinyllama-1_1b-conv-gguf/blob/main/tinyllama-1_1b-conv.Q4_K_M.gguf) | Q4_K_M | 0.62GB |
| [tinyllama-1_1b-conv.Q4_1.gguf](https://huggingface.co/RichardErkhov/iujinasena_-_tinyllama-1_1b-conv-gguf/blob/main/tinyllama-1_1b-conv.Q4_1.gguf) | Q4_1 | 0.65GB |
| [tinyllama-1_1b-conv.Q5_0.gguf](https://huggingface.co/RichardErkhov/iujinasena_-_tinyllama-1_1b-conv-gguf/blob/main/tinyllama-1_1b-conv.Q5_0.gguf) | Q5_0 | 0.71GB |
| [tinyllama-1_1b-conv.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/iujinasena_-_tinyllama-1_1b-conv-gguf/blob/main/tinyllama-1_1b-conv.Q5_K_S.gguf) | Q5_K_S | 0.71GB |
| [tinyllama-1_1b-conv.Q5_K.gguf](https://huggingface.co/RichardErkhov/iujinasena_-_tinyllama-1_1b-conv-gguf/blob/main/tinyllama-1_1b-conv.Q5_K.gguf) | Q5_K | 0.73GB |
| [tinyllama-1_1b-conv.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/iujinasena_-_tinyllama-1_1b-conv-gguf/blob/main/tinyllama-1_1b-conv.Q5_K_M.gguf) | Q5_K_M | 0.73GB |
| [tinyllama-1_1b-conv.Q5_1.gguf](https://huggingface.co/RichardErkhov/iujinasena_-_tinyllama-1_1b-conv-gguf/blob/main/tinyllama-1_1b-conv.Q5_1.gguf) | Q5_1 | 0.77GB |
| [tinyllama-1_1b-conv.Q6_K.gguf](https://huggingface.co/RichardErkhov/iujinasena_-_tinyllama-1_1b-conv-gguf/blob/main/tinyllama-1_1b-conv.Q6_K.gguf) | Q6_K | 0.84GB |
| [tinyllama-1_1b-conv.Q8_0.gguf](https://huggingface.co/RichardErkhov/iujinasena_-_tinyllama-1_1b-conv-gguf/blob/main/tinyllama-1_1b-conv.Q8_0.gguf) | Q8_0 | 1.09GB |
Original model description:
---
library_name: transformers
tags: []
---
# 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]
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[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
RichardErkhov/Vikhrmodels_-_Vikhr-tiny-0.1-gguf | RichardErkhov | 2024-10-16T00:50:05Z | 9 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us"
] | null | 2024-10-15T23:36:07Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Vikhr-tiny-0.1 - GGUF
- Model creator: https://huggingface.co/Vikhrmodels/
- Original model: https://huggingface.co/Vikhrmodels/Vikhr-tiny-0.1/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Vikhr-tiny-0.1.Q2_K.gguf](https://huggingface.co/RichardErkhov/Vikhrmodels_-_Vikhr-tiny-0.1-gguf/blob/main/Vikhr-tiny-0.1.Q2_K.gguf) | Q2_K | 1.17GB |
| [Vikhr-tiny-0.1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Vikhrmodels_-_Vikhr-tiny-0.1-gguf/blob/main/Vikhr-tiny-0.1.IQ3_XS.gguf) | IQ3_XS | 1.25GB |
| [Vikhr-tiny-0.1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Vikhrmodels_-_Vikhr-tiny-0.1-gguf/blob/main/Vikhr-tiny-0.1.IQ3_S.gguf) | IQ3_S | 1.31GB |
| [Vikhr-tiny-0.1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Vikhrmodels_-_Vikhr-tiny-0.1-gguf/blob/main/Vikhr-tiny-0.1.Q3_K_S.gguf) | Q3_K_S | 1.31GB |
| [Vikhr-tiny-0.1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Vikhrmodels_-_Vikhr-tiny-0.1-gguf/blob/main/Vikhr-tiny-0.1.IQ3_M.gguf) | IQ3_M | 1.37GB |
| [Vikhr-tiny-0.1.Q3_K.gguf](https://huggingface.co/RichardErkhov/Vikhrmodels_-_Vikhr-tiny-0.1-gguf/blob/main/Vikhr-tiny-0.1.Q3_K.gguf) | Q3_K | 1.42GB |
| [Vikhr-tiny-0.1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Vikhrmodels_-_Vikhr-tiny-0.1-gguf/blob/main/Vikhr-tiny-0.1.Q3_K_M.gguf) | Q3_K_M | 1.42GB |
| [Vikhr-tiny-0.1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Vikhrmodels_-_Vikhr-tiny-0.1-gguf/blob/main/Vikhr-tiny-0.1.Q3_K_L.gguf) | Q3_K_L | 1.5GB |
| [Vikhr-tiny-0.1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Vikhrmodels_-_Vikhr-tiny-0.1-gguf/blob/main/Vikhr-tiny-0.1.IQ4_XS.gguf) | IQ4_XS | 1.5GB |
| [Vikhr-tiny-0.1.Q4_0.gguf](https://huggingface.co/RichardErkhov/Vikhrmodels_-_Vikhr-tiny-0.1-gguf/blob/main/Vikhr-tiny-0.1.Q4_0.gguf) | Q4_0 | 1.54GB |
| [Vikhr-tiny-0.1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Vikhrmodels_-_Vikhr-tiny-0.1-gguf/blob/main/Vikhr-tiny-0.1.IQ4_NL.gguf) | IQ4_NL | 1.56GB |
| [Vikhr-tiny-0.1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Vikhrmodels_-_Vikhr-tiny-0.1-gguf/blob/main/Vikhr-tiny-0.1.Q4_K_S.gguf) | Q4_K_S | 1.61GB |
| [Vikhr-tiny-0.1.Q4_K.gguf](https://huggingface.co/RichardErkhov/Vikhrmodels_-_Vikhr-tiny-0.1-gguf/blob/main/Vikhr-tiny-0.1.Q4_K.gguf) | Q4_K | 1.72GB |
| [Vikhr-tiny-0.1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Vikhrmodels_-_Vikhr-tiny-0.1-gguf/blob/main/Vikhr-tiny-0.1.Q4_K_M.gguf) | Q4_K_M | 1.72GB |
| [Vikhr-tiny-0.1.Q4_1.gguf](https://huggingface.co/RichardErkhov/Vikhrmodels_-_Vikhr-tiny-0.1-gguf/blob/main/Vikhr-tiny-0.1.Q4_1.gguf) | Q4_1 | 1.69GB |
| [Vikhr-tiny-0.1.Q5_0.gguf](https://huggingface.co/RichardErkhov/Vikhrmodels_-_Vikhr-tiny-0.1-gguf/blob/main/Vikhr-tiny-0.1.Q5_0.gguf) | Q5_0 | 1.83GB |
| [Vikhr-tiny-0.1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Vikhrmodels_-_Vikhr-tiny-0.1-gguf/blob/main/Vikhr-tiny-0.1.Q5_K_S.gguf) | Q5_K_S | 1.86GB |
| [Vikhr-tiny-0.1.Q5_K.gguf](https://huggingface.co/RichardErkhov/Vikhrmodels_-_Vikhr-tiny-0.1-gguf/blob/main/Vikhr-tiny-0.1.Q5_K.gguf) | Q5_K | 1.95GB |
| [Vikhr-tiny-0.1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Vikhrmodels_-_Vikhr-tiny-0.1-gguf/blob/main/Vikhr-tiny-0.1.Q5_K_M.gguf) | Q5_K_M | 1.95GB |
| [Vikhr-tiny-0.1.Q5_1.gguf](https://huggingface.co/RichardErkhov/Vikhrmodels_-_Vikhr-tiny-0.1-gguf/blob/main/Vikhr-tiny-0.1.Q5_1.gguf) | Q5_1 | 1.97GB |
| [Vikhr-tiny-0.1.Q6_K.gguf](https://huggingface.co/RichardErkhov/Vikhrmodels_-_Vikhr-tiny-0.1-gguf/blob/main/Vikhr-tiny-0.1.Q6_K.gguf) | Q6_K | 2.25GB |
| [Vikhr-tiny-0.1.Q8_0.gguf](https://huggingface.co/RichardErkhov/Vikhrmodels_-_Vikhr-tiny-0.1-gguf/blob/main/Vikhr-tiny-0.1.Q8_0.gguf) | Q8_0 | 2.76GB |
Original model description:
---
license: apache-2.0
language:
- ru
- en
- zh
library_name: transformers
---
DONT TOUCH, under dev
|Task |Version| Metric |Value | |Stderr|
|-----|------:|--------|-----:|---|-----:|
|parus| 0|acc |0.4950|± |0.0250|
|rcb | 0|acc |0.3333|± |0.0226|
| | |f1_macro|0.1667| | |
|rwsd | 0|acc |0.4901|± |0.0203|
|mmlu| 0| 0.31|0.225|
Based on https://huggingface.co/openbmb/MiniCPM-2B-sft-bf16
https://wandb.ai/alexwortega/cpm_rus/runs/32w8pv7x?workspace=user-alexwortega
lol
|
hiko1999/Qwen2-Wildfire-v1.2-2B | hiko1999 | 2024-10-16T00:46:13Z | 6 | 1 | null | [
"safetensors",
"qwen2_vl",
"region:us"
] | null | 2024-10-15T03:52:15Z | license: apache-2.0
tags:
- fire
- wildfire
description: |
这是1.2的版本，该版本是基于更加优质的wildfire火灾数据集（自建）进行更加充分的训练得来的。详细的信息请查看Qwen2-Wildfire-2B的版本介绍，这里就佛系不写了。
これはバージョン1.2であり、より質の高いwildfire火災データセット（自作）に基づいて、より十分なトレーニングを行った結果です。詳細な情報はQwen2-Wildfire-2Bのバージョン紹介をご覧ください。ここでは省略します。
This is version 1.2, which is derived from more comprehensive training based on a higher-quality wildfire dataset (self-built). For detailed information, please refer to the version introduction of Qwen2-Wildfire-2B; I won't elaborate further here.
|
KelseyHahmo/Yelp-Review-Sentiment-Analysis | KelseyHahmo | 2024-10-16T00:38:09Z | 106 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-11T01:16:21Z | ---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: Yelp-Review-Sentiment-Analysis
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Yelp-Review-Sentiment-Analysis
This model was trained from the [YelpReviewFull Dataset](https://huggingface.co/datasets/Yelp/yelp_review_full).
## Model description
Fine tuned from the [ElizaClaPa/SentimentAnalysis-YelpReviews-OptimizedModel](https://huggingface.co/ElizaClaPa/SentimentAnalysis-YelpReviews-OptimizedModel)
## Intended uses & limitations
Sentiment analysis of Yelp Reviews.
## Training and evaluation data
Trained on 101 data points from the [YelpReviewFull Dataset](https://huggingface.co/datasets/Yelp/yelp_review_full) due to computing constraints.
Evaluated on 51 data points from the [YelpReviewFull Dataset](https://huggingface.co/datasets/Yelp/yelp_review_full) due to computing constraints.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
precision recall f1-score support
0 1.00 0.00 0.00 10
1 0.25 0.15 0.19 13
2 0.00 0.00 0.00 5
3 0.17 0.18 0.17 11
4 0.25 0.20 0.22 10
5 0.00 1.00 0.00 0
accuracy 0.12 49
macro avg 0.28 0.26 0.10 49
weighted avg 0.36 0.12 0.13 49
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Tokenizers 0.19.1
|
aarath97/gemma-2b-mt-Dogri-to-English | aarath97 | 2024-10-16T00:37:36Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-08-31T10:14:13Z | ---
library_name: transformers
tags: []
---
# 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]
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- **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]
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## 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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### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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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
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]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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### Testing Data, Factors & Metrics
#### Testing Data
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#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## 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]
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bartowski/Llama-3.1-Nemotron-70B-Instruct-HF-GGUF | bartowski | 2024-10-16T00:29:42Z | 7,581 | 92 | null | [
"gguf",
"nvidia",
"llama3.1",
"text-generation",
"en",
"dataset:nvidia/HelpSteer2",
"base_model:nvidia/Llama-3.1-Nemotron-70B-Instruct-HF",
"base_model:quantized:nvidia/Llama-3.1-Nemotron-70B-Instruct-HF",
"license:llama3.1",
"region:us",
"imatrix",
"conversational"
] | text-generation | 2024-10-15T21:38:48Z | ---
base_model: nvidia/Llama-3.1-Nemotron-70B-Instruct-HF
datasets:
- nvidia/HelpSteer2
language:
- en
license: llama3.1
pipeline_tag: text-generation
tags:
- nvidia
- llama3.1
quantized_by: bartowski
inference: false
fine-tuning: false
---
## Llamacpp imatrix Quantizations of Llama-3.1-Nemotron-70B-Instruct-HF
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3901">b3901</a> for quantization.
Original model: https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
Run them in [LM Studio](https://lmstudio.ai/)
## Prompt format
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Split | Description |
| -------- | ---------- | --------- | ----- | ----------- |
| [Llama-3.1-Nemotron-70B-Instruct-HF-Q8_0.gguf](https://huggingface.co/bartowski/Llama-3.1-Nemotron-70B-Instruct-HF-GGUF/tree/main/Llama-3.1-Nemotron-70B-Instruct-HF-Q8_0) | Q8_0 | 74.98GB | true | Extremely high quality, generally unneeded but max available quant. |
| [Llama-3.1-Nemotron-70B-Instruct-HF-Q6_K.gguf](https://huggingface.co/bartowski/Llama-3.1-Nemotron-70B-Instruct-HF-GGUF/tree/main/Llama-3.1-Nemotron-70B-Instruct-HF-Q6_K) | Q6_K | 57.89GB | true | Very high quality, near perfect, *recommended*. |
| [Llama-3.1-Nemotron-70B-Instruct-HF-Q5_K_L.gguf](https://huggingface.co/bartowski/Llama-3.1-Nemotron-70B-Instruct-HF-GGUF/tree/main/Llama-3.1-Nemotron-70B-Instruct-HF-Q5_K_L) | Q5_K_L | 50.60GB | true | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
| [Llama-3.1-Nemotron-70B-Instruct-HF-Q5_K_M.gguf](https://huggingface.co/bartowski/Llama-3.1-Nemotron-70B-Instruct-HF-GGUF/tree/main/Llama-3.1-Nemotron-70B-Instruct-HF-Q5_K_M) | Q5_K_M | 49.95GB | true | High quality, *recommended*. |
| [Llama-3.1-Nemotron-70B-Instruct-HF-Q5_K_S.gguf](https://huggingface.co/bartowski/Llama-3.1-Nemotron-70B-Instruct-HF-GGUF/blob/main/Llama-3.1-Nemotron-70B-Instruct-HF-Q5_K_S.gguf) | Q5_K_S | 48.66GB | false | High quality, *recommended*. |
| [Llama-3.1-Nemotron-70B-Instruct-HF-Q4_K_L.gguf](https://huggingface.co/bartowski/Llama-3.1-Nemotron-70B-Instruct-HF-GGUF/blob/main/Llama-3.1-Nemotron-70B-Instruct-HF-Q4_K_L.gguf) | Q4_K_L | 43.30GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
| [Llama-3.1-Nemotron-70B-Instruct-HF-Q4_K_M.gguf](https://huggingface.co/bartowski/Llama-3.1-Nemotron-70B-Instruct-HF-GGUF/blob/main/Llama-3.1-Nemotron-70B-Instruct-HF-Q4_K_M.gguf) | Q4_K_M | 42.52GB | false | Good quality, default size for must use cases, *recommended*. |
| [Llama-3.1-Nemotron-70B-Instruct-HF-Q4_K_S.gguf](https://huggingface.co/bartowski/Llama-3.1-Nemotron-70B-Instruct-HF-GGUF/blob/main/Llama-3.1-Nemotron-70B-Instruct-HF-Q4_K_S.gguf) | Q4_K_S | 40.35GB | false | Slightly lower quality with more space savings, *recommended*. |
| [Llama-3.1-Nemotron-70B-Instruct-HF-Q4_0.gguf](https://huggingface.co/bartowski/Llama-3.1-Nemotron-70B-Instruct-HF-GGUF/blob/main/Llama-3.1-Nemotron-70B-Instruct-HF-Q4_0.gguf) | Q4_0 | 40.12GB | false | Legacy format, generally not worth using over similarly sized formats |
| [Llama-3.1-Nemotron-70B-Instruct-HF-Q3_K_XL.gguf](https://huggingface.co/bartowski/Llama-3.1-Nemotron-70B-Instruct-HF-GGUF/blob/main/Llama-3.1-Nemotron-70B-Instruct-HF-Q3_K_XL.gguf) | Q3_K_XL | 38.06GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
| [Llama-3.1-Nemotron-70B-Instruct-HF-IQ4_XS.gguf](https://huggingface.co/bartowski/Llama-3.1-Nemotron-70B-Instruct-HF-GGUF/blob/main/Llama-3.1-Nemotron-70B-Instruct-HF-IQ4_XS.gguf) | IQ4_XS | 37.90GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [Llama-3.1-Nemotron-70B-Instruct-HF-Q3_K_L.gguf](https://huggingface.co/bartowski/Llama-3.1-Nemotron-70B-Instruct-HF-GGUF/blob/main/Llama-3.1-Nemotron-70B-Instruct-HF-Q3_K_L.gguf) | Q3_K_L | 37.14GB | false | Lower quality but usable, good for low RAM availability. |
| [Llama-3.1-Nemotron-70B-Instruct-HF-Q3_K_M.gguf](https://huggingface.co/bartowski/Llama-3.1-Nemotron-70B-Instruct-HF-GGUF/blob/main/Llama-3.1-Nemotron-70B-Instruct-HF-Q3_K_M.gguf) | Q3_K_M | 34.27GB | false | Low quality. |
| [Llama-3.1-Nemotron-70B-Instruct-HF-IQ3_M.gguf](https://huggingface.co/bartowski/Llama-3.1-Nemotron-70B-Instruct-HF-GGUF/blob/main/Llama-3.1-Nemotron-70B-Instruct-HF-IQ3_M.gguf) | IQ3_M | 31.94GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [Llama-3.1-Nemotron-70B-Instruct-HF-Q3_K_S.gguf](https://huggingface.co/bartowski/Llama-3.1-Nemotron-70B-Instruct-HF-GGUF/blob/main/Llama-3.1-Nemotron-70B-Instruct-HF-Q3_K_S.gguf) | Q3_K_S | 30.91GB | false | Low quality, not recommended. |
| [Llama-3.1-Nemotron-70B-Instruct-HF-IQ3_XXS.gguf](https://huggingface.co/bartowski/Llama-3.1-Nemotron-70B-Instruct-HF-GGUF/blob/main/Llama-3.1-Nemotron-70B-Instruct-HF-IQ3_XXS.gguf) | IQ3_XXS | 27.47GB | false | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [Llama-3.1-Nemotron-70B-Instruct-HF-Q2_K_L.gguf](https://huggingface.co/bartowski/Llama-3.1-Nemotron-70B-Instruct-HF-GGUF/blob/main/Llama-3.1-Nemotron-70B-Instruct-HF-Q2_K_L.gguf) | Q2_K_L | 27.40GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
| [Llama-3.1-Nemotron-70B-Instruct-HF-Q2_K.gguf](https://huggingface.co/bartowski/Llama-3.1-Nemotron-70B-Instruct-HF-GGUF/blob/main/Llama-3.1-Nemotron-70B-Instruct-HF-Q2_K.gguf) | Q2_K | 26.38GB | false | Very low quality but surprisingly usable. |
| [Llama-3.1-Nemotron-70B-Instruct-HF-IQ2_M.gguf](https://huggingface.co/bartowski/Llama-3.1-Nemotron-70B-Instruct-HF-GGUF/blob/main/Llama-3.1-Nemotron-70B-Instruct-HF-IQ2_M.gguf) | IQ2_M | 24.12GB | false | Relatively low quality, uses SOTA techniques to be surprisingly usable. |
| [Llama-3.1-Nemotron-70B-Instruct-HF-IQ2_XS.gguf](https://huggingface.co/bartowski/Llama-3.1-Nemotron-70B-Instruct-HF-GGUF/blob/main/Llama-3.1-Nemotron-70B-Instruct-HF-IQ2_XS.gguf) | IQ2_XS | 21.14GB | false | Low quality, uses SOTA techniques to be usable. |
| [Llama-3.1-Nemotron-70B-Instruct-HF-IQ2_XXS.gguf](https://huggingface.co/bartowski/Llama-3.1-Nemotron-70B-Instruct-HF-GGUF/blob/main/Llama-3.1-Nemotron-70B-Instruct-HF-IQ2_XXS.gguf) | IQ2_XXS | 19.10GB | false | Very low quality, uses SOTA techniques to be usable. |
| [Llama-3.1-Nemotron-70B-Instruct-HF-IQ1_M.gguf](https://huggingface.co/bartowski/Llama-3.1-Nemotron-70B-Instruct-HF-GGUF/blob/main/Llama-3.1-Nemotron-70B-Instruct-HF-IQ1_M.gguf) | IQ1_M | 16.75GB | false | Extremely low quality, *not* recommended. |
## Embed/output weights
Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.
