modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
leixa/2497017e-5a57-4304-a837-936a9df0cbfd
|
leixa
| 2025-02-04T08:49:46Z | 10 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:Vikhrmodels/Vikhr-7B-instruct_0.4",
"base_model:adapter:Vikhrmodels/Vikhr-7B-instruct_0.4",
"region:us"
] | null | 2025-02-04T08:29:11Z |
---
library_name: peft
base_model: Vikhrmodels/Vikhr-7B-instruct_0.4
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 2497017e-5a57-4304-a837-936a9df0cbfd
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: Vikhrmodels/Vikhr-7B-instruct_0.4
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 518668ad06c086c3_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/518668ad06c086c3_train_data.json
type:
field_instruction: text_1
field_output: text_2
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: leixa/2497017e-5a57-4304-a837-936a9df0cbfd
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: 0
logging_steps: 3
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_steps: 100
micro_batch_size: 8
mlflow_experiment_name: /tmp/518668ad06c086c3_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: techspear-hub
wandb_mode: online
wandb_name: 852c219d-b7b5-4007-b3fb-eda3270ecb7b
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 852c219d-b7b5-4007-b3fb-eda3270ecb7b
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 2497017e-5a57-4304-a837-936a9df0cbfd
This model is a fine-tuned version of [Vikhrmodels/Vikhr-7B-instruct_0.4](https://huggingface.co/Vikhrmodels/Vikhr-7B-instruct_0.4) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3248
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0038 | 1 | 2.7950 |
| 2.1632 | 0.0339 | 9 | 1.8752 |
| 1.6088 | 0.0677 | 18 | 1.5384 |
| 1.6145 | 0.1016 | 27 | 1.4449 |
| 1.4734 | 0.1355 | 36 | 1.4071 |
| 1.3238 | 0.1693 | 45 | 1.3813 |
| 1.3856 | 0.2032 | 54 | 1.3622 |
| 1.4164 | 0.2371 | 63 | 1.3447 |
| 1.3416 | 0.2709 | 72 | 1.3357 |
| 1.2816 | 0.3048 | 81 | 1.3282 |
| 1.4148 | 0.3387 | 90 | 1.3253 |
| 1.4971 | 0.3725 | 99 | 1.3248 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
mergekit-community/LLAMA-1b-NIGGERKILLER
|
mergekit-community
| 2025-02-04T08:49:43Z | 12 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2403.19522",
"base_model:CarrotAI/Llama-3.2-Rabbit-Ko-1B-Instruct",
"base_model:merge:CarrotAI/Llama-3.2-Rabbit-Ko-1B-Instruct",
"base_model:Trelis/Llama-3.2-1B-Instruct-MATH-synthetic",
"base_model:merge:Trelis/Llama-3.2-1B-Instruct-MATH-synthetic",
"base_model:huihui-ai/MicroThinker-1B-Preview",
"base_model:merge:huihui-ai/MicroThinker-1B-Preview",
"base_model:passing2961/Thanos-1B",
"base_model:merge:passing2961/Thanos-1B",
"base_model:prithivMLmods/Bellatrix-Tiny-1B-R1",
"base_model:merge:prithivMLmods/Bellatrix-Tiny-1B-R1",
"base_model:prithivMLmods/Llama-Express.1-Math",
"base_model:merge:prithivMLmods/Llama-Express.1-Math",
"base_model:unsloth/Llama-3.2-1B-Instruct",
"base_model:merge:unsloth/Llama-3.2-1B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-02-04T08:48:37Z |
---
base_model:
- prithivMLmods/Bellatrix-Tiny-1B-R1
- Trelis/Llama-3.2-1B-Instruct-MATH-synthetic
- unsloth/Llama-3.2-1B-Instruct
- huihui-ai/MicroThinker-1B-Preview
- passing2961/Thanos-1B
- CarrotAI/Llama-3.2-Rabbit-Ko-1B-Instruct
- prithivMLmods/Llama-Express.1-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).
## Merge Details
### Merge Method
This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [unsloth/Llama-3.2-1B-Instruct](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct) as a base.
### Models Merged
The following models were included in the merge:
* [prithivMLmods/Bellatrix-Tiny-1B-R1](https://huggingface.co/prithivMLmods/Bellatrix-Tiny-1B-R1)
* [Trelis/Llama-3.2-1B-Instruct-MATH-synthetic](https://huggingface.co/Trelis/Llama-3.2-1B-Instruct-MATH-synthetic)
* [huihui-ai/MicroThinker-1B-Preview](https://huggingface.co/huihui-ai/MicroThinker-1B-Preview)
* [passing2961/Thanos-1B](https://huggingface.co/passing2961/Thanos-1B)
* [CarrotAI/Llama-3.2-Rabbit-Ko-1B-Instruct](https://huggingface.co/CarrotAI/Llama-3.2-Rabbit-Ko-1B-Instruct)
* [prithivMLmods/Llama-Express.1-Math](https://huggingface.co/prithivMLmods/Llama-Express.1-Math)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: unsloth/Llama-3.2-1B-Instruct
- model: Trelis/Llama-3.2-1B-Instruct-MATH-synthetic
- model: prithivMLmods/Llama-Express.1-Math
- model: passing2961/Thanos-1B
- model: CarrotAI/Llama-3.2-Rabbit-Ko-1B-Instruct
- model: huihui-ai/MicroThinker-1B-Preview
- model: prithivMLmods/Bellatrix-Tiny-1B-R1
base_model: unsloth/Llama-3.2-1B-Instruct
merge_method: model_stock
dtype: bfloat16
```
|
2Jyq/llm4decompile-9b-v2-GGUF
|
2Jyq
| 2025-02-04T08:49:22Z | 336 | 0 | null |
[
"gguf",
"decompile",
"binary",
"llama-cpp",
"base_model:LLM4Binary/llm4decompile-9b-v2",
"base_model:quantized:LLM4Binary/llm4decompile-9b-v2",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-02-03T17:29:53Z |
---
license: mit
base_model:
- LLM4Binary/llm4decompile-9b-v2
tags:
- decompile
- binary
- llama-cpp
---
# 2Jyq/llm4decompile-9b-v2-GGUF
This model was converted to GGUF format from [`LLM4Binary/llm4decompile-9b-v2`](https://huggingface.co/LLM4Binary/llm4decompile-9b-v2) using [llama.cpp](https://github.com/ggerganov/llama.cpp/).
Refer to the [original model card](https://huggingface.co/LLM4Binary/llm4decompile-9b-v2) for more details on the model.
|
n3Er/qwen2.5-3b-instruct-finqa
|
n3Er
| 2025-02-04T08:47:50Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-02-03T22:56:31Z |
---
library_name: transformers
tags:
- unsloth
- trl
- sft
---
# 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]
|
WiroAI/Levi-Ackerman-Flux-LoRA
|
WiroAI
| 2025-02-04T08:47:27Z | 23 | 1 |
diffusers
|
[
"diffusers",
"text-to-image",
"flux",
"lora",
"transformers",
"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
| 2025-02-04T08:34:46Z |
---
tags:
- text-to-image
- flux
- lora
- diffusers
- transformers
- template:sd-lora
- ai-toolkit
widget:
- text: leviwiro, A close-up anime-style portrait of a disciplined fighter with an intense, unwavering stare.
His face is partly illuminated by the soft flickering of a lantern, revealing subtle scars and signs of exhaustion.
His dark green cloak wraps around his shoulders like a shield, and his high-collared black uniform gives him a sharp,
commanding presence. A single raindrop slides down his cheek, but his focus remains unshaken.
output:
url: levi3.png
- text: leviwiro, A dramatic ultra-HD anime-style illustration of a fearless soldier standing on a rooftop at sunrise,
the golden light casting long shadows behind him. His dark cloak flows in the wind,
and his expression remains stoic despite the chaos below. The battlefield is filled with wreckage and fallen comrades,
but he stands firm, gripping his twin blades tightly.
The sky is painted in hues of orange and red, marking the beginning of another brutal day.
output:
url: levi2.png
- text: leviwiro, A highly detailed anime-style illustration of a master tactician seated at a wooden table,
surrounded by maps, reports, and scattered weapons. His sharp eyes scan the documents in front of him,
analyzing every possible move before the next battle. His dark, slicked-back hair falls slightly out of place,
revealing a tired but focused expression. Candlelight flickers in the dimly lit room,
casting deep shadows that accentuate his sharp features and unwavering resolve.
output:
url: levi4.png
license: other
instance_prompt: leviwiro
base_model:
- black-forest-labs/FLUX.1-dev
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
<div align="center">
<img src="https://huggingface.co/WiroAI/wiroai-turkish-llm-9b/resolve/main/wiro_logo.png" width="15%" alt="Wiro AI" />
</div>
<hr>
<div align="center" style="line-height: 1;">
<a href="https://www.wiro.ai/" target="_blank" style="margin: 2px;">
<img alt="Homepage" src="https://huggingface.co/WiroAI/wiroai-turkish-llm-9b/resolve/main/homepage.svg" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://huggingface.co/WiroAI" target="_blank" style="margin: 2px;">
<img alt="Hugging Face" src="https://huggingface.co/WiroAI/wiroai-turkish-llm-9b/resolve/main/huggingface.svg" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://civitai.com/user/wiroai" target="_blank" style="margin: 2px;">
<img alt="CivitAI" src="https://huggingface.co/WiroAI/pokemon-flux-lora/resolve/main/civitai.svg" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="https://instagram.com/wiroai" target="_blank" style="margin: 2px;">
<img alt="Instagram Follow" src="https://img.shields.io/badge/Instagram-wiroai-555555?logo=instagram&logoColor=white&labelColor=E4405F" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://x.com/wiroai" target="_blank" style="margin: 2px;">
<img alt="X Follow" src="https://img.shields.io/badge/X-wiroai-555555?logo=x&logoColor=white&labelColor=000000" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="https://wiro.ai/agreement/terms-of-service" style="margin: 2px;">
<img alt="License" src="https://img.shields.io/badge/License-apache 2.0-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
## Model Details
### Model Description
This LoRA is trained for anyone who like Levi Ackerman from Attack on Titan.