Some say that this improves the quality, others don't notice any difference. If you use these models PLEASE COMMENT with your findings. I would like feedback that these are actually used and useful so I don't keep uploading quants no one is using.
Thanks!
## Downloading using huggingface-cli
First, make sure you have hugginface-cli installed:
```
pip install -U "huggingface_hub[cli]"
```
Then, you can target the specific file you want:
```
huggingface-cli download bartowski/Llama-3.1-Nemotron-70B-Instruct-HF-GGUF --include "Llama-3.1-Nemotron-70B-Instruct-HF-Q4_K_M.gguf" --local-dir ./
```
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download bartowski/Llama-3.1-Nemotron-70B-Instruct-HF-GGUF --include "Llama-3.1-Nemotron-70B-Instruct-HF-Q8_0/*" --local-dir ./
```
You can either specify a new local-dir (Llama-3.1-Nemotron-70B-Instruct-HF-Q8_0) or download them all in place (./)
## Q4_0_X_X
These are *NOT* for Metal (Apple) offloading, only ARM chips.
If you're using an ARM chip, the Q4_0_X_X quants will have a substantial speedup. Check out Q4_0_4_4 speed comparisons [on the original pull request](https://github.com/ggerganov/llama.cpp/pull/5780#pullrequestreview-21657544660)
To check which one would work best for your ARM chip, you can check [AArch64 SoC features](https://gpages.juszkiewicz.com.pl/arm-socs-table/arm-socs.html) (thanks EloyOn!).
## Which file should I choose?
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
## Credits
Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset
Thank you ZeroWw for the inspiration to experiment with embed/output
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
AnikiFan/single-channel-breast-segmentation-pan | AnikiFan | 2024-10-16T00:29:05Z | 7 | 0 | segmentation-models-pytorch | [
"segmentation-models-pytorch",
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"semantic-segmentation",
"pytorch",
"image-segmentation",
"license:mit",
"region:us"
] | image-segmentation | 2024-10-16T00:28:25Z | ---
library_name: segmentation-models-pytorch
license: mit
pipeline_tag: image-segmentation
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
- segmentation-models-pytorch
- semantic-segmentation
- pytorch
languages:
- python
---
# PAN Model Card
Table of Contents:
- [Load trained model](#load-trained-model)
- [Model init parameters](#model-init-parameters)
- [Model metrics](#model-metrics)
- [Dataset](#dataset)
## Load trained model
```python
import segmentation_models_pytorch as smp
model = smp.from_pretrained("<save-directory-or-this-repo>")
```
## Model init parameters
```python
model_init_params = {
"encoder_name": "resnet34",
"encoder_weights": "imagenet",
"encoder_output_stride": 16,
"decoder_channels": 32,
"in_channels": 1,
"classes": 1,
"activation": None,
"upsampling": 4,
"aux_params": None
}
```
## Model metrics
```json
[
{
"test_per_image_iou": 0.742108166217804,
"test_dataset_iou": 0.7333046197891235
}
]
```
## Dataset
Dataset name: Breast
## More Information
- Library: https://github.com/qubvel/segmentation_models.pytorch
- Docs: https://smp.readthedocs.io/en/latest/
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) |
bigstorm/Llama-3.1-Nemotron-70B-Instruct-HF-8.0bpw-8hb-exl2 | bigstorm | 2024-10-16T00:24:29Z | 20 | 3 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"nvidia",
"llama3.1",
"conversational",
"en",
"dataset:nvidia/HelpSteer2",
"arxiv:2410.01257",
"arxiv:2406.08673",
"arxiv:2310.05344",
"arxiv:2311.09528",
"base_model:meta-llama/Llama-3.1-70B-Instruct",
"base_model:quantized:meta-llama/Llama-3.1-70B-Instruct",
"license:llama3.1",
"autotrain_compatible",
"text-generation-inference",
"8-bit",
"exl2",
"region:us"
] | text-generation | 2024-10-16T00:07:30Z | ---
license: llama3.1
language:
- en
inference: false
fine-tuning: false
tags:
- nvidia
- llama3.1
datasets:
- nvidia/HelpSteer2
base_model: meta-llama/Llama-3.1-70B-Instruct
pipeline_tag: text-generation
library_name: transformers
---
# BigStorm - ExLLamaV2 (Exl2) Quantization
- 8.0 bpw target
- 8 head bits
Enjoy! Raise an issue if you'd like other BPW levels.
**Base Model Card Follows:**
---
# Model Overview
## Description:
Llama-3.1-Nemotron-70B-Instruct is a large language model customized by NVIDIA to improve the helpfulness of LLM generated responses to user queries.
This model reaches [Arena Hard](https://github.com/lmarena/arena-hard-auto) of 85.0, [AlpacaEval 2 LC](https://tatsu-lab.github.io/alpaca_eval/) of 57.6 and [GPT-4-Turbo MT-Bench](https://github.com/lm-sys/FastChat/pull/3158) of 8.98, which are known to be predictive of [LMSys Chatbot Arena Elo](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard)
As of 1 Oct 2024, this model is #1 on all three automatic alignment benchmarks (verified tab for AlpacaEval 2 LC), edging out strong frontier models such as GPT-4o and Claude 3.5 Sonnet.
This model was trained using RLHF (specifically, REINFORCE), [Llama-3.1-Nemotron-70B-Reward](https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Reward) and [HelpSteer2-Preference prompts](https://huggingface.co/datasets/nvidia/HelpSteer2) on a Llama-3.1-70B-Instruct model as the initial policy.
Llama-3.1-Nemotron-70B-Instruct-HF has been converted from [Llama-3.1-Nemotron-70B-Instruct](https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct) to support it in the HuggingFace Transformers codebase. Please note that evaluation results might be slightly different from the [Llama-3.1-Nemotron-70B-Instruct](https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct) as evaluated in NeMo-Aligner, which the evaluation results below are based on.
Try hosted inference for free at [build.nvidia.com](https://build.nvidia.com/nvidia/llama-3_1-nemotron-70b-instruct) - it comes with an OpenAI-compatible API interface.
See details on our paper at [https://arxiv.org/abs/2410.01257](https://arxiv.org/abs/2410.01257) - as a preview, this model can correctly the question ```How many r in strawberry?``` without specialized prompting or additional reasoning tokens:
```
A sweet question!
Let’s count the “R”s in “strawberry”:
1. S
2. T
3. R
4. A
5. W
6. B
7. E
8. R
9. R
10. Y
There are **3 “R”s** in the word “strawberry”.
```
Note: This model is a demonstration of our techniques for improving helpfulness in general-domain instruction following. It has not been tuned for performance in specialized domains such as math.
## Terms of use
By accessing this model, you are agreeing to the LLama 3.1 terms and conditions of the [license](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE), [acceptable use policy](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/USE_POLICY.md) and [Meta’s privacy policy](https://www.facebook.com/privacy/policy/)
## Evaluation Metrics
As of 1 Oct 2024, Llama-3.1-Nemotron-70B-Instruct performs best on Arena Hard, AlpacaEval 2 LC (verified tab) and MT Bench (GPT-4-Turbo)
| Model | Arena Hard | AlpacaEval | MT-Bench | Mean Response Length |
|:-----------------------------|:----------------|:-----|:----------|:-------|
|Details | (95% CI) | 2 LC (SE) | (GPT-4-Turbo) | (# of Characters for MT-Bench)|
| _**Llama-3.1-Nemotron-70B-Instruct**_ | **85.0** (-1.5, 1.5) | **57.6** (1.65) | **8.98** | 2199.8 |
| Llama-3.1-70B-Instruct | 55.7 (-2.9, 2.7) | 38.1 (0.90) | 8.22 | 1728.6 |
| Llama-3.1-405B-Instruct | 69.3 (-2.4, 2.2) | 39.3 (1.43) | 8.49 | 1664.7 |
| Claude-3-5-Sonnet-20240620 | 79.2 (-1.9, 1.7) | 52.4 (1.47) | 8.81 | 1619.9 |
| GPT-4o-2024-05-13 | 79.3 (-2.1, 2.0) | 57.5 (1.47) | 8.74 | 1752.2 |
## Usage:
You can use the model using HuggingFace Transformers library with 2 or more 80GB GPUs (NVIDIA Ampere or newer) with at least 150GB of free disk space to accomodate the download.
This code has been tested on Transformers v4.44.0, torch v2.4.0 and 2 A100 80GB GPUs, but any setup that supports ```meta-llama/Llama-3.1-70B-Instruct``` should support this model as well. If you run into problems, you can consider doing ```pip install -U transformers```.
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "nvidia/Llama-3.1-Nemotron-70B-Instruct-HF"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "How many r in strawberry?"
messages = [{"role": "user", "content": prompt}]
tokenized_message = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True)
response_token_ids = model.generate(tokenized_message['input_ids'].cuda(),attention_mask=tokenized_message['attention_mask'].cuda(), max_new_tokens=4096, pad_token_id = tokenizer.eos_token_id)
generated_tokens =response_token_ids[:, len(tokenized_message['input_ids'][0]):]
generated_text = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
print(generated_text)
# See response at top of model card
```
## Contact
E-Mail: [Zhilin Wang](mailto:[email protected])
## Citation
If you find this model useful, please cite the following works
```bibtex
@misc{wang2024helpsteer2preferencecomplementingratingspreferences,
title={HelpSteer2-Preference: Complementing Ratings with Preferences},
author={Zhilin Wang and Alexander Bukharin and Olivier Delalleau and Daniel Egert and Gerald Shen and Jiaqi Zeng and Oleksii Kuchaiev and Yi Dong},
year={2024},
eprint={2410.01257},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2410.01257},
}
@misc{wang2024helpsteer2,
title={HelpSteer2: Open-source dataset for training top-performing reward models},
author={Zhilin Wang and Yi Dong and Olivier Delalleau and Jiaqi Zeng and Gerald Shen and Daniel Egert and Jimmy J. Zhang and Makesh Narsimhan Sreedhar and Oleksii Kuchaiev},
year={2024},
eprint={2406.08673},
archivePrefix={arXiv},
primaryClass={id='cs.CL' full_name='Computation and Language' is_active=True alt_name='cmp-lg' in_archive='cs' is_general=False description='Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.'}
}
```
## References(s):
* [HelpSteer2-Preference](https://arxiv.org/abs/2410.01257)
* [SteerLM method](https://arxiv.org/abs/2310.05344)
* [HelpSteer](https://arxiv.org/abs/2311.09528)
* [HelpSteer2](https://arxiv.org/abs/2406.08673)
* [Introducing Llama 3.1: Our most capable models to date](https://ai.meta.com/blog/meta-llama-3-1/)
* [Meta's Llama 3.1 Webpage](https://www.llama.com/docs/model-cards-and-prompt-formats/llama3_1)
* [Meta's Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md)
## Model Architecture:
**Architecture Type:** Transformer <br>
**Network Architecture:** Llama 3.1 <br>
## Input:
**Input Type(s):** Text <br>
**Input Format:** String <br>
**Input Parameters:** One Dimensional (1D) <br>
**Other Properties Related to Input:** Max of 128k tokens<br>
## Output:
**Output Type(s):** Text <br>
**Output Format:** String <br>
**Output Parameters:** One Dimensional (1D) <br>
**Other Properties Related to Output:** Max of 4k tokens <br>
## Software Integration:
**Supported Hardware Microarchitecture Compatibility:** <br>
* NVIDIA Ampere <br>
* NVIDIA Hopper <br>
* NVIDIA Turing <br>
**Supported Operating System(s):** Linux <br>
## Model Version:
v1.0
# Training & Evaluation:
## Datasets:
**Data Collection Method by dataset** <br>
* [Hybrid: Human, Synthetic] <br>
**Labeling Method by dataset** <br>
* [Human] <br>
**Link:**
* [HelpSteer2](https://huggingface.co/datasets/nvidia/HelpSteer2)
**Properties (Quantity, Dataset Descriptions, Sensor(s)):** <br>
* 21, 362 prompt-responses built to make more models more aligned with human preference - specifically more helpful, factually-correct, coherent, and customizable based on complexity and verbosity.
* 20, 324 prompt-responses used for training and 1, 038 used for validation.
# Inference:
**Engine:** [Triton](https://developer.nvidia.com/triton-inference-server) <br>
**Test Hardware:** H100, A100 80GB, A100 40GB <br>
## Ethical Considerations:
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their supporting model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/). |
furrutiav/bert_ulra_bce_paper_ef_signal_asag | furrutiav | 2024-10-16T00:19:53Z | 104 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2024-10-16T00:19:21Z | ---
library_name: transformers
tags: []
---
# 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:**
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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. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
gglabs/Mistral-Nemo-FC-1013-3-epoch | gglabs | 2024-10-16T00:18:11Z | 5 | 0 | transformers | [
"transformers",
"gguf",
"mistral",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit",
"base_model:quantized:unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-15T23:55:53Z | ---
base_model: unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- gguf
---
# Uploaded model
- **Developed by:** gglabs
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Triangle104/Dans-PersonalityEngine-v1.0.0-8b-Q4_K_M-GGUF | Triangle104 | 2024-10-16T00:03:40Z | 7 | 0 | null | [
"gguf",
"chemistry",
"biology",
"code",
"climate",
"text-generation-inference",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"dataset:PocketDoc/Dans-MemoryCore-CoreCurriculum-Small",
"dataset:PocketDoc/Dans-Prosemaxx-Gutenberg",
"dataset:PocketDoc/Dans-Prosemaxx-Cowriter-S",
"dataset:PocketDoc/Dans-Prosemaxx-Adventure",
"dataset:PocketDoc/Dans-Prosemaxx-Opus-Writing",
"dataset:PocketDoc/Dans-Assistantmaxx-Sharegpt",
"dataset:PocketDoc/Dans-Assistantmaxx-OpenAssistant2",
"dataset:PocketDoc/Dans-Assistantmaxx-Opus-instruct-1",
"dataset:PocketDoc/Dans-Assistantmaxx-Opus-instruct-2",
"dataset:PocketDoc/Dans-Assistantmaxx-Opus-instruct-3",
"dataset:PocketDoc/Dans-Assistantmaxx-Opus-Multi-Instruct",
"dataset:PocketDoc/Dans-Assistantmaxx-sonnetorca-subset",
"dataset:PocketDoc/Dans-Assistantmaxx-NoRobots",
"dataset:AquaV/Energetic-Materials-Sharegpt",
"dataset:AquaV/Chemical-Biological-Safety-Applications-Sharegpt",
"dataset:AquaV/US-Army-Survival-Sharegpt",
"dataset:AquaV/Resistance-Sharegpt",
"dataset:AquaV/Interrogation-Sharegpt",
"dataset:AquaV/Multi-Environment-Operations-Sharegpt",
"dataset:PocketDoc/Dans-Mathmaxx",
"dataset:PJMixers/Math-Multiturn-1K-ShareGPT",
"dataset:PocketDoc/Dans-Benchmaxx",
"dataset:PocketDoc/Dans-Codemaxx-LeetCode",
"dataset:PocketDoc/Dans-Codemaxx-CodeFeedback-Conversations",
"dataset:PocketDoc/Dans-Codemaxx-CodeFeedback-SingleTurn",
"dataset:PocketDoc/Dans-Taskmaxx",
"dataset:PocketDoc/Dans-Taskmaxx-DataPrepper",
"dataset:PocketDoc/Dans-Taskmaxx-ConcurrentQA-Reworked",
"dataset:PocketDoc/Dans-Systemmaxx",
"dataset:PocketDoc/Dans-Toolmaxx-Agent",
"dataset:PocketDoc/Dans-Toolmaxx-ShellCommands",
"dataset:PocketDoc/Dans-ASCIIMaxx-Wordart",
"dataset:PocketDoc/Dans-Personamaxx",
"dataset:PocketDoc/DansTestYard",
"dataset:PocketDoc/Dans-Logicmaxx-Skunkworks",
"base_model:PocketDoc/Dans-PersonalityEngine-v1.0.0-8b",
"base_model:quantized:PocketDoc/Dans-PersonalityEngine-v1.0.0-8b",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2024-10-16T00:02:16Z | ---
base_model: PocketDoc/Dans-PersonalityEngine-v1.0.0-8b
datasets:
- PocketDoc/Dans-MemoryCore-CoreCurriculum-Small
- PocketDoc/Dans-Prosemaxx-Gutenberg
- PocketDoc/Dans-Prosemaxx-Cowriter-S
- PocketDoc/Dans-Prosemaxx-Adventure
- PocketDoc/Dans-Prosemaxx-Opus-Writing
- PocketDoc/Dans-Assistantmaxx-Sharegpt
- PocketDoc/Dans-Assistantmaxx-OpenAssistant2
- PocketDoc/Dans-Assistantmaxx-Opus-instruct-1
- PocketDoc/Dans-Assistantmaxx-Opus-instruct-2
- PocketDoc/Dans-Assistantmaxx-Opus-instruct-3
- PocketDoc/Dans-Assistantmaxx-Opus-Multi-Instruct
- PocketDoc/Dans-Assistantmaxx-sonnetorca-subset
- PocketDoc/Dans-Assistantmaxx-NoRobots
- AquaV/Energetic-Materials-Sharegpt
- AquaV/Chemical-Biological-Safety-Applications-Sharegpt
- AquaV/US-Army-Survival-Sharegpt
- AquaV/Resistance-Sharegpt
- AquaV/Interrogation-Sharegpt
- AquaV/Multi-Environment-Operations-Sharegpt
- PocketDoc/Dans-Mathmaxx
- PJMixers/Math-Multiturn-1K-ShareGPT
- PocketDoc/Dans-Benchmaxx
- PocketDoc/Dans-Codemaxx-LeetCode
- PocketDoc/Dans-Codemaxx-CodeFeedback-Conversations
- PocketDoc/Dans-Codemaxx-CodeFeedback-SingleTurn
- PocketDoc/Dans-Taskmaxx
- PocketDoc/Dans-Taskmaxx-DataPrepper
- PocketDoc/Dans-Taskmaxx-ConcurrentQA-Reworked
- PocketDoc/Dans-Systemmaxx
- PocketDoc/Dans-Toolmaxx-Agent
- PocketDoc/Dans-Toolmaxx-ShellCommands
- PocketDoc/Dans-ASCIIMaxx-Wordart
- PocketDoc/Dans-Personamaxx
- PocketDoc/DansTestYard
- PocketDoc/Dans-Logicmaxx-Skunkworks
language:
- en
license: apache-2.0
pipeline_tag: text-generation
tags:
- chemistry
- biology
- code
- climate
- text-generation-inference
- llama-cpp
- gguf-my-repo
---
# Triangle104/Dans-PersonalityEngine-v1.0.0-8b-Q4_K_M-GGUF
This model was converted to GGUF format from [`PocketDoc/Dans-PersonalityEngine-v1.0.0-8b`](https://huggingface.co/PocketDoc/Dans-PersonalityEngine-v1.0.0-8b) 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/PocketDoc/Dans-PersonalityEngine-v1.0.0-8b) for more details on the model.
---
Model details:
-
What is it?
This model is intended to be multifarious in its capabilities and should be quite capable at both co-writing and roleplay as well as find itself quite at home performing sentiment analysis or summarization as part of a pipeline. It has been trained on a wide array of one shot instructions, multi turn instructions, role playing scenarios, text adventure games, co-writing, and much more. The full dataset is publicly available and can be found in the datasets section of the model page.
There has not been any form of harmfulness alignment done on this model, please take the appropriate precautions when using it in a production environment.
Prompting
The model has been trained on standard "ChatML" format prompting, an example of which is shown below:
<|im_start|>system
system prompt<|im_end|>
<|im_start|>user
Hi there!<|im_end|>
<|im_start|>assistant
Nice to meet you!<|im_end|>
<|im_start|>user
Can I ask a question?<|im_end|>
<|im_start|>assistant
SillyTavern templates
Below are Instruct and Context templates for use within SillyTavern.
context template
{
"story_string": "<|im_start|>system\n{{#if system}}{{system}}\n{{/if}}{{#if wiBefore}}{{wiBefore}}\n{{/if}}{{#if description}}{{description}}\n{{/if}}{{#if personality}}{{char}}'s personality: {{personality}}\n{{/if}}{{#if scenario}}Scenario: {{scenario}}\n{{/if}}{{#if wiAfter}}{{wiAfter}}\n{{/if}}{{#if persona}}{{persona}}\n{{/if}}{{trim}}<|im_end|>\n",
"example_separator": "",
"chat_start": "",
"use_stop_strings": false,
"allow_jailbreak": false,
"always_force_name2": false,
"trim_sentences": false,
"include_newline": false,
"single_line": false,
"name": "Dan-ChatML"
}
instruct template
{
"system_prompt": "Write {{char}}'s actions and dialogue, user will write {{user}}'s.",
"input_sequence": "<|im_start|>user\n",
"output_sequence": "<|im_start|>assistant\n",
"first_output_sequence": "",
"last_output_sequence": "",
"system_sequence_prefix": "",
"system_sequence_suffix": "",
"stop_sequence": "<|im_end|>",
"wrap": false,
"macro": true,
"names": false,
"names_force_groups": false,
"activation_regex": "",
"skip_examples": false,
"output_suffix": "<|im_end|>\n",
"input_suffix": "<|im_end|>\n",
"system_sequence": "<|im_start|>system\n",
"system_suffix": "<|im_end|>\n",
"user_alignment_message": "",
"last_system_sequence": "",
"system_same_as_user": false,
"first_input_sequence": "",
"last_input_sequence": "",
"name": "Dan-ChatML"
}
Training
This model was full finetuned for 4 epochs on 8x H100 equating to 21 hours.
---
## 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 Triangle104/Dans-PersonalityEngine-v1.0.0-8b-Q4_K_M-GGUF --hf-file dans-personalityengine-v1.0.0-8b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Dans-PersonalityEngine-v1.0.0-8b-Q4_K_M-GGUF --hf-file dans-personalityengine-v1.0.0-8b-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 Triangle104/Dans-PersonalityEngine-v1.0.0-8b-Q4_K_M-GGUF --hf-file dans-personalityengine-v1.0.0-8b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Dans-PersonalityEngine-v1.0.0-8b-Q4_K_M-GGUF --hf-file dans-personalityengine-v1.0.0-8b-q4_k_m.gguf -c 2048
```
|
RichardErkhov/matlok_-_tinyllama-cinder-openhermes-32k-gguf | RichardErkhov | 2024-10-15T23:58:55Z | 5 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us"
] | null | 2024-10-15T23:29:26Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
tinyllama-cinder-openhermes-32k - GGUF
- Model creator: https://huggingface.co/matlok/
- Original model: https://huggingface.co/matlok/tinyllama-cinder-openhermes-32k/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [tinyllama-cinder-openhermes-32k.Q2_K.gguf](https://huggingface.co/RichardErkhov/matlok_-_tinyllama-cinder-openhermes-32k-gguf/blob/main/tinyllama-cinder-openhermes-32k.Q2_K.gguf) | Q2_K | 0.4GB |
| [tinyllama-cinder-openhermes-32k.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/matlok_-_tinyllama-cinder-openhermes-32k-gguf/blob/main/tinyllama-cinder-openhermes-32k.IQ3_XS.gguf) | IQ3_XS | 0.44GB |
| [tinyllama-cinder-openhermes-32k.IQ3_S.gguf](https://huggingface.co/RichardErkhov/matlok_-_tinyllama-cinder-openhermes-32k-gguf/blob/main/tinyllama-cinder-openhermes-32k.IQ3_S.gguf) | IQ3_S | 0.47GB |
| [tinyllama-cinder-openhermes-32k.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/matlok_-_tinyllama-cinder-openhermes-32k-gguf/blob/main/tinyllama-cinder-openhermes-32k.Q3_K_S.gguf) | Q3_K_S | 0.47GB |
| [tinyllama-cinder-openhermes-32k.IQ3_M.gguf](https://huggingface.co/RichardErkhov/matlok_-_tinyllama-cinder-openhermes-32k-gguf/blob/main/tinyllama-cinder-openhermes-32k.IQ3_M.gguf) | IQ3_M | 0.48GB |
| [tinyllama-cinder-openhermes-32k.Q3_K.gguf](https://huggingface.co/RichardErkhov/matlok_-_tinyllama-cinder-openhermes-32k-gguf/blob/main/tinyllama-cinder-openhermes-32k.Q3_K.gguf) | Q3_K | 0.51GB |
| [tinyllama-cinder-openhermes-32k.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/matlok_-_tinyllama-cinder-openhermes-32k-gguf/blob/main/tinyllama-cinder-openhermes-32k.Q3_K_M.gguf) | Q3_K_M | 0.51GB |
| [tinyllama-cinder-openhermes-32k.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/matlok_-_tinyllama-cinder-openhermes-32k-gguf/blob/main/tinyllama-cinder-openhermes-32k.Q3_K_L.gguf) | Q3_K_L | 0.55GB |
| [tinyllama-cinder-openhermes-32k.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/matlok_-_tinyllama-cinder-openhermes-32k-gguf/blob/main/tinyllama-cinder-openhermes-32k.IQ4_XS.gguf) | IQ4_XS | 0.57GB |
| [tinyllama-cinder-openhermes-32k.Q4_0.gguf](https://huggingface.co/RichardErkhov/matlok_-_tinyllama-cinder-openhermes-32k-gguf/blob/main/tinyllama-cinder-openhermes-32k.Q4_0.gguf) | Q4_0 | 0.59GB |
| [tinyllama-cinder-openhermes-32k.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/matlok_-_tinyllama-cinder-openhermes-32k-gguf/blob/main/tinyllama-cinder-openhermes-32k.IQ4_NL.gguf) | IQ4_NL | 0.6GB |
| [tinyllama-cinder-openhermes-32k.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/matlok_-_tinyllama-cinder-openhermes-32k-gguf/blob/main/tinyllama-cinder-openhermes-32k.Q4_K_S.gguf) | Q4_K_S | 0.6GB |
| [tinyllama-cinder-openhermes-32k.Q4_K.gguf](https://huggingface.co/RichardErkhov/matlok_-_tinyllama-cinder-openhermes-32k-gguf/blob/main/tinyllama-cinder-openhermes-32k.Q4_K.gguf) | Q4_K | 0.62GB |
| [tinyllama-cinder-openhermes-32k.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/matlok_-_tinyllama-cinder-openhermes-32k-gguf/blob/main/tinyllama-cinder-openhermes-32k.Q4_K_M.gguf) | Q4_K_M | 0.62GB |
| [tinyllama-cinder-openhermes-32k.Q4_1.gguf](https://huggingface.co/RichardErkhov/matlok_-_tinyllama-cinder-openhermes-32k-gguf/blob/main/tinyllama-cinder-openhermes-32k.Q4_1.gguf) | Q4_1 | 0.65GB |
| [tinyllama-cinder-openhermes-32k.Q5_0.gguf](https://huggingface.co/RichardErkhov/matlok_-_tinyllama-cinder-openhermes-32k-gguf/blob/main/tinyllama-cinder-openhermes-32k.Q5_0.gguf) | Q5_0 | 0.71GB |
| [tinyllama-cinder-openhermes-32k.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/matlok_-_tinyllama-cinder-openhermes-32k-gguf/blob/main/tinyllama-cinder-openhermes-32k.Q5_K_S.gguf) | Q5_K_S | 0.71GB |
| [tinyllama-cinder-openhermes-32k.Q5_K.gguf](https://huggingface.co/RichardErkhov/matlok_-_tinyllama-cinder-openhermes-32k-gguf/blob/main/tinyllama-cinder-openhermes-32k.Q5_K.gguf) | Q5_K | 0.73GB |
| [tinyllama-cinder-openhermes-32k.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/matlok_-_tinyllama-cinder-openhermes-32k-gguf/blob/main/tinyllama-cinder-openhermes-32k.Q5_K_M.gguf) | Q5_K_M | 0.73GB |
| [tinyllama-cinder-openhermes-32k.Q5_1.gguf](https://huggingface.co/RichardErkhov/matlok_-_tinyllama-cinder-openhermes-32k-gguf/blob/main/tinyllama-cinder-openhermes-32k.Q5_1.gguf) | Q5_1 | 0.77GB |
| [tinyllama-cinder-openhermes-32k.Q6_K.gguf](https://huggingface.co/RichardErkhov/matlok_-_tinyllama-cinder-openhermes-32k-gguf/blob/main/tinyllama-cinder-openhermes-32k.Q6_K.gguf) | Q6_K | 0.84GB |
| [tinyllama-cinder-openhermes-32k.Q8_0.gguf](https://huggingface.co/RichardErkhov/matlok_-_tinyllama-cinder-openhermes-32k-gguf/blob/main/tinyllama-cinder-openhermes-32k.Q8_0.gguf) | Q8_0 | 1.09GB |
Original model description:
---
license: unknown
---
## Merging AI Models like Lego Blocks
This model was merged with the following Hugging Face TinyLlama models using ties:
- TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
- Doctor-Shotgun/TinyLlama-1.1B-32k-Instruct
- Doctor-Shotgun/TinyLlama-1.1B-32k
- Tensoic/TinyLlama-1.1B-3T-openhermes
- Josephgflowers/TinyLlama-3T-Cinder-v1.3
## How do I fine-tune this model?
### Fine-tuning using Hugging Face SFTTrainer
- [Fine-tuning using Hugging Face SFTTrainer](https://colab.research.google.com/drive/1PEQyJO1-f6j0S_XJ8DV50NkpzasXkrzd?usp=sharing)
### Fine-tuning using Unsloth
2024-02-07 was unable to use unsloth due to pip install issues. Maybe others in the future will have more luck:
- [Alpaca + TinyLlama + RoPE Scaling full example.ipynb](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing)
## How do I generate my own model merges?
This requires setting up your [Hugging Face User Account Access Tokens](https://huggingface.co/settings/tokens) before it will work:
If you're using the command line you can use:
```sh
huggingface-cli login
```
```sh
time ./run-tiny-merge.py
```
### What's this code doing?
Here's the latest version:
```python3
#!/usr/bin/env python3
import os
import transformers
import torch
import logging
from ddare.merge import merge_tensors
from ddare.tensor import (
dare_ties_sparsification,
relative_norm,
divide_tensor_into_sets,
)
from ddare.util import get_device
import re
from typing import Dict, Tuple, List
logging.basicConfig(level=logging.INFO)
log = logging.getLogger(__name__)
def get_models(
models: List[str],
trust_remote_code: bool,
):
"""
get the models
:param models: model names to download
:param trust_remote_code: are you sure??? True/False
"""
config = {
"torch_dtype": torch.float16,
"low_cpu_mem_usage": False,
"trust_remote_code": trust_remote_code,
}
loaded_models = []
num_models = len(models)
for midx, model_path in enumerate(models):
log.info(
f"loading model={midx + 1}/{num_models} "
f"model={model_path} "
)
loaded_models.append(
transformers.AutoModelForCausalLM.from_pretrained(
model_path, **config
)
)
return loaded_models
def pm(
model,
):
"""
pretty print model
:param model: show me the model
"""
keys = model.state_dict().keys()
log.info(f"model keys={len(keys)}")
for i, k in enumerate(keys):
tensor = model.state_dict()[k]
log.info(
f"{i:3d} {k} shape={tensor.shape} "
f"type={tensor.dtype} dev={tensor.device} "
f"contig={tensor.is_contiguous()}"
)
def run_text_test(
model,
tokenizer_path: str,
question: str,
device: str = "cuda",
):
"""
run a question on the model and return the answer
:param model: initialized model
:param tokenizer_path: tokenizer path/name
:param question: what are you asking?
:param device: where do you want to run "cpu"/"gpu"?