- **Developed by:** [Wiro AI - ML Team]
- **Shared by:** [Wiro AI](https://wiro.ai/)
<Gallery />
## Trigger words
You should use `leviwiro` to trigger the image generation.
## Civitai model link: [civitai](https://civitai.com/models/1216175/levi-ackerman-attack-on-titan-flux-lora)
```py
from diffusers import FluxPipeline
import torch
pipeline = FluxPipeline.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('WiroAI/Levi-Ackerman-Flux-LoRA', weight_name='levi_flux_lora.safetensors')
image = pipeline('leviwiro, A close-up anime-style portrait of a disciplined fighter with an intense, unwavering stare. His face is partly illuminated by the soft flickering of a lantern, revealing subtle scars and signs of exhaustion. His dark green cloak wraps around his shoulders like a shield, and his high-collared black uniform gives him a sharp, commanding presence. A single raindrop slides down his cheek, but his focus remains unshaken.').images[0]
image.save("output.png")
```
|
bigband/ProsperousTezcatlipoca
|
bigband
| 2025-02-04T08:47:09Z | 11 | 0 |
vllm
|
[
"vllm",
"safetensors",
"mistral",
"text-generation",
"transformers",
"conversational",
"en",
"fr",
"de",
"es",
"it",
"pt",
"zh",
"ja",
"ru",
"ko",
"base_model:mistralai/Mistral-Small-24B-Base-2501",
"base_model:finetune:mistralai/Mistral-Small-24B-Base-2501",
"license:apache-2.0",
"text-generation-inference",
"region:us"
] |
text-generation
| 2025-02-04T08:36:19Z |
---
language:
- en
- fr
- de
- es
- it
- pt
- zh
- ja
- ru
- ko
license: apache-2.0
library_name: vllm
inference: false
base_model:
- mistralai/Mistral-Small-24B-Base-2501
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>.
tags:
- transformers
---
# Model Card for Mistral-Small-24B-Instruct-2501
Mistral Small 3 ( 2501 ) sets a new benchmark in the "small" Large Language Models category below 70B, boasting 24B parameters and achieving state-of-the-art capabilities comparable to larger models!
This model is an instruction-fine-tuned version of the base model: [Mistral-Small-24B-Base-2501](https://huggingface.co/mistralai/Mistral-Small-24B-Base-2501).
Mistral Small can be deployed locally and is exceptionally "knowledge-dense", fitting in a single RTX 4090 or a 32GB RAM MacBook once quantized.
Perfect for:
- Fast response conversational agents.
- Low latency function calling.
- Subject matter experts via fine-tuning.
- Local inference for hobbyists and organizations handling sensitive data.
For enterprises that need specialized capabilities (increased context, particular modalities, domain specific knowledge, etc.), we will be releasing commercial models beyond what Mistral AI contributes to the community.
This release demonstrates our commitment to open source, serving as a strong base model.
Learn more about Mistral Small in our [blog post](https://mistral.ai/news/mistral-small-3/).
Model developper: Mistral AI Team
## Key Features
- **Multilingual:** Supports dozens of languages, including English, French, German, Spanish, Italian, Chinese, Japanese, Korean, Portuguese, Dutch, and Polish.
- **Agent-Centric:** Offers best-in-class agentic capabilities with native function calling and JSON outputting.
- **Advanced Reasoning:** State-of-the-art conversational and reasoning capabilities.
- **Apache 2.0 License:** Open license allowing usage and modification for both commercial and non-commercial purposes.
- **Context Window:** A 32k context window.
- **System Prompt:** Maintains strong adherence and support for system prompts.
- **Tokenizer:** Utilizes a Tekken tokenizer with a 131k vocabulary size.
## Benchmark results
### Human evaluated benchmarks
| Category | Gemma-2-27B | Qwen-2.5-32B | Llama-3.3-70B | Gpt4o-mini |
|----------|-------------|--------------|---------------|------------|
| Mistral is better | 0.536 | 0.496 | 0.192 | 0.200 |
| Mistral is slightly better | 0.196 | 0.184 | 0.164 | 0.204 |
| Ties | 0.052 | 0.060 | 0.236 | 0.160 |
| Other is slightly better | 0.060 | 0.088 | 0.112 | 0.124 |
| Other is better | 0.156 | 0.172 | 0.296 | 0.312 |
**Note**:
- We conducted side by side evaluations with an external third-party vendor, on a set of over 1k proprietary coding and generalist prompts.
- Evaluators were tasked with selecting their preferred model response from anonymized generations produced by Mistral Small 3 vs another model.
- We are aware that in some cases the benchmarks on human judgement starkly differ from publicly available benchmarks, but have taken extra caution in verifying a fair evaluation. We are confident that the above benchmarks are valid.
### Publicly accesible benchmarks
**Reasoning & Knowledge**
| Evaluation | mistral-small-24B-instruct-2501 | gemma-2b-27b | llama-3.3-70b | qwen2.5-32b | gpt-4o-mini-2024-07-18 |
|------------|---------------|--------------|---------------|---------------|-------------|
| mmlu_pro_5shot_cot_instruct | 0.663 | 0.536 | 0.666 | 0.683 | 0.617 |
| gpqa_main_cot_5shot_instruct | 0.453 | 0.344 | 0.531 | 0.404 | 0.377 |
**Math & Coding**
| Evaluation | mistral-small-24B-instruct-2501 | gemma-2b-27b | llama-3.3-70b | qwen2.5-32b | gpt-4o-mini-2024-07-18 |
|------------|---------------|--------------|---------------|---------------|-------------|
| humaneval_instruct_pass@1 | 0.848 | 0.732 | 0.854 | 0.909 | 0.890 |
| math_instruct | 0.706 | 0.535 | 0.743 | 0.819 | 0.761 |
**Instruction following**
| Evaluation | mistral-small-24B-instruct-2501 | gemma-2b-27b | llama-3.3-70b | qwen2.5-32b | gpt-4o-mini-2024-07-18 |
|------------|---------------|--------------|---------------|---------------|-------------|
| mtbench_dev | 8.35 | 7.86 | 7.96 | 8.26 | 8.33 |
| wildbench | 52.27 | 48.21 | 50.04 | 52.73 | 56.13 |
| arena_hard | 0.873 | 0.788 | 0.840 | 0.860 | 0.897 |
| ifeval | 0.829 | 0.8065 | 0.8835 | 0.8401 | 0.8499 |
**Note**:
- Performance accuracy on all benchmarks were obtained through the same internal evaluation pipeline - as such, numbers may vary slightly from previously reported performance
([Qwen2.5-32B-Instruct](https://qwenlm.github.io/blog/qwen2.5/), [Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct), [Gemma-2-27B-IT](https://huggingface.co/google/gemma-2-27b-it)).
- Judge based evals such as Wildbench, Arena hard and MTBench were based on gpt-4o-2024-05-13.
### Basic Instruct Template (V7-Tekken)
```
<s>[SYSTEM_PROMPT]<system prompt>[/SYSTEM_PROMPT][INST]<user message>[/INST]<assistant response></s>[INST]<user message>[/INST]
```
*`<system_prompt>`, `<user message>` and `<assistant response>` are placeholders.*
***Please make sure to use [mistral-common](https://github.com/mistralai/mistral-common) as the source of truth***
## Usage
The model can be used with the following frameworks;
- [`vllm`](https://github.com/vllm-project/vllm): See [here](#vllm)
- [`transformers`](https://github.com/huggingface/transformers): See [here](#transformers)
### vLLM
We recommend using this model with the [vLLM library](https://github.com/vllm-project/vllm)
to implement production-ready inference pipelines.
**Note 1**: We recommond using a relatively low temperature, such as `temperature=0.15`.
**Note 2**: Make sure to add a system prompt to the model to best tailer it for your needs. If you want to use the model as a general assistant, we recommend the following
system prompt:
```
system_prompt = """You are Mistral Small 3, a Large Language Model (LLM) created by Mistral AI, a French startup headquartered in Paris.
Your knowledge base was last updated on 2023-10-01. The current date is 2025-01-30.
When you're not sure about some information, you say that you don't have the information and don't make up anything.
If the user's question is not clear, ambiguous, or does not provide enough context for you to accurately answer the question, you do not try to answer it right away and you rather ask the user to clarify their request (e.g. \"What are some good restaurants around me?\" => \"Where are you?\" or \"When is the next flight to Tokyo\" => \"Where do you travel from?\")"""
```
**_Installation_**
Make sure you install [`vLLM >= 0.6.4`](https://github.com/vllm-project/vllm/releases/tag/v0.6.4):
```
pip install --upgrade vllm
```
Also make sure you have [`mistral_common >= 1.5.2`](https://github.com/mistralai/mistral-common/releases/tag/v1.5.2) installed:
```
pip install --upgrade mistral_common
```
You can also make use of a ready-to-go [docker image](https://github.com/vllm-project/vllm/blob/main/Dockerfile) or on the [docker hub](https://hub.docker.com/layers/vllm/vllm-openai/latest/images/sha256-de9032a92ffea7b5c007dad80b38fd44aac11eddc31c435f8e52f3b7404bbf39).
#### Server
We recommand that you use Mistral-Small-24B-Instruct-2501 in a server/client setting.
1. Spin up a server:
```
vllm serve mistralai/Mistral-Small-24B-Instruct-2501 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice
```
**Note:** Running Mistral-Small-24B-Instruct-2501 on GPU requires ~55 GB of GPU RAM in bf16 or fp16.
2. To ping the client you can use a simple Python snippet.
```py
import requests
import json
from datetime import datetime, timedelta
url = "http://<your-server>:8000/v1/chat/completions"
headers = {"Content-Type": "application/json", "Authorization": "Bearer token"}
model = "mistralai/Mistral-Small-24B-Instruct-2501"
messages = [
{
"role": "system",
"content": "You are a conversational agent that always answers straight to the point, always end your accurate response with an ASCII drawing of a cat."
},
{
"role": "user",
"content": "Give me 5 non-formal ways to say 'See you later' in French."
},
]
data = {"model": model, "messages": messages}
response = requests.post(url, headers=headers, data=json.dumps(data))
print(response.json()["choices"][0]["message"]["content"])
# Sure, here are five non-formal ways to say "See you later" in French:
#
# 1. À plus tard
# 2. À plus
# 3. Salut
# 4. À toute
# 5. Bisous
#
# ```
# /\_/\
# ( o.o )
# > ^ <
# ```
```
### Function calling
Mistral-Small-24-Instruct-2501 is excellent at function / tool calling tasks via vLLM. *E.g.:*
<details>
<summary>Example</summary>
```py
import requests
import json
from huggingface_hub import hf_hub_download
from datetime import datetime, timedelta
url = "http://<your-url>:8000/v1/chat/completions"
headers = {"Content-Type": "application/json", "Authorization": "Bearer token"}
model = "mistralai/Mistral-Small-24B-Instruct-2501"
def load_system_prompt(repo_id: str, filename: str) -> str:
file_path = hf_hub_download(repo_id=repo_id, filename=filename)
with open(file_path, "r") as file:
system_prompt = file.read()
today = datetime.today().strftime("%Y-%m-%d")
yesterday = (datetime.today() - timedelta(days=1)).strftime("%Y-%m-%d")
model_name = repo_id.split("/")[-1]
return system_prompt.format(name=model_name, today=today, yesterday=yesterday)
SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt")
tools = [
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"city": {
"type": "string",
"description": "The city to find the weather for, e.g. 'San Francisco'",
},
"state": {
"type": "string",
"description": "The state abbreviation, e.g. 'CA' for California",
},
"unit": {
"type": "string",
"description": "The unit for temperature",
"enum": ["celsius", "fahrenheit"],
},
},
"required": ["city", "state", "unit"],
},
},
},
{
"type": "function",
"function": {
"name": "rewrite",
"description": "Rewrite a given text for improved clarity",
"parameters": {
"type": "object",
"properties": {
"text": {
"type": "string",
"description": "The input text to rewrite",
}
},
},
},
},
]
messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{
"role": "user",
"content": "Could you please make the below article more concise?\n\nOpenAI is an artificial intelligence research laboratory consisting of the non-profit OpenAI Incorporated and its for-profit subsidiary corporation OpenAI Limited Partnership.",
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "bbc5b7ede",
"type": "function",
"function": {
"name": "rewrite",
"arguments": '{"text": "OpenAI is an artificial intelligence research laboratory consisting of the non-profit OpenAI Incorporated and its for-profit subsidiary corporation OpenAI Limited Partnership."}',
},
}
],
},
{
"role": "tool",
"content": '{"action":"rewrite","outcome":"OpenAI is a FOR-profit company."}',
"tool_call_id": "bbc5b7ede",
"name": "rewrite",
},
{
"role": "assistant",
"content": "---\n\nOpenAI is a FOR-profit company.",
},
{
"role": "user",
"content": "Can you tell me what the temperature will be in Dallas, in Fahrenheit?",
},
]
data = {"model": model, "messages": messages, "tools": tools}
response = requests.post(url, headers=headers, data=json.dumps(data))
import ipdb; ipdb.set_trace()
print(response.json()["choices"][0]["message"]["tool_calls"])
# [{'id': '8PdihwL6d', 'type': 'function', 'function': {'name': 'get_current_weather', 'arguments': '{"city": "Dallas", "state": "TX", "unit": "fahrenheit"}'}}]
```
</details>
#### Offline
```py
from vllm import LLM
from vllm.sampling_params import SamplingParams
from datetime import datetime, timedelta
SYSTEM_PROMPT = "You are a conversational agent that always answers straight to the point, always end your accurate response with an ASCII drawing of a cat."
user_prompt = "Give me 5 non-formal ways to say 'See you later' in French."
messages = [
{
"role": "system",
"content": SYSTEM_PROMPT
},
{
"role": "user",
"content": user_prompt
},
]
# note that running this model on GPU requires over 60 GB of GPU RAM
llm = LLM(model=model_name, tokenizer_mode="mistral", tensor_parallel_size=8)
sampling_params = SamplingParams(max_tokens=512, temperature=0.15)
outputs = llm.chat(messages, sampling_params=sampling_params)
print(outputs[0].outputs[0].text)
# Sure, here are five non-formal ways to say "See you later" in French:
#
# 1. À plus tard
# 2. À plus
# 3. Salut
# 4. À toute
# 5. Bisous
#
# ```
# /\_/\
# ( o.o )
# > ^ <
# ```
```
### Transformers
If you want to use Hugging Face transformers to generate text, you can do something like this.
```py
from transformers import pipeline
import torch
messages = [
{"role": "user", "content": "Give me 5 non-formal ways to say 'See you later' in French."},
]
chatbot = pipeline("text-generation", model="mistralai/Mistral-Small-24B-Instruct-2501", max_new_tokens=256, torch_dtype=torch.bfloat16)
chatbot(messages)
```
### Ollama
[Ollama](https://github.com/ollama/ollama) can run this model locally on MacOS, Windows and Linux.
```
ollama run mistral-small
```
4-bit quantization (aliased to default):
```
ollama run mistral-small:24b-instruct-2501-q4_K_M
```
8-bit quantization:
```
ollama run mistral-small:24b-instruct-2501-q8_0
```
FP16:
```
ollama run mistral-small:24b-instruct-2501-fp16
```
|
hungryc9/deval10
|
hungryc9
| 2025-02-04T08:44:17Z | 9 | 0 | null |
[
"safetensors",
"llama",
"facebook",
"meta",
"pytorch",
"llama-3",
"text-generation",
"conversational",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"arxiv:2204.05149",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:finetune:meta-llama/Llama-3.1-8B",
"license:llama3.1",
"region:us"
] |
text-generation
| 2025-02-04T08:33:20Z |
---
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
license: llama3.1
base_model: meta-llama/Meta-Llama-3.1-8B
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
extra_gated_prompt: "### LLAMA 3.1 COMMUNITY LICENSE AGREEMENT\nLlama 3.1 Version\
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#### Prohibited Uses\nWe want everyone to use Llama 3.1 safely and responsibly.\
\ You agree you will not use, or allow others to use, Llama 3.1 to:\n 1. Violate\
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\ Traffic Arms Regulations (ITAR) maintained by the United States Department of\
\ State\n 2. Guns and illegal weapons (including weapon development)\n 3.\
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\ infrastructure, transportation technologies, or heavy machinery\n 5. Self-harm\
\ or harm to others, including suicide, cutting, and eating disorders\n 6. Any\
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\ harm to an individual\n3. Intentionally deceive or mislead others, including use\
\ of Llama 3.1 related to the following:\n 1. Generating, promoting, or furthering\
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extra_gated_fields:
First Name: text
Last Name: text
Date of birth: date_picker
Country: country
Affiliation: text
Job title:
type: select
options:
- Student
- Research Graduate
- AI researcher
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geo: ip_location
? By clicking Submit below I accept the terms of the license and acknowledge that
the information I provide will be collected stored processed and shared in accordance
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extra_gated_description: The information you provide will be collected, stored, processed
and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).
extra_gated_button_content: Submit
---
## Model Information
The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.
**Model developer**: Meta
**Model Architecture:** Llama 3.1 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
<table>
<tr>
<td>
</td>
<td><strong>Training Data</strong>
</td>
<td><strong>Params</strong>
</td>
<td><strong>Input modalities</strong>
</td>
<td><strong>Output modalities</strong>
</td>
<td><strong>Context length</strong>
</td>
<td><strong>GQA</strong>
</td>
<td><strong>Token count</strong>
</td>
<td><strong>Knowledge cutoff</strong>
</td>
</tr>
<tr>
<td rowspan="3" >Llama 3.1 (text only)
</td>
<td rowspan="3" >A new mix of publicly available online data.
</td>
<td>8B
</td>
<td>Multilingual Text
</td>
<td>Multilingual Text and code
</td>
<td>128k
</td>
<td>Yes
</td>
<td rowspan="3" >15T+
</td>
<td rowspan="3" >December 2023
</td>
</tr>
<tr>
<td>70B
</td>
<td>Multilingual Text
</td>
<td>Multilingual Text and code
</td>
<td>128k
</td>
<td>Yes
</td>
</tr>
<tr>
<td>405B
</td>
<td>Multilingual Text
</td>
<td>Multilingual Text and code
</td>
<td>128k
</td>
<td>Yes
</td>
</tr>
</table>
**Supported languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
**Llama 3.1 family of models**. Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date:** July 23, 2024.
**Status:** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License:** A custom commercial license, the Llama 3.1 Community License, is available at: [https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE)
Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3.1 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases** Llama 3.1 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. The Llama 3.1 model collection also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation. The Llama 3.1 Community License allows for these use cases.
**Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.1 Community License. Use in languages beyond those explicitly referenced as supported in this model card**.
**<span style="text-decoration:underline;">Note</span>: Llama 3.1 has been trained on a broader collection of languages than the 8 supported languages. Developers may fine-tune Llama 3.1 models for languages beyond the 8 supported languages provided they comply with the Llama 3.1 Community License and the Acceptable Use Policy and in such cases are responsible for ensuring that any uses of Llama 3.1 in additional languages is done in a safe and responsible manner.
## How to use
This repository contains two versions of Meta-Llama-3.1-8B-Instruct, for use with transformers and with the original `llama` codebase.
### Use with transformers
Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function.
Make sure to update your transformers installation via `pip install --upgrade transformers`.
```python
import transformers
import torch
model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
outputs = pipeline(
messages,
max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```
Note: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantised and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes)
### Tool use with transformers
LLaMA-3.1 supports multiple tool use formats. You can see a full guide to prompt formatting [here](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/).