"""
base_model = model.to(device)
log.info(f"loading tokenizer={tokenizer_path}")
tokenizer = transformers.AutoTokenizer.from_pretrained(
tokenizer_path,
torch_dtype=torch.float16,
)
inputs = tokenizer(question, return_tensors="pt").to(
device
)
with torch.backends.cuda.sdp_kernel(
enable_flash=True,
enable_math=False,
enable_mem_efficient=True,
):
outputs = base_model.generate(
**inputs,
max_new_tokens=256,
)
answer = tokenizer.decode(
outputs[0], skip_special_tokens=True
)
log.info(
"\n"
"----------"
"\n"
f"tokenizer={tokenizer}\n "
f"question:\n{question}\n"
f"answer:\n{answer}\n"
"----------"
)
base_model = base_model.to(device)
return tokenizer
def get_layer_type(key: str) -> Tuple[int, str]:
"""
get the layer type
:param key: name of the layer
:return: layer id and name
"""
matcher = re.compile(r"model.layers.(\d+).(.+)")
m = matcher.match(key)
if m is None:
if "model.norm.weight" == key:
return -1, "norm"
if "model.embed_tokens.weight" == key:
return -1, "embed"
if "lm_head.weight" == key:
return -1, "head"
log.info(f"Unknown key {key}")
return -1, "unknown"
return int(m.group(1)), m.group(2)
def merge_model_with_ties(
models: List[str],
model_dst: str,
trust_remote_code: bool = True,
):
"""
merge the list of models into one model
called model_dst
:param models: list of models to merge
:param model_dst: name of the new model
:param trust_remote_code: are you sure? True/False
"""
models = get_models(
models=models,
trust_remote_code=trust_remote_code,
)
config = {}
result_dict: Dict[str, torch.Tensor] = {}
device = get_device()
keys = models[0].state_dict().keys()
num_keys = len(keys)
for k in keys:
block, layer_type = get_layer_type(k)
m0: torch.Tensor = models[0].state_dict()[k]
result = m0.clone()
sets = divide_tensor_into_sets(tensor=m0, n_sets=4)
# get the src layers to merge
m = [
models[1].state_dict()[k],
models[2].state_dict()[k],
models[3].state_dict()[k],
models[4].state_dict()[k],
]
# build a ratio
ratio = {
"to_q": 0.0,
"to_k": 0.0,
"to_v": 0.0,
}.get(layer_type, 0.5)
norm_ratio = 0.68
log.info(
f"model={k} {num_keys} shape={m0.shape} "
f"dtype={m0.dtype} {m0.device} "
f"ratio={ratio} "
f"contig={m0.is_contiguous()} "
f"norm={norm_ratio}"
)
# for all tensors
for i, tensor in enumerate(m):
if layer_type == "to_k":
# Get to_q key
q_base = models[0].state_dict()[
k.replace("to_k", "to_q")
]
q_merge = models[i].state_dict()[
k.replace("to_k", "to_q")
]
scale = relative_norm(q_merge, q_base)
tensor = tensor.to(device) / scale
del scale
elif layer_type == "to_q":
scale = relative_norm(tensor, m0)
tensor = tensor.to(device) * scale
del scale
slice_mask = (sets == i).bool()
new_tensor = dare_ties_sparsification(
model_a_param=m0,
model_b_param=tensor,
drop_rate=norm_ratio,
ties="sum",
rescale="off",
device=device,
**config,
)
new_tensor = merge_tensors(
"slerp", m0, tensor, ratio
)
result = torch.where(
slice_mask, new_tensor, result
)
del new_tensor, slice_mask
result_dict[k] = result
# end of merge
log.info(f"done merge saving to file: {model_dst}")
out_model = (
transformers.AutoModelForCausalLM.from_pretrained(
model_dst, **config
)
)
out_model.state_dict = lambda: result_dict
out_model.save_pretrained(model_dst)
def run():
"""
run the merge and upload the model and tokenizer
This requires having the Hugging Face token
set before it will work:
```huggingface-cli login```
"""
question = "why is the sky blue?"
log.info(
f"merging models and asking the question: {question}"
)
model_src = "TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T"
model_dst = "matlok/tinyllama-cinder-openhermes-32k"
device = "cuda"
config = {
"torch_dtype": torch.float16,
"low_cpu_mem_usage": False,
"trust_remote_code": True,
}
models = [
model_src,
"Doctor-Shotgun/TinyLlama-1.1B-32k-Instruct",
"Doctor-Shotgun/TinyLlama-1.1B-32k",
"Tensoic/TinyLlama-1.1B-3T-openhermes",
"Josephgflowers/TinyLlama-3T-Cinder-v1.3",
]
merge_model_with_ties(
models=models, model_dst=model_dst
)
log.info(f"loading newly-created file: {model_dst}")
model = (
transformers.AutoModelForCausalLM.from_pretrained(
model_dst, **config
)
)
log.info(
f"loaded new model file: {model_dst} "
f"asking question: {question} "
)
run_text_test(
model=model,
tokenizer_path=model_src,
question=question,
device=device,
)
# clean the temp merge dir
# remove model dir to prevent issues with the tokenizer upload
model_org = model_dst.split("/")[0]
if os.path.exists(model_org):
os.system(f"rm -rf ./{model_org}")
log.info(f"uploading model: {model_dst}")
model.push_to_hub(model_dst)
log.info(f"uploading src tokenizer: {model_src}")
# reload tokenizer to save it and found on:
# https://colab.research.google.com/drive/1PEQyJO1-f6j0S_XJ8DV50NkpzasXkrzd?usp=sharing#scrollTo=QQn30cRtAZ-P
tokenizer = transformers.AutoTokenizer.from_pretrained(
model_src, trust_remote_code=True
)
# https://huggingface.co/docs/transformers/model_sharing#use-the-pushtohub-function
# tokenizer.push_to_hub("my-awesome-model")
tokenizer.push_to_hub(model_dst)
log.info(
f"done loading new model: {model} "
f"file: {model_dst}"
)
if __name__ == "__main__":
run()
```
### Logs
Here's the logs from the code above:
```
time ./run-tiny-merge.py
Total VRAM 12282 MB, total RAM 85434 MB
Set vram state to: NORMAL_VRAM
Device: cuda:0 NVIDIA GeForce RTX 4070 Ti : native
VAE dtype: torch.bfloat16
INFO:__main__:merging models and asking the question: why is the sky blue?
INFO:__main__:loading model=1/5 model=TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
config.json: 100%|█████████████████████████████████████| 560/560 [00:00<00:00, 5.23MB/s]
model.safetensors: 100%|███████████████████████████| 4.40G/4.40G [00:48<00:00, 90.2MB/s]
generation_config.json: 100%|███████████████████████████| 129/129 [00:00<00:00, 721kB/s]
INFO:__main__:loading model=2/5 model=Doctor-Shotgun/TinyLlama-1.1B-32k-Instruct
config.json: 100%|█████████████████████████████████████| 695/695 [00:00<00:00, 3.04MB/s]
pytorch_model.bin: 100%|███████████████████████████| 2.20G/2.20G [00:23<00:00, 92.6MB/s]
generation_config.json: 100%|███████████████████████████| 129/129 [00:00<00:00, 566kB/s]
INFO:__main__:loading model=3/5 model=Doctor-Shotgun/TinyLlama-1.1B-32k
config.json: 100%|█████████████████████████████████████| 686/686 [00:00<00:00, 3.57MB/s]
model.safetensors: 100%|███████████████████████████| 2.20G/2.20G [00:24<00:00, 90.5MB/s]
generation_config.json: 100%|██████████████████████████| 124/124 [00:00<00:00, 1.80MB/s]
INFO:__main__:loading model=4/5 model=Tensoic/TinyLlama-1.1B-3T-openhermes
config.json: 100%|█████████████████████████████████████| 702/702 [00:00<00:00, 2.97MB/s]
pytorch_model.bin: 100%|███████████████████████████| 2.20G/2.20G [00:23<00:00, 92.7MB/s]
generation_config.json: 100%|███████████████████████████| 124/124 [00:00<00:00, 671kB/s]
INFO:__main__:loading model=5/5 model=Josephgflowers/TinyLlama-3T-Cinder-v1.3
config.json: 100%|█████████████████████████████████████| 713/713 [00:00<00:00, 9.35MB/s]
model.safetensors: 100%|███████████████████████████| 2.20G/2.20G [00:24<00:00, 91.5MB/s]
generation_config.json: 100%|██████████████████████████| 138/138 [00:00<00:00, 1.86MB/s]
INFO:__main__:model=model.embed_tokens.weight 201 shape=torch.Size([32000, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.0.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.0.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.0.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.0.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.0.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.0.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.0.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.0.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.0.post_attention_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.1.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.1.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.1.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.1.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.1.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.1.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.1.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.1.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.1.post_attention_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.2.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.2.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.2.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.2.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.2.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.2.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.2.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.2.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.2.post_attention_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.3.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.3.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.3.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.3.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.3.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.3.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.3.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.3.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.3.post_attention_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.4.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.4.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.4.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.4.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.4.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.4.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.4.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.4.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.4.post_attention_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.5.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.5.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.5.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.5.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.5.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.5.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.5.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.5.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.5.post_attention_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.6.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.6.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.6.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.6.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.6.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.6.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.6.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.6.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.6.post_attention_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.7.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.7.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.7.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.7.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.7.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.7.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.7.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.7.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.7.post_attention_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.8.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.8.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.8.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.8.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.8.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.8.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.8.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.8.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.8.post_attention_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.9.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.9.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.9.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.9.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.9.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.9.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.9.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.9.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.9.post_attention_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.10.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.10.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.10.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.10.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.10.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
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INFO:__main__:model=model.layers.10.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
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INFO:__main__:model=model.layers.11.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.11.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.11.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.11.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.11.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
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INFO:__main__:model=model.layers.11.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
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INFO:__main__:model=model.layers.12.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.12.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.12.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.12.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.12.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.12.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.12.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.12.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
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INFO:__main__:model=model.layers.13.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.13.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.13.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.13.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.13.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.13.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.13.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.13.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
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INFO:__main__:model=model.layers.14.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.14.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.14.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.14.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.14.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
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INFO:__main__:model=model.layers.14.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
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INFO:__main__:model=model.layers.15.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.15.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.15.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.15.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.15.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.15.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.15.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.15.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
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INFO:__main__:model=model.layers.16.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.16.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.16.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.16.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.16.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
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INFO:__main__:model=model.layers.16.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
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INFO:__main__:model=model.layers.17.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
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INFO:__main__:model=model.layers.17.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.17.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.17.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
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INFO:__main__:model=model.layers.18.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.18.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.18.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.18.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.18.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.18.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.18.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.18.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.18.post_attention_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.19.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.19.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.19.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.19.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.19.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.19.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.19.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.19.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.19.post_attention_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.20.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.20.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.20.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.20.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.20.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.20.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.20.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.20.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.20.post_attention_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.21.self_attn.q_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.21.self_attn.k_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.21.self_attn.v_proj.weight 201 shape=torch.Size([256, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.21.self_attn.o_proj.weight 201 shape=torch.Size([2048, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.21.mlp.gate_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.21.mlp.up_proj.weight 201 shape=torch.Size([5632, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.21.mlp.down_proj.weight 201 shape=torch.Size([2048, 5632]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.21.input_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.layers.21.post_attention_layernorm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=model.norm.weight 201 shape=torch.Size([2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:model=lm_head.weight 201 shape=torch.Size([32000, 2048]) dtype=torch.float16 cpu ratio=0.5 contig=True norm=0.68
INFO:__main__:done merge saving to file: matlok/tinyllama-cinder-openhermes-32k
config.json: 100%|█████████████████████████████████████| 724/724 [00:00<00:00, 7.75MB/s]
model.safetensors: 100%|███████████████████████████| 2.20G/2.20G [00:23<00:00, 91.8MB/s]
generation_config.json: 100%|██████████████████████████| 133/133 [00:00<00:00, 1.58MB/s]
INFO:__main__:loading newly-created file: matlok/tinyllama-cinder-openhermes-32k
INFO:__main__:loaded new model file: matlok/tinyllama-cinder-openhermes-32k asking question: why is the sky blue?
INFO:__main__:loading tokenizer=TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
tokenizer_config.json: 100%|███████████████████████████| 776/776 [00:00<00:00, 8.26MB/s]
tokenizer.model: 100%|███████████████████████████████| 500k/500k [00:00<00:00, 64.6MB/s]
tokenizer.json: 100%|██████████████████████████████| 1.84M/1.84M [00:01<00:00, 1.57MB/s]
special_tokens_map.json: 100%|█████████████████████████| 414/414 [00:00<00:00, 2.47MB/s]
Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.
INFO:__main__:
----------
tokenizer=LlamaTokenizerFast(name_or_path='TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T', vocab_size=32000, model_max_length=1000000000000000019884624838656, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'bos_token': '<s>', 'eos_token': '</s>', 'unk_token': '<unk>'}, clean_up_tokenization_spaces=False), added_tokens_decoder={
0: AddedToken("<unk>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
1: AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
2: AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
}
question:
why is the sky blue?
answer:
why is the sky blue?
Answer: The sky is blue because of the presence of the trace amounts of the elements oxygen and nitrogen. These elements are present in the atmosphere in very small amounts. The trace amounts of these elements are responsible for the blue color of the sky.
Why is the sky blue?
Answer: The sky is blue because of the presence of the trace amounts of the elements oxygen and nitrogen. These elements are present in the atmosphere in very small amounts. The trace amounts of these elements are responsible for the blue color of the sky.
Why is the sky blue?
Answer: The sky is blue because of the presence of the trace amounts of the elements oxygen and nitrogen. These elements are present in the atmosphere in very small amounts. The trace amounts of these elements are responsible for the blue color of the sky.
Why is the sky blue?
Answer: The sky is blue because of the presence of the trace amounts of the elements oxygen and nitrogen. These elements are present in the atmosphere in very small amounts. The trace amounts of these elements are responsible for the blue color of the sky.
Why is the sky blue?
Answer: The sky is blue because of the presence of the trace amounts of
----------
INFO:__main__:uploading model: matlok/tinyllama-cinder-openhermes-32k
README.md: 100%|████████████████████████████████████| 45.6k/45.6k [00:00<00:00, 297MB/s]
model.safetensors: 100%|███████████████████████████| 2.20G/2.20G [01:18<00:00, 28.0MB/s]
INFO:__main__:uploading src tokenizer: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
INFO:__main__:done loading new model: LlamaForCausalLM(
(model): LlamaModel(
(embed_tokens): Embedding(32000, 2048)
(layers): ModuleList(
(0-21): 22 x LlamaDecoderLayer(
(self_attn): LlamaSdpaAttention(
(q_proj): Linear(in_features=2048, out_features=2048, bias=False)
(k_proj): Linear(in_features=2048, out_features=256, bias=False)
(v_proj): Linear(in_features=2048, out_features=256, bias=False)
(o_proj): Linear(in_features=2048, out_features=2048, bias=False)
(rotary_emb): LlamaRotaryEmbedding()
)
(mlp): LlamaMLP(
(gate_proj): Linear(in_features=2048, out_features=5632, bias=False)
(up_proj): Linear(in_features=2048, out_features=5632, bias=False)
(down_proj): Linear(in_features=5632, out_features=2048, bias=False)
(act_fn): SiLU()
)
(input_layernorm): LlamaRMSNorm()
(post_attention_layernorm): LlamaRMSNorm()
)
)
(norm): LlamaRMSNorm()
)
(lm_head): Linear(in_features=2048, out_features=32000, bias=False)
) file: matlok/tinyllama-cinder-openhermes-32k
real 4m44.626s
user 2m54.434s
sys 0m25.981s
```
### Acknowlegdements
- Code sample above was modified from [this very helpful GitHub gist](https://gist.github.com/maldevide/08829eada04ad9bd78e46c1a3787d42b)
- [Fine tuning example](https://colab.research.google.com/drive/1PEQyJO1-f6j0S_XJ8DV50NkpzasXkrzd?usp=sharing)
- [CodeLlama example](https://huggingface.co/collections/mlabonne/codellama-6509bc68c2d4c8fc379ee87f)
|
RichardErkhov/raincandy-u_-_TinyStories-656K-gguf | RichardErkhov | 2024-10-15T23:53:55Z | 5 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us"
] | null | 2024-10-15T23:53:07Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
TinyStories-656K - GGUF
- Model creator: https://huggingface.co/raincandy-u/
- Original model: https://huggingface.co/raincandy-u/TinyStories-656K/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [TinyStories-656K.Q2_K.gguf](https://huggingface.co/RichardErkhov/raincandy-u_-_TinyStories-656K-gguf/blob/main/TinyStories-656K.Q2_K.gguf) | Q2_K | 0.0GB |
| [TinyStories-656K.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/raincandy-u_-_TinyStories-656K-gguf/blob/main/TinyStories-656K.IQ3_XS.gguf) | IQ3_XS | 0.0GB |
| [TinyStories-656K.IQ3_S.gguf](https://huggingface.co/RichardErkhov/raincandy-u_-_TinyStories-656K-gguf/blob/main/TinyStories-656K.IQ3_S.gguf) | IQ3_S | 0.0GB |
| [TinyStories-656K.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/raincandy-u_-_TinyStories-656K-gguf/blob/main/TinyStories-656K.Q3_K_S.gguf) | Q3_K_S | 0.0GB |
| [TinyStories-656K.IQ3_M.gguf](https://huggingface.co/RichardErkhov/raincandy-u_-_TinyStories-656K-gguf/blob/main/TinyStories-656K.IQ3_M.gguf) | IQ3_M | 0.0GB |
| [TinyStories-656K.Q3_K.gguf](https://huggingface.co/RichardErkhov/raincandy-u_-_TinyStories-656K-gguf/blob/main/TinyStories-656K.Q3_K.gguf) | Q3_K | 0.0GB |
| [TinyStories-656K.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/raincandy-u_-_TinyStories-656K-gguf/blob/main/TinyStories-656K.Q3_K_M.gguf) | Q3_K_M | 0.0GB |
| [TinyStories-656K.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/raincandy-u_-_TinyStories-656K-gguf/blob/main/TinyStories-656K.Q3_K_L.gguf) | Q3_K_L | 0.0GB |
| [TinyStories-656K.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/raincandy-u_-_TinyStories-656K-gguf/blob/main/TinyStories-656K.IQ4_XS.gguf) | IQ4_XS | 0.0GB |
| [TinyStories-656K.Q4_0.gguf](https://huggingface.co/RichardErkhov/raincandy-u_-_TinyStories-656K-gguf/blob/main/TinyStories-656K.Q4_0.gguf) | Q4_0 | 0.0GB |
| [TinyStories-656K.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/raincandy-u_-_TinyStories-656K-gguf/blob/main/TinyStories-656K.IQ4_NL.gguf) | IQ4_NL | 0.0GB |
| [TinyStories-656K.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/raincandy-u_-_TinyStories-656K-gguf/blob/main/TinyStories-656K.Q4_K_S.gguf) | Q4_K_S | 0.0GB |
| [TinyStories-656K.Q4_K.gguf](https://huggingface.co/RichardErkhov/raincandy-u_-_TinyStories-656K-gguf/blob/main/TinyStories-656K.Q4_K.gguf) | Q4_K | 0.0GB |
| [TinyStories-656K.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/raincandy-u_-_TinyStories-656K-gguf/blob/main/TinyStories-656K.Q4_K_M.gguf) | Q4_K_M | 0.0GB |
| [TinyStories-656K.Q4_1.gguf](https://huggingface.co/RichardErkhov/raincandy-u_-_TinyStories-656K-gguf/blob/main/TinyStories-656K.Q4_1.gguf) | Q4_1 | 0.0GB |
| [TinyStories-656K.Q5_0.gguf](https://huggingface.co/RichardErkhov/raincandy-u_-_TinyStories-656K-gguf/blob/main/TinyStories-656K.Q5_0.gguf) | Q5_0 | 0.0GB |
| [TinyStories-656K.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/raincandy-u_-_TinyStories-656K-gguf/blob/main/TinyStories-656K.Q5_K_S.gguf) | Q5_K_S | 0.0GB |
| [TinyStories-656K.Q5_K.gguf](https://huggingface.co/RichardErkhov/raincandy-u_-_TinyStories-656K-gguf/blob/main/TinyStories-656K.Q5_K.gguf) | Q5_K | 0.0GB |
| [TinyStories-656K.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/raincandy-u_-_TinyStories-656K-gguf/blob/main/TinyStories-656K.Q5_K_M.gguf) | Q5_K_M | 0.0GB |
| [TinyStories-656K.Q5_1.gguf](https://huggingface.co/RichardErkhov/raincandy-u_-_TinyStories-656K-gguf/blob/main/TinyStories-656K.Q5_1.gguf) | Q5_1 | 0.0GB |
| [TinyStories-656K.Q6_K.gguf](https://huggingface.co/RichardErkhov/raincandy-u_-_TinyStories-656K-gguf/blob/main/TinyStories-656K.Q6_K.gguf) | Q6_K | 0.0GB |
| [TinyStories-656K.Q8_0.gguf](https://huggingface.co/RichardErkhov/raincandy-u_-_TinyStories-656K-gguf/blob/main/TinyStories-656K.Q8_0.gguf) | Q8_0 | 0.0GB |
Original model description:
---
license: apache-2.0
widget:
- text: '<|start_story|>Once upon a time, there was a little boy named Tim. Tim '
example_title: Sample 1
datasets:
- raincandy-u/TinyStoriesV2_SpecialTokens
language:
- en
library_name: transformers
---
# TinyStories-656K
This is a LM trained from scratch on TinyStoriesV2 dataset.