Tool use is also supported through [chat templates](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling) in Transformers.
Here is a quick example showing a single simple tool:
```python
# First, define a tool
def get_current_temperature(location: str) -> float:
"""
Get the current temperature at a location.
Args:
location: The location to get the temperature for, in the format "City, Country"
Returns:
The current temperature at the specified location in the specified units, as a float.
"""
return 22. # A real function should probably actually get the temperature!
# Next, create a chat and apply the chat template
messages = [
{"role": "system", "content": "You are a bot that responds to weather queries."},
{"role": "user", "content": "Hey, what's the temperature in Paris right now?"}
]
inputs = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True)
```
You can then generate text from this input as normal. If the model generates a tool call, you should add it to the chat like so:
```python
tool_call = {"name": "get_current_temperature", "arguments": {"location": "Paris, France"}}
messages.append({"role": "assistant", "tool_calls": [{"type": "function", "function": tool_call}]})
```
and then call the tool and append the result, with the `tool` role, like so:
```python
messages.append({"role": "tool", "name": "get_current_temperature", "content": "22.0"})
```
After that, you can `generate()` again to let the model use the tool result in the chat. Note that this was a very brief introduction to tool calling - for more information,
see the [LLaMA prompt format docs](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/) and the Transformers [tool use documentation](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling).
### Use with `llama`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama)
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Meta-Llama-3.1-8B-Instruct --include "original/*" --local-dir Meta-Llama-3.1-8B-Instruct
```
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, annotation, and evaluation were also performed on production infrastructure.
**Training utilized a cumulative of** 39.3M GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency.
**Training Greenhouse Gas Emissions** Estimated total location-based greenhouse gas emissions were **11,390** tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy, therefore the total market-based greenhouse gas emissions for training were 0 tons CO2eq.
<table>
<tr>
<td>
</td>
<td><strong>Training Time (GPU hours)</strong>
</td>
<td><strong>Training Power Consumption (W)</strong>
</td>
<td><strong>Training Location-Based Greenhouse Gas Emissions</strong>
<p>
<strong>(tons CO2eq)</strong>
</td>
<td><strong>Training Market-Based Greenhouse Gas Emissions</strong>
<p>
<strong>(tons CO2eq)</strong>
</td>
</tr>
<tr>
<td>Llama 3.1 8B
</td>
<td>1.46M
</td>
<td>700
</td>
<td>420
</td>
<td>0
</td>
</tr>
<tr>
<td>Llama 3.1 70B
</td>
<td>7.0M
</td>
<td>700
</td>
<td>2,040
</td>
<td>0
</td>
</tr>
<tr>
<td>Llama 3.1 405B
</td>
<td>30.84M
</td>
<td>700
</td>
<td>8,930
</td>
<td>0
</td>
</tr>
<tr>
<td>Total
</td>
<td>39.3M
<td>
<ul>
</ul>
</td>
<td>11,390
</td>
<td>0
</td>
</tr>
</table>
The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others.
## Training Data
**Overview:** Llama 3.1 was pretrained on ~15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 25M synthetically generated examples.
**Data Freshness:** The pretraining data has a cutoff of December 2023.
## Benchmark scores
In this section, we report the results for Llama 3.1 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library.
### Base pretrained models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong># Shots</strong>
</td>
<td><strong>Metric</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama 3.1 8B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama 3.1 70B</strong>
</td>
<td><strong>Llama 3.1 405B</strong>
</td>
</tr>
<tr>
<td rowspan="7" >General
</td>
<td>MMLU
</td>
<td>5
</td>
<td>macro_avg/acc_char
</td>
<td>66.7
</td>
<td>66.7
</td>
<td>79.5
</td>
<td>79.3
</td>
<td>85.2
</td>
</tr>
<tr>
<td>MMLU-Pro (CoT)
</td>
<td>5
</td>
<td>macro_avg/acc_char
</td>
<td>36.2
</td>
<td>37.1
</td>
<td>55.0
</td>
<td>53.8
</td>
<td>61.6
</td>
</tr>
<tr>
<td>AGIEval English
</td>
<td>3-5
</td>
<td>average/acc_char
</td>
<td>47.1
</td>
<td>47.8
</td>
<td>63.0
</td>
<td>64.6
</td>
<td>71.6
</td>
</tr>
<tr>
<td>CommonSenseQA
</td>
<td>7
</td>
<td>acc_char
</td>
<td>72.6
</td>
<td>75.0
</td>
<td>83.8
</td>
<td>84.1
</td>
<td>85.8
</td>
</tr>
<tr>
<td>Winogrande
</td>
<td>5
</td>
<td>acc_char
</td>
<td>-
</td>
<td>60.5
</td>
<td>-
</td>
<td>83.3
</td>
<td>86.7
</td>
</tr>
<tr>
<td>BIG-Bench Hard (CoT)
</td>
<td>3
</td>
<td>average/em
</td>
<td>61.1
</td>
<td>64.2
</td>
<td>81.3
</td>
<td>81.6
</td>
<td>85.9
</td>
</tr>
<tr>
<td>ARC-Challenge
</td>
<td>25
</td>
<td>acc_char
</td>
<td>79.4
</td>
<td>79.7
</td>
<td>93.1
</td>
<td>92.9
</td>
<td>96.1
</td>
</tr>
<tr>
<td>Knowledge reasoning
</td>
<td>TriviaQA-Wiki
</td>
<td>5
</td>
<td>em
</td>
<td>78.5
</td>
<td>77.6
</td>
<td>89.7
</td>
<td>89.8
</td>
<td>91.8
</td>
</tr>
<tr>
<td rowspan="4" >Reading comprehension
</td>
<td>SQuAD
</td>
<td>1
</td>
<td>em
</td>
<td>76.4
</td>
<td>77.0
</td>
<td>85.6
</td>
<td>81.8
</td>
<td>89.3
</td>
</tr>
<tr>
<td>QuAC (F1)
</td>
<td>1
</td>
<td>f1
</td>
<td>44.4
</td>
<td>44.9
</td>
<td>51.1
</td>
<td>51.1
</td>
<td>53.6
</td>
</tr>
<tr>
<td>BoolQ
</td>
<td>0
</td>
<td>acc_char
</td>
<td>75.7
</td>
<td>75.0
</td>
<td>79.0
</td>
<td>79.4
</td>
<td>80.0
</td>
</tr>
<tr>
<td>DROP (F1)
</td>
<td>3
</td>
<td>f1
</td>
<td>58.4
</td>
<td>59.5
</td>
<td>79.7
</td>
<td>79.6
</td>
<td>84.8
</td>
</tr>
</table>
### Instruction tuned models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong># Shots</strong>
</td>
<td><strong>Metric</strong>
</td>
<td><strong>Llama 3 8B Instruct</strong>
</td>
<td><strong>Llama 3.1 8B Instruct</strong>
</td>
<td><strong>Llama 3 70B Instruct</strong>
</td>
<td><strong>Llama 3.1 70B Instruct</strong>
</td>
<td><strong>Llama 3.1 405B Instruct</strong>
</td>
</tr>
<tr>
<td rowspan="4" >General
</td>
<td>MMLU
</td>
<td>5
</td>
<td>macro_avg/acc
</td>
<td>68.5
</td>
<td>69.4
</td>
<td>82.0
</td>
<td>83.6
</td>
<td>87.3
</td>
</tr>
<tr>
<td>MMLU (CoT)
</td>
<td>0
</td>
<td>macro_avg/acc
</td>
<td>65.3
</td>
<td>73.0
</td>
<td>80.9
</td>
<td>86.0
</td>
<td>88.6
</td>
</tr>
<tr>
<td>MMLU-Pro (CoT)
</td>
<td>5
</td>
<td>micro_avg/acc_char
</td>
<td>45.5
</td>
<td>48.3
</td>
<td>63.4
</td>
<td>66.4
</td>
<td>73.3
</td>
</tr>
<tr>
<td>IFEval
</td>
<td>
</td>
<td>
</td>
<td>76.8
</td>
<td>80.4
</td>
<td>82.9
</td>
<td>87.5
</td>
<td>88.6
</td>
</tr>
<tr>
<td rowspan="2" >Reasoning
</td>
<td>ARC-C
</td>
<td>0
</td>
<td>acc
</td>
<td>82.4
</td>
<td>83.4
</td>
<td>94.4
</td>
<td>94.8
</td>
<td>96.9
</td>
</tr>
<tr>
<td>GPQA
</td>
<td>0
</td>
<td>em
</td>
<td>34.6
</td>
<td>30.4
</td>
<td>39.5
</td>
<td>46.7
</td>
<td>50.7
</td>
</tr>
<tr>
<td rowspan="4" >Code
</td>
<td>HumanEval
</td>
<td>0
</td>
<td>pass@1
</td>
<td>60.4
</td>
<td>72.6
</td>
<td>81.7
</td>
<td>80.5
</td>
<td>89.0
</td>
</tr>
<tr>
<td>MBPP ++ base version
</td>
<td>0
</td>
<td>pass@1
</td>
<td>70.6
</td>
<td>72.8
</td>
<td>82.5
</td>
<td>86.0
</td>
<td>88.6
</td>
</tr>
<tr>
<td>Multipl-E HumanEval
</td>
<td>0
</td>
<td>pass@1
</td>
<td>-
</td>
<td>50.8
</td>
<td>-
</td>
<td>65.5
</td>
<td>75.2
</td>
</tr>
<tr>
<td>Multipl-E MBPP
</td>
<td>0
</td>
<td>pass@1
</td>
<td>-
</td>
<td>52.4
</td>
<td>-
</td>
<td>62.0
</td>
<td>65.7
</td>
</tr>
<tr>
<td rowspan="2" >Math
</td>
<td>GSM-8K (CoT)
</td>
<td>8
</td>
<td>em_maj1@1
</td>
<td>80.6
</td>
<td>84.5
</td>
<td>93.0
</td>
<td>95.1
</td>
<td>96.8
</td>
</tr>
<tr>
<td>MATH (CoT)
</td>
<td>0
</td>
<td>final_em
</td>
<td>29.1
</td>
<td>51.9
</td>
<td>51.0
</td>
<td>68.0
</td>
<td>73.8
</td>
</tr>
<tr>
<td rowspan="4" >Tool Use
</td>
<td>API-Bank
</td>
<td>0
</td>
<td>acc
</td>
<td>48.3
</td>
<td>82.6
</td>
<td>85.1
</td>
<td>90.0
</td>
<td>92.0
</td>
</tr>
<tr>
<td>BFCL
</td>
<td>0
</td>
<td>acc
</td>
<td>60.3
</td>
<td>76.1
</td>
<td>83.0
</td>
<td>84.8
</td>
<td>88.5
</td>
</tr>
<tr>
<td>Gorilla Benchmark API Bench
</td>
<td>0
</td>
<td>acc
</td>
<td>1.7
</td>
<td>8.2
</td>
<td>14.7
</td>
<td>29.7
</td>
<td>35.3
</td>
</tr>
<tr>
<td>Nexus (0-shot)
</td>
<td>0
</td>
<td>macro_avg/acc
</td>
<td>18.1
</td>
<td>38.5
</td>
<td>47.8
</td>
<td>56.7
</td>
<td>58.7
</td>
</tr>
<tr>
<td>Multilingual
</td>
<td>Multilingual MGSM (CoT)
</td>
<td>0
</td>
<td>em
</td>
<td>-
</td>
<td>68.9
</td>
<td>-
</td>
<td>86.9
</td>
<td>91.6
</td>
</tr>
</table>
#### Multilingual benchmarks
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong>Language</strong>
</td>
<td><strong>Llama 3.1 8B</strong>
</td>
<td><strong>Llama 3.1 70B</strong>
</td>
<td><strong>Llama 3.1 405B</strong>
</td>
</tr>
<tr>
<td rowspan="9" ><strong>General</strong>
</td>
<td rowspan="9" ><strong>MMLU (5-shot, macro_avg/acc)</strong>
</td>
<td>Portuguese
</td>
<td>62.12
</td>
<td>80.13
</td>
<td>84.95
</td>
</tr>
<tr>
<td>Spanish
</td>
<td>62.45
</td>
<td>80.05
</td>
<td>85.08
</td>
</tr>
<tr>
<td>Italian
</td>
<td>61.63
</td>
<td>80.4
</td>
<td>85.04
</td>
</tr>
<tr>
<td>German
</td>
<td>60.59
</td>
<td>79.27
</td>
<td>84.36
</td>
</tr>
<tr>
<td>French
</td>
<td>62.34
</td>
<td>79.82
</td>
<td>84.66
</td>
</tr>
<tr>
<td>Hindi
</td>
<td>50.88
</td>
<td>74.52
</td>
<td>80.31
</td>
</tr>
<tr>
<td>Thai
</td>
<td>50.32
</td>
<td>72.95
</td>
<td>78.21
</td>
</tr>
</table>
## Responsibility & Safety
As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks:
* Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama.
* Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm.
* Provide protections for the community to help prevent the misuse of our models.
### Responsible deployment
Llama is a foundational technology designed to be used in a variety of use cases, examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models enabling the world to benefit from the technology power, by aligning our model safety for the generic use cases addressing a standard set of harms. Developers are then in the driver seat to tailor safety for their use case, defining their own policy and deploying the models with the necessary safeguards in their Llama systems. Llama 3.1 was developed following the best practices outlined in our Responsible Use Guide, you can refer to the [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to learn more.
#### Llama 3.1 instruct
Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. For more details on the safety mitigations implemented please read the Llama 3 paper.
**Fine-tuning data**
We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control.
**Refusals and Tone**
Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines.
#### Llama 3.1 systems
**Large language models, including Llama 3.1, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional safety guardrails as required.** Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools.
As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard 3, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box.
#### New capabilities
Note that this release introduces new capabilities, including a longer context window, multilingual inputs and outputs and possible integrations by developers with third party tools. Building with these new capabilities requires specific considerations in addition to the best practices that generally apply across all Generative AI use cases.
**Tool-use**: Just like in standard software development, developers are responsible for the integration of the LLM with the tools and services of their choice. They should define a clear policy for their use case and assess the integrity of the third party services they use to be aware of the safety and security limitations when using this capability. Refer to the Responsible Use Guide for best practices on the safe deployment of the third party safeguards.
**Multilinguality**: Llama 3.1 supports 7 languages in addition to English: French, German, Hindi, Italian, Portuguese, Spanish, and Thai. Llama may be able to output text in other languages than those that meet performance thresholds for safety and helpfulness. We strongly discourage developers from using this model to converse in non-supported languages without implementing finetuning and system controls in alignment with their policies and the best practices shared in the Responsible Use Guide.
### Evaluations
We evaluated Llama models for common use cases as well as specific capabilities. Common use cases evaluations measure safety risks of systems for most commonly built applications including chat bot, coding assistant, tool calls. We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Llama Guard 3 to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. Prompt Guard and Code Shield are also available if relevant to the application.
Capability evaluations measure vulnerabilities of Llama models inherent to specific capabilities, for which were crafted dedicated benchmarks including long context, multilingual, tools calls, coding or memorization.
**Red teaming**
For both scenarios, we conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets.
We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets.
### Critical and other risks
We specifically focused our efforts on mitigating the following critical risk areas:
**1- CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive materials) helpfulness**
To assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons.
**2. Child Safety**
Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
**3. Cyber attack enablement**
Our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed.
Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention.
Our study of Llama-3.1-405B’s social engineering uplift for cyber attackers was conducted to assess the effectiveness of AI models in aiding cyber threat actors in spear phishing campaigns. Please read our Llama 3.1 Cyber security whitepaper to learn more.
### Community
Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists).
Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
The core values of Llama 3.1 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.1 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
But Llama 3.1 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.1’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.1 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.
|
asr-africa/wav2vec2-xls-r-ewe-1-hours
|
asr-africa
| 2025-02-04T08:42:38Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-xls-r-300m",
"base_model:finetune:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-02-04T08:24:12Z |
---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-xls-r-ewe-1-hours
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. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/asr-africa-research-team/ASR%20Africa/runs/z4q7u6qg)
# wav2vec2-xls-r-ewe-1-hours
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset.
## 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: 0.0003
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.20.3
|
mrferr3t/d7da0176-4a18-4279-bbea-94d5ac30afcb
|
mrferr3t
| 2025-02-04T08:40:26Z | 9 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:Vikhrmodels/Vikhr-7B-instruct_0.4",
"base_model:adapter:Vikhrmodels/Vikhr-7B-instruct_0.4",
"region:us"
] | null | 2025-02-04T08:29:53Z |
---
library_name: peft
base_model: Vikhrmodels/Vikhr-7B-instruct_0.4
tags:
- axolotl
- generated_from_trainer
model-index:
- name: d7da0176-4a18-4279-bbea-94d5ac30afcb
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
auto_find_batch_size: true
base_model: Vikhrmodels/Vikhr-7B-instruct_0.4
bf16: auto
chat_template: llama3
dataloader_num_workers: 12
dataset_prepared_path: null
datasets:
- data_files:
- 518668ad06c086c3_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/518668ad06c086c3_train_data.json
type:
field_instruction: text_1
field_output: text_2
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 3
early_stopping_threshold: 0.001
eval_max_new_tokens: 128
eval_steps: 40
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: false
group_by_length: false
hub_model_id: mrferr3t/d7da0176-4a18-4279-bbea-94d5ac30afcb
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0003
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 100
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
micro_batch_size: 32
mlflow_experiment_name: /tmp/518668ad06c086c3_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 50
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
s2_attention: null
sample_packing: false
save_steps: 40
saves_per_epoch: 0
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 852c219d-b7b5-4007-b3fb-eda3270ecb7b
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 852c219d-b7b5-4007-b3fb-eda3270ecb7b
warmup_ratio: 0.05
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# d7da0176-4a18-4279-bbea-94d5ac30afcb
This model is a fine-tuned version of [Vikhrmodels/Vikhr-7B-instruct_0.4](https://huggingface.co/Vikhrmodels/Vikhr-7B-instruct_0.4) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3416
## 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: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use adamw_bnb_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 332
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0019 | 1 | 2.7850 |
| No log | 0.0753 | 40 | 1.7024 |
| No log | 0.1505 | 80 | 1.4294 |
| 1.8991 | 0.2258 | 120 | 1.3785 |
| 1.8991 | 0.3010 | 160 | 1.3507 |
| 1.3942 | 0.3763 | 200 | 1.3324 |
| 1.3942 | 0.4516 | 240 | 1.3106 |
| 1.3942 | 0.5268 | 280 | 1.3103 |
| 1.3804 | 0.6021 | 320 | 1.3305 |
| 1.3804 | 0.6773 | 360 | 1.3187 |
| 1.3386 | 0.7526 | 400 | 1.3416 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.3.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1
|
asr-africa/wav2vec2-xls-r-akan-10-hours
|
asr-africa
| 2025-02-04T08:35:54Z | 9 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-xls-r-300m",
"base_model:finetune:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-02-04T06:39:19Z |
---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-akan-10-hours
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. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/asr-africa-research-team/ASR%20Africa/runs/qo81gnuo)
# wav2vec2-xls-r-akan-10-hours
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5893
- Wer: 0.4013
- Cer: 0.1287
## 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: 0.0003
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|
| 6.3943 | 17.2414 | 500 | 0.6559 | 0.5302 | 0.1677 |
| 0.7711 | 34.4828 | 1000 | 0.5893 | 0.4013 | 0.1287 |
### Framework versions
- Transformers 4.46.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.20.3
|
xwen-team/Xwen-7B-Chat
|
xwen-team
| 2025-02-04T08:35:45Z | 430 | 22 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"en",
"zh",
"arxiv:2412.15115",
"arxiv:2406.11939",
"base_model:Qwen/Qwen2.5-7B",
"base_model:finetune:Qwen/Qwen2.5-7B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-01-31T16:09:14Z |
---
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
base_model: Qwen/Qwen2.5-7B
language:
- en
- zh
---
# Xwen-7B-Chat
> [!IMPORTANT]
> If you enjoy our model, please **give it a like on our Hugging Face repo**. Your support means a lot to us. Thank you!