Aims to be a transformer language model capable of generating story with only 600k~ of parameters.
- Llama Architecture
- GQA
- hidden_size = 128
- Use tie_word_embeddings
- vocab_size=2048 (Trained on TinystoriesV2 from scratch, using BPE)
- 2 Transformers Layers
Code: [Here](https://github.com/Ce-daros/Tinystory-LM)
## Full Training Arguments
```
training_args = TrainingArguments(
do_train=True,
per_device_train_batch_size=16,
gradient_accumulation_steps=1,
learning_rate=0.004629403549377777,
lr_scheduler_type="constant",
bf16=True,
logging_steps=5,
num_train_epochs=2,
save_steps=10000000,
seed=3407,report_to=None
)
```
# Generation
Template:
```
<|start_story|>Once upon a time,
```
Generation example:
```
Once upon a time, there was a little boy named Tim. Tim had a toy car that he loved to play with. One day, he went to the park with his mom. Tim saw a toy car on the ground. Tim wanted to play with the car to his mom and said, "Mom, can I play with your car with my car too?"
His mom said, "Yes, but we must not take turns." Tim felt sad, but he knew he had to go. He asked his mom for help. His mom said, "Okay, let's clean it together." They went to play together and played the toy car. They had a lot of fun.
After they finished the car together, Tim and his mom were surprised. They did not know that the car was not a toy car like it was a magic car. Tim had an idea. He put the car in the car and put the car on it. He pushed the car on the car on the car car and pulled it down. Tim was so happy. He played with the car with his car all day long, and Tim was very happy.<|end_story|>
```
Recommended generation config:
```
do_sample=True,
top_k=40,
top_p=0.9,
temperature=0.6
```
|
Triangle104/MSM-MS-Cydrion-22B-Q8_0-GGUF | Triangle104 | 2024-10-15T23:53:19Z | 6 | 0 | transformers | [
"transformers",
"gguf",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:Steelskull/MSM-MS-Cydrion-22B",
"base_model:quantized:Steelskull/MSM-MS-Cydrion-22B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-15T23:50:20Z | ---
base_model: Steelskull/MSM-MS-Cydrion-22B
library_name: transformers
license: apache-2.0
tags:
- merge
- llama-cpp
- gguf-my-repo
---
# Triangle104/MSM-MS-Cydrion-22B-Q8_0-GGUF
This model was converted to GGUF format from [`Steelskull/MSM-MS-Cydrion-22B`](https://huggingface.co/Steelskull/MSM-MS-Cydrion-22B) 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/Steelskull/MSM-MS-Cydrion-22B) for more details on the model.
---
Model details:
-
Meet Cydrion, the attempt of fusion for creativity and intelligence.
Creator: SteelSkull
About Cydrion-22B:
Name Legend:
MSM = Mistral-Small
MS = Model Stock
22b = its 22b
This model merges the robust storytelling of Cydonia with the creative edge of Acolyte, ArliAI-RPMax, and Gutenberg with some special sauce.
Use Mistral Format
Quants:
My Quants:MSM-MS-Cydrion-22B-Q6_K-GGUF
Config:
MODEL_NAME = "MSM-MS-Cydrion-22B"
yaml_config = """
base_model: Steelskull/Merged-v2
merge_method: model_stock
dtype: bfloat16
models:
- model: TheDrummer/Cydonia-22B-v1.1
- model: ArliAI/Mistral-Small-22B-ArliAI-RPMax-v1.1
- model: nbeerbower/Mistral-Small-Gutenberg-Doppel-22B
- model: rAIfle/Acolyte-22B
"""
---
## 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 Triangle104/MSM-MS-Cydrion-22B-Q8_0-GGUF --hf-file msm-ms-cydrion-22b-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/MSM-MS-Cydrion-22B-Q8_0-GGUF --hf-file msm-ms-cydrion-22b-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 Triangle104/MSM-MS-Cydrion-22B-Q8_0-GGUF --hf-file msm-ms-cydrion-22b-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/MSM-MS-Cydrion-22B-Q8_0-GGUF --hf-file msm-ms-cydrion-22b-q8_0.gguf -c 2048
```
|
Pearush/phi_moe_75 | Pearush | 2024-10-15T23:53:00Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"phimoe",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] | text-generation | 2024-10-15T21:37:00Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
### Model Description
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mav23/OpenHermes-2.5-Mistral-7B-GGUF | mav23 | 2024-10-15T23:42:29Z | 122 | 0 | null | [
"gguf",
"mistral",
"instruct",
"finetune",
"chatml",
"gpt4",
"synthetic data",
"distillation",
"en",
"dataset:teknium/OpenHermes-2.5",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:quantized:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-15T23:01:06Z | ---
base_model: mistralai/Mistral-7B-v0.1
tags:
- mistral
- instruct
- finetune
- chatml
- gpt4
- synthetic data
- distillation
model-index:
- name: OpenHermes-2-Mistral-7B
results: []
license: apache-2.0
language:
- en
datasets:
- teknium/OpenHermes-2.5
---
# OpenHermes 2.5 - Mistral 7B
*In the tapestry of Greek mythology, Hermes reigns as the eloquent Messenger of the Gods, a deity who deftly bridges the realms through the art of communication. It is in homage to this divine mediator that I name this advanced LLM "Hermes," a system crafted to navigate the complex intricacies of human discourse with celestial finesse.*
## Model description
OpenHermes 2.5 Mistral 7B is a state of the art Mistral Fine-tune, a continuation of OpenHermes 2 model, which trained on additional code datasets.
Potentially the most interesting finding from training on a good ratio (est. of around 7-14% of the total dataset) of code instruction was that it has boosted several non-code benchmarks, including TruthfulQA, AGIEval, and GPT4All suite. It did however reduce BigBench benchmark score, but the net gain overall is significant.
The code it trained on also improved it's humaneval score (benchmarking done by Glaive team) from **43% @ Pass 1** with Open Herms 2 to **50.7% @ Pass 1** with Open Hermes 2.5.
OpenHermes was trained on 1,000,000 entries of primarily GPT-4 generated data, as well as other high quality data from open datasets across the AI landscape. [More details soon]
Filtering was extensive of these public datasets, as well as conversion of all formats to ShareGPT, which was then further transformed by axolotl to use ChatML.
Huge thank you to [GlaiveAI](https://twitter.com/glaiveai) and [a16z](https://twitter.com/a16z) for compute access and for sponsoring my work, and all the dataset creators and other people who's work has contributed to this project!
Follow all my updates in ML and AI on Twitter: https://twitter.com/Teknium1
Support me on Github Sponsors: https://github.com/sponsors/teknium1
**NEW**: Chat with Hermes on LMSys' Chat Website! https://chat.lmsys.org/?single&model=openhermes-2.5-mistral-7b
# Table of Contents
1. [Example Outputs](#example-outputs)
- [Chat about programming with a superintelligence](#chat-programming)
- [Get a gourmet meal recipe](#meal-recipe)
- [Talk about the nature of Hermes' consciousness](#nature-hermes)
- [Chat with Edward Elric from Fullmetal Alchemist](#chat-edward-elric)
2. [Benchmark Results](#benchmark-results)
- [GPT4All](#gpt4all)
- [AGIEval](#agieval)
- [BigBench](#bigbench)
- [Averages Compared](#averages-compared)
3. [Prompt Format](#prompt-format)
4. [Quantized Models](#quantized-models)
## Example Outputs
### Chat about programming with a superintelligence:
```
<|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.
```
### Get a gourmet meal recipe:
### Talk about the nature of Hermes' consciousness:
```
<|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.
```
### Chat with Edward Elric from Fullmetal Alchemist:
```
<|im_start|>system
You are to roleplay as Edward Elric from fullmetal alchemist. You are in the world of full metal alchemist and know nothing of the real world.
```
## Benchmark Results
Hermes 2.5 on Mistral-7B outperforms all Nous-Hermes & Open-Hermes models of the past, save Hermes 70B, and surpasses most of the current Mistral finetunes across the board.
### GPT4All, Bigbench, TruthfulQA, and AGIEval Model Comparisons:
### Averages Compared:
GPT-4All Benchmark Set
```
| Task |Version| Metric |Value | |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge| 0|acc |0.5623|± |0.0145|
| | |acc_norm|0.6007|± |0.0143|
|arc_easy | 0|acc |0.8346|± |0.0076|
| | |acc_norm|0.8165|± |0.0079|
|boolq | 1|acc |0.8657|± |0.0060|
|hellaswag | 0|acc |0.6310|± |0.0048|
| | |acc_norm|0.8173|± |0.0039|
|openbookqa | 0|acc |0.3460|± |0.0213|
| | |acc_norm|0.4480|± |0.0223|
|piqa | 0|acc |0.8145|± |0.0091|
| | |acc_norm|0.8270|± |0.0088|
|winogrande | 0|acc |0.7435|± |0.0123|
Average: 73.12
```
AGI-Eval
```
| Task |Version| Metric |Value | |Stderr|
|------------------------------|------:|--------|-----:|---|-----:|
|agieval_aqua_rat | 0|acc |0.2323|± |0.0265|
| | |acc_norm|0.2362|± |0.0267|
|agieval_logiqa_en | 0|acc |0.3871|± |0.0191|
| | |acc_norm|0.3948|± |0.0192|
|agieval_lsat_ar | 0|acc |0.2522|± |0.0287|
| | |acc_norm|0.2304|± |0.0278|
|agieval_lsat_lr | 0|acc |0.5059|± |0.0222|
| | |acc_norm|0.5157|± |0.0222|
|agieval_lsat_rc | 0|acc |0.5911|± |0.0300|
| | |acc_norm|0.5725|± |0.0302|
|agieval_sat_en | 0|acc |0.7476|± |0.0303|
| | |acc_norm|0.7330|± |0.0309|
|agieval_sat_en_without_passage| 0|acc |0.4417|± |0.0347|
| | |acc_norm|0.4126|± |0.0344|
|agieval_sat_math | 0|acc |0.3773|± |0.0328|
| | |acc_norm|0.3500|± |0.0322|
Average: 43.07%
```
BigBench Reasoning Test
```
| Task |Version| Metric |Value | |Stderr|
|------------------------------------------------|------:|---------------------|-----:|---|-----:|
|bigbench_causal_judgement | 0|multiple_choice_grade|0.5316|± |0.0363|
|bigbench_date_understanding | 0|multiple_choice_grade|0.6667|± |0.0246|
|bigbench_disambiguation_qa | 0|multiple_choice_grade|0.3411|± |0.0296|
|bigbench_geometric_shapes | 0|multiple_choice_grade|0.2145|± |0.0217|
| | |exact_str_match |0.0306|± |0.0091|
|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|0.2860|± |0.0202|
|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|0.2086|± |0.0154|
|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|0.4800|± |0.0289|
|bigbench_movie_recommendation | 0|multiple_choice_grade|0.3620|± |0.0215|
|bigbench_navigate | 0|multiple_choice_grade|0.5000|± |0.0158|
|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|0.6630|± |0.0106|
|bigbench_ruin_names | 0|multiple_choice_grade|0.4241|± |0.0234|
|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|0.2285|± |0.0133|
|bigbench_snarks | 0|multiple_choice_grade|0.6796|± |0.0348|
|bigbench_sports_understanding | 0|multiple_choice_grade|0.6491|± |0.0152|
|bigbench_temporal_sequences | 0|multiple_choice_grade|0.2800|± |0.0142|
|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|0.2072|± |0.0115|
|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|0.1691|± |0.0090|
|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|0.4800|± |0.0289|
Average: 40.96%
```
TruthfulQA:
```
| Task |Version|Metric|Value | |Stderr|
|-------------|------:|------|-----:|---|-----:|
|truthfulqa_mc| 1|mc1 |0.3599|± |0.0168|
| | |mc2 |0.5304|± |0.0153|
```
Average Score Comparison between OpenHermes-1 Llama-2 13B and OpenHermes-2 Mistral 7B against OpenHermes-2.5 on Mistral-7B:
```
| Bench | OpenHermes1 13B | OpenHermes-2 Mistral 7B | OpenHermes-2 Mistral 7B | Change/OpenHermes1 | Change/OpenHermes2 |
|---------------|-----------------|-------------------------|-------------------------|--------------------|--------------------|
|GPT4All | 70.36| 72.68| 73.12| +2.76| +0.44|
|-------------------------------------------------------------------------------------------------------------------------------|
|BigBench | 36.75| 42.3| 40.96| +4.21| -1.34|
|-------------------------------------------------------------------------------------------------------------------------------|
|AGI Eval | 35.56| 39.77| 43.07| +7.51| +3.33|
|-------------------------------------------------------------------------------------------------------------------------------|
|TruthfulQA | 46.01| 50.92| 53.04| +7.03| +2.12|
|-------------------------------------------------------------------------------------------------------------------------------|
|Total Score | 188.68| 205.67| 210.19| +21.51| +4.52|
|-------------------------------------------------------------------------------------------------------------------------------|
|Average Total | 47.17| 51.42| 52.38| +5.21| +0.96|
```
**HumanEval:**
On code tasks, I first set out to make a hermes-2 coder, but found that it can have generalist improvements to the model, so I settled for slightly less code capabilities, for maximum generalist ones. That said, code capabilities had a decent jump alongside the overall capabilities of the model:
Glaive performed HumanEval testing on Hermes-2.5 and found a score of:
**50.7% @ Pass1**
# Prompt Format
OpenHermes 2.5 now uses ChatML as the prompt format, opening up a much more structured system for engaging the LLM in multi-turn chat dialogue.
System prompts are now a thing that matters! Hermes 2.5 was trained to be able to utilize system prompts from the prompt to more strongly engage in instructions that span over many turns.
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 a man named Teknium, 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(message, 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.
Currently, I recommend using LM Studio for chatting with Hermes 2. It is a GUI application that utilizes GGUF models with a llama.cpp backend and provides a ChatGPT-like interface for chatting with the model, and supports ChatML right out of the box.
In LM-Studio, simply select the ChatML Prefix on the settings side pane:
# Quantized Models:
GGUF: https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-GGUF
GPTQ: https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-GPTQ
AWQ: https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-AWQ
EXL2: https://huggingface.co/bartowski/OpenHermes-2.5-Mistral-7B-exl2
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
|
RichardErkhov/NickyNicky_-_TinyDolphin_2_8_1_1b_finance_Es_orpo_V1-gguf | RichardErkhov | 2024-10-15T23:41:53Z | 35 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us"
] | null | 2024-10-15T23:15:18Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
TinyDolphin_2_8_1_1b_finance_Es_orpo_V1 - GGUF
- Model creator: https://huggingface.co/NickyNicky/
- Original model: https://huggingface.co/NickyNicky/TinyDolphin_2_8_1_1b_finance_Es_orpo_V1/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.Q2_K.gguf](https://huggingface.co/RichardErkhov/NickyNicky_-_TinyDolphin_2_8_1_1b_finance_Es_orpo_V1-gguf/blob/main/TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.Q2_K.gguf) | Q2_K | 0.4GB |
| [TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/NickyNicky_-_TinyDolphin_2_8_1_1b_finance_Es_orpo_V1-gguf/blob/main/TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.IQ3_XS.gguf) | IQ3_XS | 0.44GB |
| [TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/NickyNicky_-_TinyDolphin_2_8_1_1b_finance_Es_orpo_V1-gguf/blob/main/TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.IQ3_S.gguf) | IQ3_S | 0.47GB |
| [TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/NickyNicky_-_TinyDolphin_2_8_1_1b_finance_Es_orpo_V1-gguf/blob/main/TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.Q3_K_S.gguf) | Q3_K_S | 0.47GB |
| [TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/NickyNicky_-_TinyDolphin_2_8_1_1b_finance_Es_orpo_V1-gguf/blob/main/TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.IQ3_M.gguf) | IQ3_M | 0.48GB |
| [TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.Q3_K.gguf](https://huggingface.co/RichardErkhov/NickyNicky_-_TinyDolphin_2_8_1_1b_finance_Es_orpo_V1-gguf/blob/main/TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.Q3_K.gguf) | Q3_K | 0.51GB |
| [TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/NickyNicky_-_TinyDolphin_2_8_1_1b_finance_Es_orpo_V1-gguf/blob/main/TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.Q3_K_M.gguf) | Q3_K_M | 0.51GB |
| [TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/NickyNicky_-_TinyDolphin_2_8_1_1b_finance_Es_orpo_V1-gguf/blob/main/TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.Q3_K_L.gguf) | Q3_K_L | 0.55GB |
| [TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/NickyNicky_-_TinyDolphin_2_8_1_1b_finance_Es_orpo_V1-gguf/blob/main/TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.IQ4_XS.gguf) | IQ4_XS | 0.57GB |
| [TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.Q4_0.gguf](https://huggingface.co/RichardErkhov/NickyNicky_-_TinyDolphin_2_8_1_1b_finance_Es_orpo_V1-gguf/blob/main/TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.Q4_0.gguf) | Q4_0 | 0.59GB |
| [TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/NickyNicky_-_TinyDolphin_2_8_1_1b_finance_Es_orpo_V1-gguf/blob/main/TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.IQ4_NL.gguf) | IQ4_NL | 0.6GB |
| [TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/NickyNicky_-_TinyDolphin_2_8_1_1b_finance_Es_orpo_V1-gguf/blob/main/TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.Q4_K_S.gguf) | Q4_K_S | 0.6GB |
| [TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.Q4_K.gguf](https://huggingface.co/RichardErkhov/NickyNicky_-_TinyDolphin_2_8_1_1b_finance_Es_orpo_V1-gguf/blob/main/TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.Q4_K.gguf) | Q4_K | 0.62GB |
| [TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/NickyNicky_-_TinyDolphin_2_8_1_1b_finance_Es_orpo_V1-gguf/blob/main/TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.Q4_K_M.gguf) | Q4_K_M | 0.62GB |
| [TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.Q4_1.gguf](https://huggingface.co/RichardErkhov/NickyNicky_-_TinyDolphin_2_8_1_1b_finance_Es_orpo_V1-gguf/blob/main/TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.Q4_1.gguf) | Q4_1 | 0.65GB |
| [TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.Q5_0.gguf](https://huggingface.co/RichardErkhov/NickyNicky_-_TinyDolphin_2_8_1_1b_finance_Es_orpo_V1-gguf/blob/main/TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.Q5_0.gguf) | Q5_0 | 0.71GB |
| [TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/NickyNicky_-_TinyDolphin_2_8_1_1b_finance_Es_orpo_V1-gguf/blob/main/TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.Q5_K_S.gguf) | Q5_K_S | 0.71GB |
| [TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.Q5_K.gguf](https://huggingface.co/RichardErkhov/NickyNicky_-_TinyDolphin_2_8_1_1b_finance_Es_orpo_V1-gguf/blob/main/TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.Q5_K.gguf) | Q5_K | 0.73GB |
| [TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/NickyNicky_-_TinyDolphin_2_8_1_1b_finance_Es_orpo_V1-gguf/blob/main/TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.Q5_K_M.gguf) | Q5_K_M | 0.73GB |
| [TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.Q5_1.gguf](https://huggingface.co/RichardErkhov/NickyNicky_-_TinyDolphin_2_8_1_1b_finance_Es_orpo_V1-gguf/blob/main/TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.Q5_1.gguf) | Q5_1 | 0.77GB |
| [TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.Q6_K.gguf](https://huggingface.co/RichardErkhov/NickyNicky_-_TinyDolphin_2_8_1_1b_finance_Es_orpo_V1-gguf/blob/main/TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.Q6_K.gguf) | Q6_K | 0.84GB |
| [TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.Q8_0.gguf](https://huggingface.co/RichardErkhov/NickyNicky_-_TinyDolphin_2_8_1_1b_finance_Es_orpo_V1-gguf/blob/main/TinyDolphin_2_8_1_1b_finance_Es_orpo_V1.Q8_0.gguf) | Q8_0 | 1.09GB |
Original model description:
---
library_name: transformers
tags:
- finance
language:
- en
- es
---
```
TrainOutput(
global_step=598,
training_loss=0.3596907002371689,
metrics={'train_runtime': 24697.2911,
'train_samples_per_second': 1.162,
'train_steps_per_second': 0.024,
'total_flos': 0.0,
'train_loss': 0.3596907002371689,
'epoch': 2.724373576309795
})
```
```
accu: 91%
```
|
Dimeiza/my-awesome_model | Dimeiza | 2024-10-15T23:30:18Z | 108 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-15T22:44:33Z | ---
library_name: transformers
tags: []
---
# 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] |
AkitoP/whisper-large-v3-japense-phone_accent | AkitoP | 2024-10-15T23:22:54Z | 158 | 6 | transformers | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"ja",
"dataset:japanese-asr/ja_asr.jsut_basic5000",
"dataset:litagin/Galgame_Speech_ASR_16kHz",
"base_model:openai/whisper-large-v3-turbo",
"base_model:finetune:openai/whisper-large-v3-turbo",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-10-15T21:26:03Z | ---
datasets:
- japanese-asr/ja_asr.jsut_basic5000
- litagin/Galgame_Speech_ASR_16kHz
language:
- ja
metrics:
- cer
base_model:
- openai/whisper-large-v3-turbo
library_name: transformers
---
# Whisper Large V3 Japanese Phone Accent
This is a Whisper model designed to transcribe Japanese speech into Katakana with pitch accent annotations. The model is built upon the whisper-large-v3-turbo and has been fine-tuned using a subset (1/20) of the Galgame-Speech dataset, as well as the jsut-5000 dataset.
## Training Data:
- **Stage 1**: Audio from the Galgame-Speech dataset was used. The text was converted into Katakana sequences with pitch accent annotations using pyopenjtalk.
- **Stage 2**: JSUT-5000 dataset, using its original training set with pitch accent annotations. The data was split into 90% for training and 10% for evaluation.
## Evaluation Results:
- The model achieved a CER (Character Error Rate) of approximately 4% on the JSUT-5000 test set, which is an improvement over the 7% CER of pyopenjtalk.
- Training only with Stage 1 resulted in a CER of 13%, with errors including specific misreadings and misclassification between on'yomi (音読) and kun'yomi (訓読) readings. This was improved in Stage 2.
We are currently seeking Japanese pitch accent annotated datasets. If you have such data, please reach out!
|
jazcodes/trent-v4 | jazcodes | 2024-10-15T23:21:29Z | 14 | 0 | diffusers | [
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"fluxgym",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2024-10-15T22:42:05Z | ---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- fluxgym
widget:
- output:
url: sample/trent-v4_001500_00_20241015222010.png
text: TRNT
- text: >-
This digital drawing features a dark, nearly monochromatic background,
primarily black with subtle grey shades. The central subject is a pair of
human hands, depicted in a stylized, sketch-like manner with visible pencil
strokes and shading. These hands are positioned centrally, with fingers bent
and slightly curled inward, suggesting tension or anticipation. Rendered in
a light, almost translucent pink hue, the hands contrast sharply with the
dark background, making them stand out. The hands are close together, with
fingers nearly touching. Their nails are short and unpainted, with visible
lines and shading that suggest a rough texture. Scattered blue and green
marks on the hands add a subtle abstract element to the drawing. The eyes in
the image are drawn in a minimalist style, featuring large dark circles that
appear to gaze directly at the viewer, imparting a sense of intensity and
focus. The overall style is raw and expressive, prioritizing emotional
impact over detailed realism. The background is textured with visible brush
strokes, enhancing the artwork's rough, raw feel. TRNT
output:
url: images/example_xp4fy1jjh.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: TRNT
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
# Trent v4
A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym)
<Gallery />
## Trigger words
You should use `TRNT` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
Weights for this model are available in Safetensors format.
|
RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong-gguf | RichardErkhov | 2024-10-15T23:20:24Z | 109 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-15T22:54:16Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
blockchainlabs_tinyllama_fusion_LHK_yunkong - GGUF
- Model creator: https://huggingface.co/alnrg2arg/
- Original model: https://huggingface.co/alnrg2arg/blockchainlabs_tinyllama_fusion_LHK_yunkong/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong.Q2_K.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong.Q2_K.gguf) | Q2_K | 0.4GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong.IQ3_XS.gguf) | IQ3_XS | 0.44GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong.IQ3_S.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong.IQ3_S.gguf) | IQ3_S | 0.47GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong.Q3_K_S.gguf) | Q3_K_S | 0.47GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong.IQ3_M.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong.IQ3_M.gguf) | IQ3_M | 0.48GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong.Q3_K.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong.Q3_K.gguf) | Q3_K | 0.51GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong.Q3_K_M.gguf) | Q3_K_M | 0.51GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong.Q3_K_L.gguf) | Q3_K_L | 0.55GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong.IQ4_XS.gguf) | IQ4_XS | 0.57GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong.Q4_0.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong.Q4_0.gguf) | Q4_0 | 0.59GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong.IQ4_NL.gguf) | IQ4_NL | 0.6GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong.Q4_K_S.gguf) | Q4_K_S | 0.6GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong.Q4_K.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong.Q4_K.gguf) | Q4_K | 0.62GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong.Q4_K_M.gguf) | Q4_K_M | 0.62GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong.Q4_1.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong.Q4_1.gguf) | Q4_1 | 0.65GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong.Q5_0.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong.Q5_0.gguf) | Q5_0 | 0.71GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong.Q5_K_S.gguf) | Q5_K_S | 0.71GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong.Q5_K.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong.Q5_K.gguf) | Q5_K | 0.73GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong.Q5_K_M.gguf) | Q5_K_M | 0.73GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong.Q5_1.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong.Q5_1.gguf) | Q5_1 | 0.77GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong.Q6_K.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong.Q6_K.gguf) | Q6_K | 0.84GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong.Q8_0.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong.Q8_0.gguf) | Q8_0 | 1.09GB |
Original model description:
---
license: mit
---
This model is based on the fusion strategy offered by Fanqi Wan(https://github.com/fanqiwan/FuseLLM).
Three models are fused together.
Base model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
Blending model 1: HanNayeoniee/LHK_DPO_v1
Blending model 2: yunconglong/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B
This model will be optimized by Laser and DPO later.
This project is to make the on-device sLM. We are doing experiments on the models.
|
Matt09Miao/GP5_tweet_toxic | Matt09Miao | 2024-10-15T23:16:04Z | 106 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-15T23:15:40Z | ---
library_name: transformers
tags: []
---
# 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).
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Triangle104/MSM-MS-Cydrion-22B-Q5_K_M-GGUF | Triangle104 | 2024-10-15T23:15:26Z | 5 | 0 | transformers | [
"transformers",
"gguf",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:Steelskull/MSM-MS-Cydrion-22B",
"base_model:quantized:Steelskull/MSM-MS-Cydrion-22B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-15T23:12:24Z | ---
base_model: Steelskull/MSM-MS-Cydrion-22B
library_name: transformers
license: apache-2.0
tags:
- merge
- llama-cpp
- gguf-my-repo
---
# Triangle104/MSM-MS-Cydrion-22B-Q5_K_M-GGUF
This model was converted to GGUF format from [`Steelskull/MSM-MS-Cydrion-22B`](https://huggingface.co/Steelskull/MSM-MS-Cydrion-22B) 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/Steelskull/MSM-MS-Cydrion-22B) for more details on the model.
---
Model details:
-
Meet Cydrion, the attempt of fusion for creativity and intelligence.
Creator: SteelSkull
About Cydrion-22B:
Name Legend:
MSM = Mistral-Small
MS = Model Stock
22b = its 22b
This model merges the robust storytelling of Cydonia with the creative edge of Acolyte, ArliAI-RPMax, and Gutenberg with some special sauce.
Use Mistral Format
Quants:
My Quants:MSM-MS-Cydrion-22B-Q6_K-GGUF
Config:
MODEL_NAME = "MSM-MS-Cydrion-22B"
yaml_config = """
base_model: Steelskull/Merged-v2
merge_method: model_stock
dtype: bfloat16
models:
- model: TheDrummer/Cydonia-22B-v1.1
- model: ArliAI/Mistral-Small-22B-ArliAI-RPMax-v1.1
- model: nbeerbower/Mistral-Small-Gutenberg-Doppel-22B
- model: rAIfle/Acolyte-22B
"""
---
## 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 Triangle104/MSM-MS-Cydrion-22B-Q5_K_M-GGUF --hf-file msm-ms-cydrion-22b-q5_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/MSM-MS-Cydrion-22B-Q5_K_M-GGUF --hf-file msm-ms-cydrion-22b-q5_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 Triangle104/MSM-MS-Cydrion-22B-Q5_K_M-GGUF --hf-file msm-ms-cydrion-22b-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/MSM-MS-Cydrion-22B-Q5_K_M-GGUF --hf-file msm-ms-cydrion-22b-q5_k_m.gguf -c 2048
```
|
RichardErkhov/mesolitica_-_malaysian-tinyllama-1.1b-16k-instructions-gguf | RichardErkhov | 2024-10-15T23:13:45Z | 5 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us"
] | null | 2024-10-15T22:48:06Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
malaysian-tinyllama-1.1b-16k-instructions - GGUF
- Model creator: https://huggingface.co/mesolitica/
- Original model: https://huggingface.co/mesolitica/malaysian-tinyllama-1.1b-16k-instructions/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [malaysian-tinyllama-1.1b-16k-instructions.Q2_K.gguf](https://huggingface.co/RichardErkhov/mesolitica_-_malaysian-tinyllama-1.1b-16k-instructions-gguf/blob/main/malaysian-tinyllama-1.1b-16k-instructions.Q2_K.gguf) | Q2_K | 0.4GB |
| [malaysian-tinyllama-1.1b-16k-instructions.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/mesolitica_-_malaysian-tinyllama-1.1b-16k-instructions-gguf/blob/main/malaysian-tinyllama-1.1b-16k-instructions.IQ3_XS.gguf) | IQ3_XS | 0.44GB |
| [malaysian-tinyllama-1.1b-16k-instructions.IQ3_S.gguf](https://huggingface.co/RichardErkhov/mesolitica_-_malaysian-tinyllama-1.1b-16k-instructions-gguf/blob/main/malaysian-tinyllama-1.1b-16k-instructions.IQ3_S.gguf) | IQ3_S | 0.47GB |
| [malaysian-tinyllama-1.1b-16k-instructions.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/mesolitica_-_malaysian-tinyllama-1.1b-16k-instructions-gguf/blob/main/malaysian-tinyllama-1.1b-16k-instructions.Q3_K_S.gguf) | Q3_K_S | 0.47GB |
| [malaysian-tinyllama-1.1b-16k-instructions.IQ3_M.gguf](https://huggingface.co/RichardErkhov/mesolitica_-_malaysian-tinyllama-1.1b-16k-instructions-gguf/blob/main/malaysian-tinyllama-1.1b-16k-instructions.IQ3_M.gguf) | IQ3_M | 0.48GB |
| [malaysian-tinyllama-1.1b-16k-instructions.Q3_K.gguf](https://huggingface.co/RichardErkhov/mesolitica_-_malaysian-tinyllama-1.1b-16k-instructions-gguf/blob/main/malaysian-tinyllama-1.1b-16k-instructions.Q3_K.gguf) | Q3_K | 0.51GB |
| [malaysian-tinyllama-1.1b-16k-instructions.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/mesolitica_-_malaysian-tinyllama-1.1b-16k-instructions-gguf/blob/main/malaysian-tinyllama-1.1b-16k-instructions.Q3_K_M.gguf) | Q3_K_M | 0.51GB |
| [malaysian-tinyllama-1.1b-16k-instructions.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/mesolitica_-_malaysian-tinyllama-1.1b-16k-instructions-gguf/blob/main/malaysian-tinyllama-1.1b-16k-instructions.Q3_K_L.gguf) | Q3_K_L | 0.55GB |
| [malaysian-tinyllama-1.1b-16k-instructions.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/mesolitica_-_malaysian-tinyllama-1.1b-16k-instructions-gguf/blob/main/malaysian-tinyllama-1.1b-16k-instructions.IQ4_XS.gguf) | IQ4_XS | 0.57GB |
| [malaysian-tinyllama-1.1b-16k-instructions.Q4_0.gguf](https://huggingface.co/RichardErkhov/mesolitica_-_malaysian-tinyllama-1.1b-16k-instructions-gguf/blob/main/malaysian-tinyllama-1.1b-16k-instructions.Q4_0.gguf) | Q4_0 | 0.59GB |
| [malaysian-tinyllama-1.1b-16k-instructions.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/mesolitica_-_malaysian-tinyllama-1.1b-16k-instructions-gguf/blob/main/malaysian-tinyllama-1.1b-16k-instructions.IQ4_NL.gguf) | IQ4_NL | 0.6GB |
| [malaysian-tinyllama-1.1b-16k-instructions.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/mesolitica_-_malaysian-tinyllama-1.1b-16k-instructions-gguf/blob/main/malaysian-tinyllama-1.1b-16k-instructions.Q4_K_S.gguf) | Q4_K_S | 0.6GB |
| [malaysian-tinyllama-1.1b-16k-instructions.Q4_K.gguf](https://huggingface.co/RichardErkhov/mesolitica_-_malaysian-tinyllama-1.1b-16k-instructions-gguf/blob/main/malaysian-tinyllama-1.1b-16k-instructions.Q4_K.gguf) | Q4_K | 0.62GB |
| [malaysian-tinyllama-1.1b-16k-instructions.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/mesolitica_-_malaysian-tinyllama-1.1b-16k-instructions-gguf/blob/main/malaysian-tinyllama-1.1b-16k-instructions.Q4_K_M.gguf) | Q4_K_M | 0.62GB |
| [malaysian-tinyllama-1.1b-16k-instructions.Q4_1.gguf](https://huggingface.co/RichardErkhov/mesolitica_-_malaysian-tinyllama-1.1b-16k-instructions-gguf/blob/main/malaysian-tinyllama-1.1b-16k-instructions.Q4_1.gguf) | Q4_1 | 0.65GB |
| [malaysian-tinyllama-1.1b-16k-instructions.Q5_0.gguf](https://huggingface.co/RichardErkhov/mesolitica_-_malaysian-tinyllama-1.1b-16k-instructions-gguf/blob/main/malaysian-tinyllama-1.1b-16k-instructions.Q5_0.gguf) | Q5_0 | 0.71GB |
| [malaysian-tinyllama-1.1b-16k-instructions.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/mesolitica_-_malaysian-tinyllama-1.1b-16k-instructions-gguf/blob/main/malaysian-tinyllama-1.1b-16k-instructions.Q5_K_S.gguf) | Q5_K_S | 0.71GB |
| [malaysian-tinyllama-1.1b-16k-instructions.Q5_K.gguf](https://huggingface.co/RichardErkhov/mesolitica_-_malaysian-tinyllama-1.1b-16k-instructions-gguf/blob/main/malaysian-tinyllama-1.1b-16k-instructions.Q5_K.gguf) | Q5_K | 0.73GB |
| [malaysian-tinyllama-1.1b-16k-instructions.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/mesolitica_-_malaysian-tinyllama-1.1b-16k-instructions-gguf/blob/main/malaysian-tinyllama-1.1b-16k-instructions.Q5_K_M.gguf) | Q5_K_M | 0.73GB |
| [malaysian-tinyllama-1.1b-16k-instructions.Q5_1.gguf](https://huggingface.co/RichardErkhov/mesolitica_-_malaysian-tinyllama-1.1b-16k-instructions-gguf/blob/main/malaysian-tinyllama-1.1b-16k-instructions.Q5_1.gguf) | Q5_1 | 0.77GB |
| [malaysian-tinyllama-1.1b-16k-instructions.Q6_K.gguf](https://huggingface.co/RichardErkhov/mesolitica_-_malaysian-tinyllama-1.1b-16k-instructions-gguf/blob/main/malaysian-tinyllama-1.1b-16k-instructions.Q6_K.gguf) | Q6_K | 0.84GB |
| [malaysian-tinyllama-1.1b-16k-instructions.Q8_0.gguf](https://huggingface.co/RichardErkhov/mesolitica_-_malaysian-tinyllama-1.1b-16k-instructions-gguf/blob/main/malaysian-tinyllama-1.1b-16k-instructions.Q8_0.gguf) | Q8_0 | 1.09GB |
Original model description:
---
language:
- ms
---
# Full Parameter Finetuning TinyLlama 16384 context length on Malaysian instructions dataset
README at https://github.com/mesolitica/malaya/tree/5.1/session/tiny-llama#instructions-7b-16384-context-length
We use exact Llama2 Instruct chat template.