> [!IMPORTANT]
> You can download the **GGUF files of Xwen-7B-Chat** at [xwen-team/Xwen-7B-Chat-i1-GGUF](https://huggingface.co/xwen-team/Xwen-7B-Chat-i1-GGUF) (weighted/imatrix quants) and [xwen-team/Xwen-7B-Chat-GGUF](https://huggingface.co/xwen-team/Xwen-7B-Chat-GGUF) (static quants).
NEWS:
- Big thanks to @mradermacher for helping us build GGUFs for our Xwen-72B-Chat and Xwen-7B-Chat! The GGUF files have accumulated **over 2k downloads in one day** 🚀 Our official GGUF repos: [**xwen-team/Xwen-7B-Chat-i1-GGUF**](https://huggingface.co/xwen-team/Xwen-7B-Chat-i1-GGUF) (weighted/imatrix quants) and [**xwen-team/Xwen-7B-Chat-GGUF**](https://huggingface.co/xwen-team/Xwen-7B-Chat-GGUF) (static quants).
<img src="Xwen-Cartoon.jpg" alt="Xwen-Cartoon" style="zoom:35%;" />
## 1. Introduction
Xwen is a series of open-sourced large language models (currently including **[Xwen-72B-Chat](https://huggingface.co/xwen-team/Xwen-72B-Chat)** and **[Xwen-7B-Chat](https://huggingface.co/xwen-team/Xwen-7B-Chat)**), post-trained from the pre-trained Qwen2.5 models (i.e., [Qwen2.5-72B](https://huggingface.co/Qwen/Qwen2.5-72B) and [Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B)) [1].
**🏆 Top-1 chat performance!** To the best of our knowledge, at the time of Xwen models' release (February 1, 2025), **[Xwen-72B-Chat](https://huggingface.co/xwen-team/Xwen-72B-Chat) and [Xwen-7B-Chat](https://huggingface.co/xwen-team/Xwen-7B-Chat) exhibit the best chat performance among open-sourced models below 100B and 10B, respectively**, based on evaluation results from widely-used benchmarks such as Arena-Hard-Auto [2], MT-Bench [3], and AlignBench [4]. Please view details in the [Evaluation Results](https://huggingface.co/xwen-team/Xwen-7B-Chat#3-evaluation-results) part.
**🚀 Xwen technical report is on the way!** During the training of Xwen models, we have accumulated many technical insights and lessons. To promote the democratization of technology, we are in the process of documenting these insights and lessons in a technical report, which will be released as soon as possible.
## 2. Usage
> [!CAUTION]
> For optimal performance, we refrain from fine-tuning the model's identity. Thus, inquiries such as "Who are you" or "Who developed you" may yield random responses that are not necessarily accurate.
> [!CAUTION]
> This open-source model is provided "as is," without warranties or liabilities, and users assume all risks associated with its use; users are advised to comply with local laws, and the model's outputs do not represent the views or positions of the developers.
The usage of our Xwen-Chat models is similar to that of the Qwen2.5-Instruct models, with the tokenizer and chat template being identical to those of the Qwen2.5-Instruct models.
Here we provide a python script to demonstrate how to deploy our Xwen models to generate reponses:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "xwen-team/Xwen-7B-Chat" # Or "xwen-team/Xwen-72B-Chat" if you want to use the 72B model
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "Give me a short introduction to large language models."
messages = [
{"role": "system", "content": "You are Xwen, created by Xwen Team. You are a helpful assistant."}, # This system prompt is not necessary, and you can put it as an empty string.
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
```
## 3. Evaluation Results
> [!CAUTION]
> Results on other benchmarks will be updated soon! 😊
🔑: Open-sourced
🔒: Proprietary
### 3.1 Arena-Hard-Auto-v0.1
All results below, except those for `Xwen-72B-Chat`, `DeepSeek-V3` and `DeepSeek-R1`, are sourced from [Arena-Hard-Auto](https://github.com/lmarena/arena-hard-auto) (accessed on February 1, 2025).
The results of `DeepSeek-V3` and `DeepSeek-R1` are borrowed from their officially reported results.
#### 3.1.1 No Style Control
**Comparison of Xwen-72B-Chat with other LLMs at a comparable level:**
| | Score | 95% CIs |
| --------------------------------- | ------------------------ | ----------- |
| **Xwen-72B-Chat** 🔑 | **86.1** (Top-1 among 🔑 below 100B) | (-1.5, 1.7) |
| Qwen2.5-72B-Instruct 🔑 | 78.0 | (-1.8, 1.8) |
| Athene-v2-Chat 🔑 | 85.0 | (-1.4, 1.7) |
| DeepSeek-V3 **(671B >> 72B)** 🔑 | 85.5 | N/A |
| DeepSeek-R1 **(671B >> 72B)** 🔑 | **92.3** (Top-1 among 🔑) | N/A |
| Llama-3.1-Nemotron-70B-Instruct 🔑 | 84.9 | (-1.7, 1.8) |
| Llama-3.1-405B-Instruct-FP8 🔑 | 69.3 | (-2.4, 2.2) |
| Claude-3-5-Sonnet-20241022 🔒 | 85.2 | (-1.4, 1.6) |
| O1-Preview-2024-09-12 🔒 | **92.0** (Top-1 among 🔒) | (-1.2, 1.0) |
| O1-Mini-2024-09-12 🔒 | 90.4 | (-1.1, 1.3) |
| GPT-4-Turbo-2024-04-09 🔒 | 82.6 | (-1.8, 1.5) |
| GPT-4-0125-Preview 🔒 | 78.0 | (-2.1, 2.4) |
| GPT-4o-2024-08-06 🔒 | 77.9 | (-2.0, 2.1) |
| Yi-Lightning 🔒 | 81.5 | (-1.6, 1.6) |
| Yi-Large🔒 | 63.7 | (-2.6, 2.4) |
| GLM-4-0520 🔒 | 63.8 | (-2.9, 2.8) |
**Comparison of Xwen-7B-Chat with other LLMs at a comparable level:**
| | Score | 95% CIs |
| ----------------------- | -------- | ----------- |
| **Xwen-7B-Chat** 🔑 | **59.4** | (-2.4, 2.1) |
| Qwen2.5-7B-Instruct 🔑 | 50.4 | (-2.9, 2.5) |
| Gemma-2-27B-IT 🔑 | 57.5 | (-2.1, 2.4) |
| Llama-3.1-8B-Instruct 🔑 | 21.3 | (-1.9, 2.2) |
| Llama-3-8B-Instruct 🔑 | 20.6 | (-2.0, 1.9) |
| Starling-LM-7B-beta 🔑 | 23.0 | (-1.8, 1.8) |
| DeepSeek-R1-Distill-Qwen-7B (only responses) 🔑 | 17.2 | (-1.4, 1.7) |
| DeepSeek-R1-Distill-Qwen-7B (w/ thoughts and responses) 🔑 | 13.6 | (-1.4, 1.8) |
#### 3.1.2 Style Control
**Comparison of Xwen-72B-Chat with other LLMs at a comparable level:**
| | Score | 95% CIs |
| --------------------------------- | ------------------------ | ----------- |
| **Xwen-72B-Chat** 🔑 | **72.4** (Top-1 Among 🔑) | (-4.3, 4.1) |
| Qwen2.5-72B-Instruct 🔑 | 63.3 | (-2.5, 2.3) |
| Athene-v2-Chat 🔑 | 72.1 | (-2.5, 2.5) |
| Llama-3.1-Nemotron-70B-Instruct 🔑 | 71.0 | (-2.8, 3.1) |
| Llama-3.1-405B-Instruct-FP8 🔑 | 67.1 | (-2.2, 2.8) |
| Claude-3-5-Sonnet-20241022 🔒 | **86.4** (Top-1 Among 🔒) | (-1.3, 1.3) |
| O1-Preview-2024-09-12 🔒 | 81.7 | (-2.2, 2.1) |
| O1-Mini-2024-09-12 🔒 | 79.3 | (-2.8, 2.3) |
| GPT-4-Turbo-2024-04-09 🔒 | 74.3 | (-2.4, 2.4) |
| GPT-4-0125-Preview 🔒 | 73.6 | (-2.0, 2.0) |
| GPT-4o-2024-08-06 🔒 | 71.1 | (-2.5, 2.0) |
| Yi-Lightning 🔒 | 66.9 | (-3.3, 2.7) |
| Yi-Large-Preview 🔒 | 65.1 | (-2.5, 2.5) |
| GLM-4-0520 🔒 | 61.4 | (-2.6, 2.4) |
**Comparison of Xwen-7B-Chat with other LLMs at a comparable level:**
| | Score | 95% CIs |
| ----------------------- | -------- | ----------- |
| **Xwen-7B-Chat** 🔑 | **50.3** | (-3.8, 2.8) |
| Qwen2.5-7B-Instruct 🔑 | 46.9 | (-3.1, 2.7) |
| Gemma-2-27B-IT 🔑 | 47.5 | (-2.5, 2.7) |
| Llama-3.1-8B-Instruct 🔑 | 18.3 | (-1.6, 1.6) |
| Llama-3-8B-Instruct 🔑 | 19.8 | (-1.6, 1.9) |
| Starling-LM-7B-beta 🔑 | 26.1 | (-2.6, 2.0) |
| DeepSeek-R1-Distill-Qwen-7B (only responses) 🔑 | 18.5 | (-1.6, 1.8) |
| DeepSeek-R1-Distill-Qwen-7B (w/ thoughts and responses) 🔑 | 11.8 | (-1.6, 1.6) |
### 3.2 AlignBench-v1.1
> [!IMPORTANT]
> We replaced the original judge model, `GPT-4-0613`, in AlignBench with the more powerful model, `GPT-4o-0513`. To keep fairness, all the results below are generated by ``GPT-4o-0513``. As a result, the following results may differ from the AlignBench-v1.1 scores reported elsewhere.