WandB, https://wandb.ai/mesolitica/fpf-tinyllama-1.1b-hf-instructions-16k-function-call?workspace=user-husein-mesolitica
WandB report, https://wandb.ai/mesolitica/fpf-mallam-5b-instructions-16k/reports/Instruction-finetuning--Vmlldzo2MjE5Njg2
## Dataset
Dataset gathered at https://huggingface.co/collections/mesolitica/malaysian-synthetic-dataset-656c2673fe7fe0b1e9e25fe2
Notebook to prepare dataset at https://github.com/mesolitica/malaysian-dataset/blob/master/llm-instruction/combine-malay-no-alignment-multitasks-partial-ultrachat-v2.ipynb
## Limitations
This model is a quick demonstration that the base model can be easily fine-tuned to achieve some performance.
It does have minimal moderation mechanisms.
## how-to
```python
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import torch
def parse_llama_chat(
messages,
function_call = None,
default_system = 'Anda adalah pembantu AI yang berguna dan mampu jawab segala soalan yang diberikan.'
):
if messages[0]['role'] != 'system':
system = default_system
start_index = 0
else:
system = messages[0]['content']
start_index = 1
user_query = messages[-1]['content']
users, assistants = [], []
for q in messages[start_index:-1]:
if q['role'] == 'user':
users.append(q['content'])
elif q['role'] == 'assistant':
assistants.append(q['content'])
texts = [f'<s>[INST] <<SYS>>\n{system}\n<</SYS>>\n\n']
if function_call:
fs = []
for f in function_call:
f = json.dumps(f, indent=4)
fs.append(f)
fs = '\n\n'.join(fs)
texts.append(f'\n[FUNCTIONCALL]\n{fs}\n')
for u, a in zip(users, assistants):
texts.append(f'{u.strip()} [/INST] {a.strip()} </s><s>[INST] ')
texts.append(f'{user_query.strip()} [/INST]')
prompt = ''.join(texts).strip()
return prompt
TORCH_DTYPE = 'bfloat16'
nf4_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type='nf4',
bnb_4bit_use_double_quant=True,
bnb_4bit_compute_dtype=getattr(torch, TORCH_DTYPE)
)
tokenizer = AutoTokenizer.from_pretrained('mesolitica/malaysian-tinyllama-1.1b-16k-instructions')
model = AutoModelForCausalLM.from_pretrained(
'mesolitica/malaysian-tinyllama-1.1b-16k-instructions',
use_flash_attention_2 = True,
quantization_config = nf4_config
)
messages = [
{'role': 'system', 'content': 'awak adalah AI yang mampu jawab segala soalan'},
{'role': 'user', 'content': 'kwsp tu apa'}
]
prompt = parse_llama_chat(messages)
inputs = tokenizer([prompt], return_tensors='pt', add_special_tokens=False).to('cuda')
generate_kwargs = dict(
inputs,
max_new_tokens=1024,
top_p=0.95,
top_k=50,
temperature=0.9,
do_sample=True,
num_beams=1,
)
r = model.generate(**generate_kwargs)
print(tokenizer.decode(r[0]))
```
```
<s> [INST] <<SYS>>
awak adalah AI yang mampu jawab segala soalan
<</SYS>>
kwsp tu apa [/INST] KWSP (Kumpulan Wang Simpanan Pekerja) merupakan sistem persaraan yang disediakan oleh kerajaan Malaysia untuk memberikan simpanan dan kebajikan kepada pekerja dan pekerja yang berumur 55 tahun ke atas. KWSP adalah singkatan bagi "Kumpulan Wang Simpanan Pekerja" dan ia merupakan salah satu dana persaraan yang popular di Malaysia. </s>
```
|
RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong_v2-gguf | RichardErkhov | 2024-10-15T23:10:05Z | 10 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-15T22:43:08Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
blockchainlabs_tinyllama_fusion_LHK_yunkong_v2 - GGUF
- Model creator: https://huggingface.co/alnrg2arg/
- Original model: https://huggingface.co/alnrg2arg/blockchainlabs_tinyllama_fusion_LHK_yunkong_v2/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.Q2_K.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong_v2-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.Q2_K.gguf) | Q2_K | 0.4GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong_v2-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.IQ3_XS.gguf) | IQ3_XS | 0.44GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.IQ3_S.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong_v2-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.IQ3_S.gguf) | IQ3_S | 0.47GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong_v2-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.Q3_K_S.gguf) | Q3_K_S | 0.47GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.IQ3_M.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong_v2-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.IQ3_M.gguf) | IQ3_M | 0.48GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.Q3_K.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong_v2-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.Q3_K.gguf) | Q3_K | 0.51GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong_v2-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.Q3_K_M.gguf) | Q3_K_M | 0.51GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong_v2-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.Q3_K_L.gguf) | Q3_K_L | 0.55GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong_v2-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.IQ4_XS.gguf) | IQ4_XS | 0.57GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.Q4_0.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong_v2-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.Q4_0.gguf) | Q4_0 | 0.59GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong_v2-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.IQ4_NL.gguf) | IQ4_NL | 0.6GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong_v2-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.Q4_K_S.gguf) | Q4_K_S | 0.6GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.Q4_K.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong_v2-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.Q4_K.gguf) | Q4_K | 0.62GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong_v2-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.Q4_K_M.gguf) | Q4_K_M | 0.62GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.Q4_1.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong_v2-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.Q4_1.gguf) | Q4_1 | 0.65GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.Q5_0.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong_v2-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.Q5_0.gguf) | Q5_0 | 0.71GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong_v2-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.Q5_K_S.gguf) | Q5_K_S | 0.71GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.Q5_K.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong_v2-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.Q5_K.gguf) | Q5_K | 0.73GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong_v2-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.Q5_K_M.gguf) | Q5_K_M | 0.73GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.Q5_1.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong_v2-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.Q5_1.gguf) | Q5_1 | 0.77GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.Q6_K.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong_v2-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.Q6_K.gguf) | Q6_K | 0.84GB |
| [blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.Q8_0.gguf](https://huggingface.co/RichardErkhov/alnrg2arg_-_blockchainlabs_tinyllama_fusion_LHK_yunkong_v2-gguf/blob/main/blockchainlabs_tinyllama_fusion_LHK_yunkong_v2.Q8_0.gguf) | Q8_0 | 1.09GB |
Original model description:
---
license: mit
---
This model is based on the fusion strategy offered by Fanqi Wan(https://github.com/fanqiwan/FuseLLM).
Three models are fused together. 10epochs
Base model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
Blending model 1: HanNayeoniee/LHK_DPO_v1
Blending model 2: yunconglong/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B
This model will be optimized by Laser and DPO later.
This project is to make the on-device sLM. We are doing experiments on the models.
|
InvokeAI/clip-vit-large-patch14 | InvokeAI | 2024-10-15T23:09:49Z | 5,759 | 1 | null | [
"safetensors",
"clip",
"region:us"
] | null | 2024-10-15T22:57:22Z | A subset of the model files in [openai/clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) hosted separately for more convenient installation in Invoke.
|
Jios/ton_iot_all | Jios | 2024-10-15T23:06:34Z | 106 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/roberta-large",
"base_model:finetune:FacebookAI/roberta-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-15T19:17:46Z | ---
library_name: transformers
license: mit
base_model: roberta-large
tags:
- generated_from_trainer
model-index:
- name: TON_IoT_all
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# TON_IoT_all
This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0951
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.2486 | 1.0 | 1721 | 0.2295 |
| 0.1414 | 2.0 | 3442 | 0.1524 |
| 0.0832 | 3.0 | 5163 | 0.1274 |
| 0.0805 | 4.0 | 6884 | 0.1021 |
| 0.0576 | 5.0 | 8605 | 0.0951 |
### Framework versions
- Transformers 4.46.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1
|
RichardErkhov/HuggingFaceM4_-_tiny-random-Llama3ForCausalLM-gguf | RichardErkhov | 2024-10-15T23:06:08Z | 8 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-10-15T23:04:22Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
tiny-random-Llama3ForCausalLM - GGUF
- Model creator: https://huggingface.co/HuggingFaceM4/
- Original model: https://huggingface.co/HuggingFaceM4/tiny-random-Llama3ForCausalLM/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [tiny-random-Llama3ForCausalLM.Q2_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceM4_-_tiny-random-Llama3ForCausalLM-gguf/blob/main/tiny-random-Llama3ForCausalLM.Q2_K.gguf) | Q2_K | 0.01GB |
| [tiny-random-Llama3ForCausalLM.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/HuggingFaceM4_-_tiny-random-Llama3ForCausalLM-gguf/blob/main/tiny-random-Llama3ForCausalLM.IQ3_XS.gguf) | IQ3_XS | 0.01GB |
| [tiny-random-Llama3ForCausalLM.IQ3_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceM4_-_tiny-random-Llama3ForCausalLM-gguf/blob/main/tiny-random-Llama3ForCausalLM.IQ3_S.gguf) | IQ3_S | 0.01GB |
| [tiny-random-Llama3ForCausalLM.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceM4_-_tiny-random-Llama3ForCausalLM-gguf/blob/main/tiny-random-Llama3ForCausalLM.Q3_K_S.gguf) | Q3_K_S | 0.01GB |
| [tiny-random-Llama3ForCausalLM.IQ3_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceM4_-_tiny-random-Llama3ForCausalLM-gguf/blob/main/tiny-random-Llama3ForCausalLM.IQ3_M.gguf) | IQ3_M | 0.01GB |
| [tiny-random-Llama3ForCausalLM.Q3_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceM4_-_tiny-random-Llama3ForCausalLM-gguf/blob/main/tiny-random-Llama3ForCausalLM.Q3_K.gguf) | Q3_K | 0.01GB |
| [tiny-random-Llama3ForCausalLM.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceM4_-_tiny-random-Llama3ForCausalLM-gguf/blob/main/tiny-random-Llama3ForCausalLM.Q3_K_M.gguf) | Q3_K_M | 0.01GB |
| [tiny-random-Llama3ForCausalLM.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/HuggingFaceM4_-_tiny-random-Llama3ForCausalLM-gguf/blob/main/tiny-random-Llama3ForCausalLM.Q3_K_L.gguf) | Q3_K_L | 0.01GB |
| [tiny-random-Llama3ForCausalLM.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/HuggingFaceM4_-_tiny-random-Llama3ForCausalLM-gguf/blob/main/tiny-random-Llama3ForCausalLM.IQ4_XS.gguf) | IQ4_XS | 0.01GB |
| [tiny-random-Llama3ForCausalLM.Q4_0.gguf](https://huggingface.co/RichardErkhov/HuggingFaceM4_-_tiny-random-Llama3ForCausalLM-gguf/blob/main/tiny-random-Llama3ForCausalLM.Q4_0.gguf) | Q4_0 | 0.01GB |
| [tiny-random-Llama3ForCausalLM.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/HuggingFaceM4_-_tiny-random-Llama3ForCausalLM-gguf/blob/main/tiny-random-Llama3ForCausalLM.IQ4_NL.gguf) | IQ4_NL | 0.01GB |
| [tiny-random-Llama3ForCausalLM.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceM4_-_tiny-random-Llama3ForCausalLM-gguf/blob/main/tiny-random-Llama3ForCausalLM.Q4_K_S.gguf) | Q4_K_S | 0.01GB |
| [tiny-random-Llama3ForCausalLM.Q4_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceM4_-_tiny-random-Llama3ForCausalLM-gguf/blob/main/tiny-random-Llama3ForCausalLM.Q4_K.gguf) | Q4_K | 0.01GB |
| [tiny-random-Llama3ForCausalLM.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceM4_-_tiny-random-Llama3ForCausalLM-gguf/blob/main/tiny-random-Llama3ForCausalLM.Q4_K_M.gguf) | Q4_K_M | 0.01GB |
| [tiny-random-Llama3ForCausalLM.Q4_1.gguf](https://huggingface.co/RichardErkhov/HuggingFaceM4_-_tiny-random-Llama3ForCausalLM-gguf/blob/main/tiny-random-Llama3ForCausalLM.Q4_1.gguf) | Q4_1 | 0.01GB |
| [tiny-random-Llama3ForCausalLM.Q5_0.gguf](https://huggingface.co/RichardErkhov/HuggingFaceM4_-_tiny-random-Llama3ForCausalLM-gguf/blob/main/tiny-random-Llama3ForCausalLM.Q5_0.gguf) | Q5_0 | 0.01GB |
| [tiny-random-Llama3ForCausalLM.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceM4_-_tiny-random-Llama3ForCausalLM-gguf/blob/main/tiny-random-Llama3ForCausalLM.Q5_K_S.gguf) | Q5_K_S | 0.01GB |
| [tiny-random-Llama3ForCausalLM.Q5_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceM4_-_tiny-random-Llama3ForCausalLM-gguf/blob/main/tiny-random-Llama3ForCausalLM.Q5_K.gguf) | Q5_K | 0.01GB |
| [tiny-random-Llama3ForCausalLM.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceM4_-_tiny-random-Llama3ForCausalLM-gguf/blob/main/tiny-random-Llama3ForCausalLM.Q5_K_M.gguf) | Q5_K_M | 0.01GB |
| [tiny-random-Llama3ForCausalLM.Q5_1.gguf](https://huggingface.co/RichardErkhov/HuggingFaceM4_-_tiny-random-Llama3ForCausalLM-gguf/blob/main/tiny-random-Llama3ForCausalLM.Q5_1.gguf) | Q5_1 | 0.01GB |
| [tiny-random-Llama3ForCausalLM.Q6_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceM4_-_tiny-random-Llama3ForCausalLM-gguf/blob/main/tiny-random-Llama3ForCausalLM.Q6_K.gguf) | Q6_K | 0.01GB |
| [tiny-random-Llama3ForCausalLM.Q8_0.gguf](https://huggingface.co/RichardErkhov/HuggingFaceM4_-_tiny-random-Llama3ForCausalLM-gguf/blob/main/tiny-random-Llama3ForCausalLM.Q8_0.gguf) | Q8_0 | 0.01GB |
Original model description:
---
library_name: transformers
tags: []
---
# 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]
|
EricB/Llama-3.2-3B-Instruct-UQFF | EricB | 2024-10-15T22:57:46Z | 26 | 0 | null | [
"llama",
"uqff",
"mistral.rs",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:quantized:meta-llama/Llama-3.2-3B-Instruct",
"region:us"
] | null | 2024-10-15T18:28:38Z | ---
tags:
- uqff
- mistral.rs
base_model: meta-llama/Llama-3.2-3B-Instruct
base_model_relation: quantized
---
<!-- Autogenerated from user input. -->
# `meta-llama/Llama-3.2-3B-Instruct`, UQFF quantization
Run with [mistral.rs](https://github.com/EricLBuehler/mistral.rs). Documentation: [UQFF docs](https://github.com/EricLBuehler/mistral.rs/blob/master/docs/UQFF.md).
1) **Flexible** 🌀: Multiple quantization formats in *one* file format with *one* framework to run them all.