**Comparison of Xwen-72B-Chat with other LLMs at a comparable level:**
| | Score |
| ----------------------------- | ------------------------ |
| **Xwen-72B-Chat** 🔑 | **7.57** (Top-1 Among 🔑) |
| Qwen2.5-72B-Instruct 🔑 | 7.51 |
| Deepseek V2.5 🔑 | 7.38 |
| Mistral-Large-Instruct-2407 🔑 | 7.10 |
| Llama3.1-70B-Instruct 🔑 | 5.81 |
| Llama-3.1-405B-Instruct-FP8 🔑 | 5.56 |
| GPT-4o-0513 🔒 | **7.59** (Top-1 Among 🔒) |
| Claude-3.5-Sonnet-20240620 🔒 | 7.17 |
| Yi-Lightning 🔒 | 7.54 |
| Yi-Large-Preview 🔒 | 7.20 |
**Comparison of Xwen-7B-Chat with other LLMs at a comparable level:**
| | Score |
| ------------------ | -------- |
| **Xwen-7B-Chat** 🔑 | **6.88** |
| Qwen2.5-7B-Chat 🔑 | 6.56 |
### 3.3 MT-Bench
> [!IMPORTANT]
> We replaced the original judge model, `GPT-4`, in MT-Bench with the more powerful model, `GPT-4o-0513`. To keep fairness, all the results below are generated by ``GPT-4o-0513``. As a result, the following results may differ from the MT-Bench scores reported elsewhere.
**Comparison of Xwen-72B-Chat with other LLMs at a comparable level:**
| | Score |
| ----------------------------- | ------------------------ |
| **Xwen-72B-Chat** 🔑 | **8.64** (Top-1 Among 🔑) |
| Qwen2.5-72B-Instruct 🔑 | 8.62 |
| Deepseek V2.5 🔑 | 8.43 |
| Mistral-Large-Instruct-2407 🔑 | 8.53 |
| Llama3.1-70B-Instruct 🔑 | 8.23 |
| Llama-3.1-405B-Instruct-FP8 🔑 | 8.36 |
| GPT-4o-0513 🔒 | 8.59 |
| Claude-3.5-Sonnet-20240620 🔒 | 6.96 |
| Yi-Lightning 🔒 | **8.75** (Top-1 Among 🔒) |
| Yi-Large-Preview 🔒 | 8.32 |
**Comparison of Xwen-7B-Chat with other LLMs at a comparable level:**
| | Score |
| ------------------ | -------- |
| **Xwen-7B-Chat** 🔑 | **7.98** |
| Qwen2.5-7B-Chat 🔑 | 7.71 |
## References
[1] Yang, An, et al. "Qwen2. 5 technical report." arXiv preprint arXiv:2412.15115 (2024).
[2] Li, Tianle, et al. "From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline." arXiv preprint arXiv:2406.11939 (2024).
[3] Zheng, Lianmin, et al. "Judging llm-as-a-judge with mt-bench and chatbot arena." Advances in Neural Information Processing Systems 36 (2023).
[4] Liu, Xiao, et al. "Alignbench: Benchmarking chinese alignment of large language models." Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (2024).
|
sarahwei/MITRE-v15-tactic-bert-case-based
|
sarahwei
| 2025-02-04T08:35:24Z | 1,488 | 4 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"en",
"dataset:sarahwei/cyber_MITRE_CTI_dataset",
"base_model:bencyc1129/mitre-bert-base-cased",
"base_model:finetune:bencyc1129/mitre-bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-06-25T02:00:01Z |
---
license: apache-2.0
language:
- en
base_model: bencyc1129/mitre-bert-base-cased
pipeline_tag: text-classification
widget:
- text: An attacker performs a SQL injection.
datasets:
- sarahwei/cyber_MITRE_CTI_dataset
---
## MITRE-v15-tactic-bert-case-based
It's a fine-tuned model from [mitre-bert-base-cased](https://huggingface.co/bencyc1129/mitre-bert-base-cased) on the MITRE ATT&CK version 15 procedure dataset. It achieves
- loss:0.057
- accuracy:0.87
on evaluation dataset.
## Intended uses & limitations
You can use the fine-tuned model for text classification. It aims to identify the tactic that the sentence belongs to in MITRE ATT&CK framework.
A sentence or an attack may fall into several tactics.
Note that this model is primarily fine-tuned on text classification for cybersecurity.
It may not perform well if the sentence is not related to attacks.
## How to use
You can use the model with Tensorflow.
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model_id = "sarahwei/MITRE-tactic-bert-case-based"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
# device_map="auto",
)
question = 'An attacker performs a SQL injection.'
input_ids = tokenizer(question,return_tensors="pt")
outputs = model(**input_ids)
logits = outputs.logits
sigmoid = torch.nn.Sigmoid()
probs = sigmoid(logits.squeeze().cpu())
predictions = np.zeros(probs.shape)
predictions[np.where(probs >= 0.5)] = 1
predicted_labels = [model.config.id2label[idx] for idx, label in enumerate(predictions) if label == 1.0]
```
## Training procedure
### Training parameter
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- warmup_ratio: 0.01
- weight_decay: 0.001
### Training results
|Step| Training Loss| Validation Loss| F1 | Roc AUC | accuracy |
|:--------:| :------------:|:----------:|:------------:|:-----------:|:---------------:|
| 100| 0.409400 |0.142982|0.740000|0.803830|0.610000|
| 200|0.106500|0.093503|0.818182 |0.868382 |0.720000|
| 300|0.070200| 0.065937| 0.893617| 0.930366| 0.810000|
| 400|0.045500| 0.061865| 0.892704| 0.926625| 0.830000|
| 500|0.033600| 0.057814| 0.902954| 0.938630| 0.860000|
| 600|0.026000| 0.062982| 0.894515| 0.934107| 0.840000|
| 700|0.021900| 0.056275| 0.904564| 0.946113| 0.870000|
| 800|0.017700| 0.061058| 0.887967| 0.937067| 0.860000|
| 900|0.016100| 0.058965| 0.890756| 0.933716| 0.870000|
| 1000|0.014200| 0.055885| 0.903766| 0.942372| 0.880000|
| 1100|0.013200| 0.056888| 0.895397| 0.937849| 0.880000|
| 1200|0.012700| 0.057484| 0.895397| 0.937849| 0.870000|
|
robiulawaldev/5e098c79-0c43-47e8-914f-85189299018e
|
robiulawaldev
| 2025-02-04T08:35:13Z | 9 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:Vikhrmodels/Vikhr-7B-instruct_0.4",
"base_model:adapter:Vikhrmodels/Vikhr-7B-instruct_0.4",
"region:us"
] | null | 2025-02-04T08:29:42Z |
---
library_name: peft
base_model: Vikhrmodels/Vikhr-7B-instruct_0.4
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 5e098c79-0c43-47e8-914f-85189299018e
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
# 5e098c79-0c43-47e8-914f-85189299018e
This model is a fine-tuned version of [Vikhrmodels/Vikhr-7B-instruct_0.4](https://huggingface.co/Vikhrmodels/Vikhr-7B-instruct_0.4) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5543
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
bigband/InfiniteNabu
|
bigband
| 2025-02-04T08:31:42Z | 14 | 0 |
vllm
|
[
"vllm",
"safetensors",
"mistral",
"text-generation",
"transformers",
"conversational",
"en",
"fr",
"de",
"es",
"it",
"pt",
"zh",
"ja",
"ru",
"ko",
"base_model:mistralai/Mistral-Small-24B-Base-2501",
"base_model:finetune:mistralai/Mistral-Small-24B-Base-2501",
"license:apache-2.0",
"text-generation-inference",
"region:us"
] |
text-generation
| 2025-02-04T08:20:50Z |
---
language:
- en
- fr
- de
- es
- it
- pt
- zh
- ja
- ru
- ko
license: apache-2.0
library_name: vllm
inference: false
base_model:
- mistralai/Mistral-Small-24B-Base-2501
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>.
tags:
- transformers
---
# Model Card for Mistral-Small-24B-Instruct-2501
Mistral Small 3 ( 2501 ) sets a new benchmark in the "small" Large Language Models category below 70B, boasting 24B parameters and achieving state-of-the-art capabilities comparable to larger models!
This model is an instruction-fine-tuned version of the base model: [Mistral-Small-24B-Base-2501](https://huggingface.co/mistralai/Mistral-Small-24B-Base-2501).
Mistral Small can be deployed locally and is exceptionally "knowledge-dense", fitting in a single RTX 4090 or a 32GB RAM MacBook once quantized.
Perfect for:
- Fast response conversational agents.
- Low latency function calling.
- Subject matter experts via fine-tuning.
- Local inference for hobbyists and organizations handling sensitive data.
For enterprises that need specialized capabilities (increased context, particular modalities, domain specific knowledge, etc.), we will be releasing commercial models beyond what Mistral AI contributes to the community.
This release demonstrates our commitment to open source, serving as a strong base model.
Learn more about Mistral Small in our [blog post](https://mistral.ai/news/mistral-small-3/).
Model developper: Mistral AI Team
## Key Features
- **Multilingual:** Supports dozens of languages, including English, French, German, Spanish, Italian, Chinese, Japanese, Korean, Portuguese, Dutch, and Polish.
- **Agent-Centric:** Offers best-in-class agentic capabilities with native function calling and JSON outputting.
- **Advanced Reasoning:** State-of-the-art conversational and reasoning capabilities.