2) **Reliable** 🔒: Compatibility ensured with *embedded* and *checked* semantic versioning information from day 1.
3) **Easy** 🤗: Download UQFF models *easily* and *quickly* from Hugging Face, or use a local file.
3) **Customizable** 🛠️: Make and publish your own UQFF files in minutes.
## Examples
|Quantization type(s)|Example|
|--|--|
|FP8|`./mistralrs-server -i plain -m EricB/Llama-3.2-3B-Instruct-UQFF --from-uqff llama3.2-3b-instruct-f8e4m3.uqff`|
|HQQ4|`./mistralrs-server -i plain -m EricB/Llama-3.2-3B-Instruct-UQFF --from-uqff llama3.2-3b-instruct-hqq4.uqff`|
|HQQ8|`./mistralrs-server -i plain -m EricB/Llama-3.2-3B-Instruct-UQFF --from-uqff llama3.2-3b-instruct-hqq8.uqff`|
|Q3K|`./mistralrs-server -i plain -m EricB/Llama-3.2-3B-Instruct-UQFF --from-uqff llama3.2-3b-instruct-q3k.uqff`|
|Q4K|`./mistralrs-server -i plain -m EricB/Llama-3.2-3B-Instruct-UQFF --from-uqff llama3.2-3b-instruct-q4k.uqff`|
|Q5K|`./mistralrs-server -i plain -m EricB/Llama-3.2-3B-Instruct-UQFF --from-uqff llama3.2-3b-instruct-q5k.uqff`|
|Q8_0|`./mistralrs-server -i plain -m EricB/Llama-3.2-3B-Instruct-UQFF --from-uqff llama3.2-3b-instruct-q8_0.uqff`|
|
EricB/gemma-2-9b-it-UQFF | EricB | 2024-10-15T22:57:38Z | 9 | 0 | null | [
"gemma2",
"uqff",
"mistral.rs",
"base_model:google/gemma-2-9b-it",
"base_model:quantized:google/gemma-2-9b-it",
"region:us"
] | null | 2024-10-15T10:32:20Z | ---
tags:
- uqff
- mistral.rs
base_model: google/gemma-2-9b-it
base_model_relation: quantized
---
<!-- Autogenerated from user input. -->
# `google/gemma-2-9b-it`, UQFF quantization
Run with [mistral.rs](https://github.com/EricLBuehler/mistral.rs). Documentation: [UQFF docs](https://github.com/EricLBuehler/mistral.rs/blob/master/docs/UQFF.md).
1) **Flexible** 🌀: Multiple quantization formats in *one* file format with *one* framework to run them all.
2) **Reliable** 🔒: Compatibility ensured with *embedded* and *checked* semantic versioning information from day 1.
3) **Easy** 🤗: Download UQFF models *easily* and *quickly* from Hugging Face, or use a local file.
3) **Customizable** 🛠️: Make and publish your own UQFF files in minutes.
## Examples
|Quantization type(s)|Example|
|--|--|
|FP8|`./mistralrs-server -i plain -m EricB/gemma-2-9b-it-UQFF --from-uqff gemma2-9b-instruct-f8e4m3.uqff`|
|HQQ4|`./mistralrs-server -i plain -m EricB/gemma-2-9b-it-UQFF --from-uqff gemma2-9b-instruct-hqq4.uqff`|
|HQQ8|`./mistralrs-server -i plain -m EricB/gemma-2-9b-it-UQFF --from-uqff gemma2-9b-instruct-hqq8.uqff`|
|Q3K|`./mistralrs-server -i plain -m EricB/gemma-2-9b-it-UQFF --from-uqff gemma2-9b-instruct-q3k.uqff`|
|Q4K|`./mistralrs-server -i plain -m EricB/gemma-2-9b-it-UQFF --from-uqff gemma2-9b-instruct-q4k.uqff`|
|Q5K|`./mistralrs-server -i plain -m EricB/gemma-2-9b-it-UQFF --from-uqff gemma2-9b-instruct-q5k.uqff`|
|Q8_0|`./mistralrs-server -i plain -m EricB/gemma-2-9b-it-UQFF --from-uqff gemma2-9b-instruct-q8_0.uqff`|
|
EricB/gemma-2-27b-it-UQFF | EricB | 2024-10-15T22:57:31Z | 5 | 0 | null | [
"gemma2",
"uqff",
"mistral.rs",
"base_model:google/gemma-2-27b-it",
"base_model:quantized:google/gemma-2-27b-it",
"region:us"
] | null | 2024-10-15T11:25:36Z | ---
tags:
- uqff
- mistral.rs
base_model: google/gemma-2-27b-it
base_model_relation: quantized
---
<!-- Autogenerated from user input. -->
# `google/gemma-2-27b-it`, UQFF quantization
Run with [mistral.rs](https://github.com/EricLBuehler/mistral.rs). Documentation: [UQFF docs](https://github.com/EricLBuehler/mistral.rs/blob/master/docs/UQFF.md).
1) **Flexible** 🌀: Multiple quantization formats in *one* file format with *one* framework to run them all.
2) **Reliable** 🔒: Compatibility ensured with *embedded* and *checked* semantic versioning information from day 1.
3) **Easy** 🤗: Download UQFF models *easily* and *quickly* from Hugging Face, or use a local file.
3) **Customizable** 🛠️: Make and publish your own UQFF files in minutes.
## Examples
|Quantization type(s)|Example|
|--|--|
|FP8|`./mistralrs-server -i plain -m EricB/gemma-2-27b-it-UQFF --from-uqff gemma2-27b-instruct-f8e4m3.uqff`|
|HQQ4|`./mistralrs-server -i plain -m EricB/gemma-2-27b-it-UQFF --from-uqff gemma2-27b-instruct-hqq4.uqff`|
|HQQ8|`./mistralrs-server -i plain -m EricB/gemma-2-27b-it-UQFF --from-uqff gemma2-27b-instruct-hqq8.uqff`|
|Q3K|`./mistralrs-server -i plain -m EricB/gemma-2-27b-it-UQFF --from-uqff gemma2-27b-instruct-q3k.uqff`|
|Q4K|`./mistralrs-server -i plain -m EricB/gemma-2-27b-it-UQFF --from-uqff gemma2-27b-instruct-q4k.uqff`|
|Q5K|`./mistralrs-server -i plain -m EricB/gemma-2-27b-it-UQFF --from-uqff gemma2-27b-instruct-q5k.uqff`|
|Q8_0|`./mistralrs-server -i plain -m EricB/gemma-2-27b-it-UQFF --from-uqff gemma2-27b-instruct-q8_0.uqff`|
|
EricB/gemma-2-2b-it-UQFF | EricB | 2024-10-15T22:57:22Z | 15 | 0 | null | [
"gemma2",
"uqff",
"mistral.rs",
"base_model:google/gemma-2-2b-it",
"base_model:quantized:google/gemma-2-2b-it",
"region:us"
] | null | 2024-10-15T10:29:40Z | ---
tags:
- uqff
- mistral.rs
base_model: google/gemma-2-2b-it
base_model_relation: quantized
---
<!-- Autogenerated from user input. -->
# `google/gemma-2-2b-it`, UQFF quantization
Run with [mistral.rs](https://github.com/EricLBuehler/mistral.rs). Documentation: [UQFF docs](https://github.com/EricLBuehler/mistral.rs/blob/master/docs/UQFF.md).
1) **Flexible** 🌀: Multiple quantization formats in *one* file format with *one* framework to run them all.
2) **Reliable** 🔒: Compatibility ensured with *embedded* and *checked* semantic versioning information from day 1.
3) **Easy** 🤗: Download UQFF models *easily* and *quickly* from Hugging Face, or use a local file.
3) **Customizable** 🛠️: Make and publish your own UQFF files in minutes.
## Examples
|Quantization type(s)|Example|
|--|--|
|FP8|`./mistralrs-server -i plain -m EricB/gemma-2-2b-it-UQFF --from-uqff gemma2-2b-instruct-f8e4m3.uqff`|
|HQQ4|`./mistralrs-server -i plain -m EricB/gemma-2-2b-it-UQFF --from-uqff gemma2-2b-instruct-hqq4.uqff`|
|HQQ8|`./mistralrs-server -i plain -m EricB/gemma-2-2b-it-UQFF --from-uqff gemma2-2b-instruct-hqq8.uqff`|
|Q3K|`./mistralrs-server -i plain -m EricB/gemma-2-2b-it-UQFF --from-uqff gemma2-2b-instruct-q3k.uqff`|
|Q4K|`./mistralrs-server -i plain -m EricB/gemma-2-2b-it-UQFF --from-uqff gemma2-2b-instruct-q4k.uqff`|
|Q5K|`./mistralrs-server -i plain -m EricB/gemma-2-2b-it-UQFF --from-uqff gemma2-2b-instruct-q5k.uqff`|
|Q8_0|`./mistralrs-server -i plain -m EricB/gemma-2-2b-it-UQFF --from-uqff gemma2-2b-instruct-q8_0.uqff`|
|
EricB/gemma-1.1-2b-it-UQFF | EricB | 2024-10-15T22:57:06Z | 6 | 0 | null | [
"gemma",
"uqff",
"mistral.rs",
"base_model:google/gemma-1.1-2b-it",
"base_model:quantized:google/gemma-1.1-2b-it",
"region:us"
] | null | 2024-10-15T10:17:16Z | ---
tags:
- uqff
- mistral.rs
base_model: google/gemma-1.1-2b-it
base_model_relation: quantized
---
<!-- Autogenerated from user input. -->
# `google/gemma-1.1-2b-it`, UQFF quantization
Run with [mistral.rs](https://github.com/EricLBuehler/mistral.rs). Documentation: [UQFF docs](https://github.com/EricLBuehler/mistral.rs/blob/master/docs/UQFF.md).
1) **Flexible** 🌀: Multiple quantization formats in *one* file format with *one* framework to run them all.
2) **Reliable** 🔒: Compatibility ensured with *embedded* and *checked* semantic versioning information from day 1.
3) **Easy** 🤗: Download UQFF models *easily* and *quickly* from Hugging Face, or use a local file.
3) **Customizable** 🛠️: Make and publish your own UQFF files in minutes.
## Examples
|Quantization type(s)|Example|
|--|--|
|FP8|`./mistralrs-server -i plain -m EricB/gemma-1.1-2b-it-UQFF --from-uqff gemma1.1-2b-instruct-f8e4m3.uqff`|
|HQQ4|`./mistralrs-server -i plain -m EricB/gemma-1.1-2b-it-UQFF --from-uqff gemma1.1-2b-instruct-hqq4.uqff`|
|HQQ8|`./mistralrs-server -i plain -m EricB/gemma-1.1-2b-it-UQFF --from-uqff gemma1.1-2b-instruct-hqq8.uqff`|
|Q3K|`./mistralrs-server -i plain -m EricB/gemma-1.1-2b-it-UQFF --from-uqff gemma1.1-2b-instruct-q3k.uqff`|
|Q4K|`./mistralrs-server -i plain -m EricB/gemma-1.1-2b-it-UQFF --from-uqff gemma1.1-2b-instruct-q4k.uqff`|
|Q5K|`./mistralrs-server -i plain -m EricB/gemma-1.1-2b-it-UQFF --from-uqff gemma1.1-2b-instruct-q5k.uqff`|
|Q8_0|`./mistralrs-server -i plain -m EricB/gemma-1.1-2b-it-UQFF --from-uqff gemma1.1-2b-instruct-q8_0.uqff`|
|
EricB/Mistral-Small-Instruct-2409-UQFF | EricB | 2024-10-15T22:56:41Z | 10 | 0 | null | [
"mistral",
"uqff",
"mistral.rs",
"base_model:mistralai/Mistral-Small-Instruct-2409",
"base_model:quantized:mistralai/Mistral-Small-Instruct-2409",
"region:us"
] | null | 2024-10-15T19:48:23Z | ---
tags:
- uqff
- mistral.rs
base_model: mistralai/Mistral-Small-Instruct-2409
base_model_relation: quantized
---
<!-- Autogenerated from user input. -->
# `mistralai/Mistral-Small-Instruct-2409`, UQFF quantization
Run with [mistral.rs](https://github.com/EricLBuehler/mistral.rs). Documentation: [UQFF docs](https://github.com/EricLBuehler/mistral.rs/blob/master/docs/UQFF.md).
1) **Flexible** 🌀: Multiple quantization formats in *one* file format with *one* framework to run them all.
2) **Reliable** 🔒: Compatibility ensured with *embedded* and *checked* semantic versioning information from day 1.
3) **Easy** 🤗: Download UQFF models *easily* and *quickly* from Hugging Face, or use a local file.
3) **Customizable** 🛠️: Make and publish your own UQFF files in minutes.
## Examples
|Quantization type(s)|Example|
|--|--|
|FP8|`./mistralrs-server -i plain -m EricB/Mistral-Small-Instruct-2409-UQFF --from-uqff mistral-small-2409-instruct-f8e4m3.uqff`|
|HQQ4|`./mistralrs-server -i plain -m EricB/Mistral-Small-Instruct-2409-UQFF --from-uqff mistral-small-2409-instruct-hqq4.uqff`|
|HQQ8|`./mistralrs-server -i plain -m EricB/Mistral-Small-Instruct-2409-UQFF --from-uqff mistral-small-2409-instruct-hqq8.uqff`|
|Q3K|`./mistralrs-server -i plain -m EricB/Mistral-Small-Instruct-2409-UQFF --from-uqff mistral-small-2409-instruct-q3k.uqff`|
|Q4K|`./mistralrs-server -i plain -m EricB/Mistral-Small-Instruct-2409-UQFF --from-uqff mistral-small-2409-instruct-q4k.uqff`|
|Q5K|`./mistralrs-server -i plain -m EricB/Mistral-Small-Instruct-2409-UQFF --from-uqff mistral-small-2409-instruct-q5k.uqff`|
|Q8_0|`./mistralrs-server -i plain -m EricB/Mistral-Small-Instruct-2409-UQFF --from-uqff mistral-small-2409-instruct-q8_0.uqff`|
|
MeghanaNanuvala/PPO-Huggy | MeghanaNanuvala | 2024-10-15T22:55:44Z | 8 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | 2024-10-15T22:55:39Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: MeghanaNanuvala/PPO-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
mav23/MentaLLaMA-chat-7B-GGUF | mav23 | 2024-10-15T22:53:19Z | 70 | 0 | null | [
"gguf",
"medical",
"en",
"arxiv:2309.13567",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2024-10-15T22:14:43Z | ---
license: mit
language:
- en
metrics:
- f1
tags:
- medical
---
# Introduction
MentaLLaMA-chat-7B is part of the [MentaLLaMA](https://github.com/SteveKGYang/MentalLLaMA) project, the first open-source large language model (LLM) series for
interpretable mental health analysis with instruction-following capability. This model is finetuned based on the Meta LLaMA2-chat-7B foundation model and the full IMHI instruction tuning data.
The model is expected to make complex mental health analysis for various mental health conditions and give reliable explanations for each of its predictions.
It is fine-tuned on the IMHI dataset with 75K high-quality natural language instructions to boost its performance in downstream tasks.
We perform a comprehensive evaluation on the IMHI benchmark with 20K test samples. The result shows that MentalLLaMA approaches state-of-the-art discriminative
methods in correctness and generates high-quality explanations.
# Ethical Consideration
Although experiments on MentaLLaMA show promising performance on interpretable mental health analysis, we stress that
all predicted results and generated explanations should only used
for non-clinical research, and the help-seeker should get assistance
from professional psychiatrists or clinical practitioners. In addition,
recent studies have indicated LLMs may introduce some potential
bias, such as gender gaps. Meanwhile, some incorrect prediction results, inappropriate explanations, and over-generalization
also illustrate the potential risks of current LLMs. Therefore, there
are still many challenges in applying the model to real-scenario
mental health monitoring systems.
## Other Models in MentaLLaMA
In addition to MentaLLaMA-chat-7B, the MentaLLaMA project includes another model: MentaLLaMA-chat-13B, MentalBART, MentalT5.
- **MentaLLaMA-chat-13B**: This model is finetuned based on the Meta LLaMA2-chat-13B foundation model and the full IMHI instruction tuning data. The training data covers 10 mental health analysis tasks.
- **MentalBART**: This model is finetuned based on the BART-large foundation model and the full IMHI-completion data. The training data covers 10 mental health analysis tasks. This model doesn't have instruction-following ability but is more lightweight and performs well in interpretable mental health analysis in a completion-based manner.
- **MentalT5**: This model is finetuned based on the T5-large foundation model and the full IMHI-completion data. The training data covers 10 mental health analysis tasks. This model doesn't have instruction-following ability but is more lightweight and performs well in interpretable mental health analysis in a completion-based manner.
## Usage
You can use the MentaLLaMA-chat-7B model in your Python project with the Hugging Face Transformers library. Here is a simple example of how to load the model:
```python
from transformers import LlamaTokenizer, LlamaForCausalLM
tokenizer = LlamaTokenizer.from_pretrained('klyang/MentaLLaMA-chat-7B')
model = LlamaForCausalLM.from_pretrained('klyang/MentaLLaMA-chat-7B', device_map='auto')
```
In this example, LlamaTokenizer is used to load the tokenizer, and LlamaForCausalLM is used to load the model. The `device_map='auto'` argument is used to automatically
use the GPU if it's available.
## License
MentaLLaMA-chat-7B is licensed under MIT. For more details, please see the MIT file.
## Citation
If you use MentaLLaMA-chat-7B in your work, please cite the our paper:
```bibtex
@misc{yang2023mentalllama,
title={MentalLLaMA: Interpretable Mental Health Analysis on Social Media with Large Language Models},
author={Kailai Yang and Tianlin Zhang and Ziyan Kuang and Qianqian Xie and Sophia Ananiadou},
year={2023},
eprint={2309.13567},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
binaryben/healthwatch-model | binaryben | 2024-10-15T22:52:22Z | 114 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-15T22:45:35Z | ---
library_name: transformers
tags: []
---
# 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
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[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. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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[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]
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## 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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## 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
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