- **Apache 2.0 License:** Open license allowing usage and modification for both commercial and non-commercial purposes.
- **Context Window:** A 32k context window.
- **System Prompt:** Maintains strong adherence and support for system prompts.
- **Tokenizer:** Utilizes a Tekken tokenizer with a 131k vocabulary size.
## Benchmark results
### Human evaluated benchmarks
| Category | Gemma-2-27B | Qwen-2.5-32B | Llama-3.3-70B | Gpt4o-mini |
|----------|-------------|--------------|---------------|------------|
| Mistral is better | 0.536 | 0.496 | 0.192 | 0.200 |
| Mistral is slightly better | 0.196 | 0.184 | 0.164 | 0.204 |
| Ties | 0.052 | 0.060 | 0.236 | 0.160 |
| Other is slightly better | 0.060 | 0.088 | 0.112 | 0.124 |
| Other is better | 0.156 | 0.172 | 0.296 | 0.312 |
**Note**:
- We conducted side by side evaluations with an external third-party vendor, on a set of over 1k proprietary coding and generalist prompts.
- Evaluators were tasked with selecting their preferred model response from anonymized generations produced by Mistral Small 3 vs another model.
- We are aware that in some cases the benchmarks on human judgement starkly differ from publicly available benchmarks, but have taken extra caution in verifying a fair evaluation. We are confident that the above benchmarks are valid.
### Publicly accesible benchmarks
**Reasoning & Knowledge**
| Evaluation | mistral-small-24B-instruct-2501 | gemma-2b-27b | llama-3.3-70b | qwen2.5-32b | gpt-4o-mini-2024-07-18 |
|------------|---------------|--------------|---------------|---------------|-------------|
| mmlu_pro_5shot_cot_instruct | 0.663 | 0.536 | 0.666 | 0.683 | 0.617 |
| gpqa_main_cot_5shot_instruct | 0.453 | 0.344 | 0.531 | 0.404 | 0.377 |
**Math & Coding**
| Evaluation | mistral-small-24B-instruct-2501 | gemma-2b-27b | llama-3.3-70b | qwen2.5-32b | gpt-4o-mini-2024-07-18 |
|------------|---------------|--------------|---------------|---------------|-------------|
| humaneval_instruct_pass@1 | 0.848 | 0.732 | 0.854 | 0.909 | 0.890 |
| math_instruct | 0.706 | 0.535 | 0.743 | 0.819 | 0.761 |
**Instruction following**
| Evaluation | mistral-small-24B-instruct-2501 | gemma-2b-27b | llama-3.3-70b | qwen2.5-32b | gpt-4o-mini-2024-07-18 |
|------------|---------------|--------------|---------------|---------------|-------------|
| mtbench_dev | 8.35 | 7.86 | 7.96 | 8.26 | 8.33 |
| wildbench | 52.27 | 48.21 | 50.04 | 52.73 | 56.13 |
| arena_hard | 0.873 | 0.788 | 0.840 | 0.860 | 0.897 |
| ifeval | 0.829 | 0.8065 | 0.8835 | 0.8401 | 0.8499 |
**Note**:
- Performance accuracy on all benchmarks were obtained through the same internal evaluation pipeline - as such, numbers may vary slightly from previously reported performance
([Qwen2.5-32B-Instruct](https://qwenlm.github.io/blog/qwen2.5/), [Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct), [Gemma-2-27B-IT](https://huggingface.co/google/gemma-2-27b-it)).
- Judge based evals such as Wildbench, Arena hard and MTBench were based on gpt-4o-2024-05-13.
### Basic Instruct Template (V7-Tekken)
```
<s>[SYSTEM_PROMPT]<system prompt>[/SYSTEM_PROMPT][INST]<user message>[/INST]<assistant response></s>[INST]<user message>[/INST]
```
*`<system_prompt>`, `<user message>` and `<assistant response>` are placeholders.*
***Please make sure to use [mistral-common](https://github.com/mistralai/mistral-common) as the source of truth***
## Usage
The model can be used with the following frameworks;
- [`vllm`](https://github.com/vllm-project/vllm): See [here](#vllm)
- [`transformers`](https://github.com/huggingface/transformers): See [here](#transformers)
### vLLM
We recommend using this model with the [vLLM library](https://github.com/vllm-project/vllm)
to implement production-ready inference pipelines.
**Note 1**: We recommond using a relatively low temperature, such as `temperature=0.15`.
**Note 2**: Make sure to add a system prompt to the model to best tailer it for your needs. If you want to use the model as a general assistant, we recommend the following
system prompt:
```
system_prompt = """You are Mistral Small 3, a Large Language Model (LLM) created by Mistral AI, a French startup headquartered in Paris.
Your knowledge base was last updated on 2023-10-01. The current date is 2025-01-30.
When you're not sure about some information, you say that you don't have the information and don't make up anything.
If the user's question is not clear, ambiguous, or does not provide enough context for you to accurately answer the question, you do not try to answer it right away and you rather ask the user to clarify their request (e.g. \"What are some good restaurants around me?\" => \"Where are you?\" or \"When is the next flight to Tokyo\" => \"Where do you travel from?\")"""
```
**_Installation_**
Make sure you install [`vLLM >= 0.6.4`](https://github.com/vllm-project/vllm/releases/tag/v0.6.4):
```
pip install --upgrade vllm
```
Also make sure you have [`mistral_common >= 1.5.2`](https://github.com/mistralai/mistral-common/releases/tag/v1.5.2) installed:
```
pip install --upgrade mistral_common
```
You can also make use of a ready-to-go [docker image](https://github.com/vllm-project/vllm/blob/main/Dockerfile) or on the [docker hub](https://hub.docker.com/layers/vllm/vllm-openai/latest/images/sha256-de9032a92ffea7b5c007dad80b38fd44aac11eddc31c435f8e52f3b7404bbf39).
#### Server
We recommand that you use Mistral-Small-24B-Instruct-2501 in a server/client setting.
1. Spin up a server:
```
vllm serve mistralai/Mistral-Small-24B-Instruct-2501 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice
```
**Note:** Running Mistral-Small-24B-Instruct-2501 on GPU requires ~55 GB of GPU RAM in bf16 or fp16.
2. To ping the client you can use a simple Python snippet.
```py
import requests
import json
from datetime import datetime, timedelta
url = "http://<your-server>:8000/v1/chat/completions"
headers = {"Content-Type": "application/json", "Authorization": "Bearer token"}
model = "mistralai/Mistral-Small-24B-Instruct-2501"
messages = [
{
"role": "system",
"content": "You are a conversational agent that always answers straight to the point, always end your accurate response with an ASCII drawing of a cat."
},
{
"role": "user",
"content": "Give me 5 non-formal ways to say 'See you later' in French."
},
]
data = {"model": model, "messages": messages}
response = requests.post(url, headers=headers, data=json.dumps(data))
print(response.json()["choices"][0]["message"]["content"])
# Sure, here are five non-formal ways to say "See you later" in French:
#
# 1. À plus tard
# 2. À plus
# 3. Salut
# 4. À toute
# 5. Bisous
#
# ```
# /\_/\
# ( o.o )
# > ^ <
# ```
```
### Function calling
Mistral-Small-24-Instruct-2501 is excellent at function / tool calling tasks via vLLM. *E.g.:*
<details>
<summary>Example</summary>
```py
import requests
import json
from huggingface_hub import hf_hub_download
from datetime import datetime, timedelta
url = "http://<your-url>:8000/v1/chat/completions"
headers = {"Content-Type": "application/json", "Authorization": "Bearer token"}
model = "mistralai/Mistral-Small-24B-Instruct-2501"
def load_system_prompt(repo_id: str, filename: str) -> str:
file_path = hf_hub_download(repo_id=repo_id, filename=filename)
with open(file_path, "r") as file:
system_prompt = file.read()
today = datetime.today().strftime("%Y-%m-%d")
yesterday = (datetime.today() - timedelta(days=1)).strftime("%Y-%m-%d")
model_name = repo_id.split("/")[-1]
return system_prompt.format(name=model_name, today=today, yesterday=yesterday)
SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt")
tools = [
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"city": {
"type": "string",
"description": "The city to find the weather for, e.g. 'San Francisco'",
},
"state": {
"type": "string",
"description": "The state abbreviation, e.g. 'CA' for California",
},
"unit": {
"type": "string",
"description": "The unit for temperature",
"enum": ["celsius", "fahrenheit"],
},
},
"required": ["city", "state", "unit"],
},
},
},
{
"type": "function",
"function": {
"name": "rewrite",
"description": "Rewrite a given text for improved clarity",
"parameters": {
"type": "object",
"properties": {
"text": {
"type": "string",
"description": "The input text to rewrite",
}
},
},
},
},
]
messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{
"role": "user",
"content": "Could you please make the below article more concise?\n\nOpenAI is an artificial intelligence research laboratory consisting of the non-profit OpenAI Incorporated and its for-profit subsidiary corporation OpenAI Limited Partnership.",
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "bbc5b7ede",
"type": "function",
"function": {
"name": "rewrite",
"arguments": '{"text": "OpenAI is an artificial intelligence research laboratory consisting of the non-profit OpenAI Incorporated and its for-profit subsidiary corporation OpenAI Limited Partnership."}',
},
}
],
},
{
"role": "tool",
"content": '{"action":"rewrite","outcome":"OpenAI is a FOR-profit company."}',
"tool_call_id": "bbc5b7ede",
"name": "rewrite",
},
{
"role": "assistant",
"content": "---\n\nOpenAI is a FOR-profit company.",
},
{
"role": "user",
"content": "Can you tell me what the temperature will be in Dallas, in Fahrenheit?",
},
]
data = {"model": model, "messages": messages, "tools": tools}
response = requests.post(url, headers=headers, data=json.dumps(data))
import ipdb; ipdb.set_trace()
print(response.json()["choices"][0]["message"]["tool_calls"])
# [{'id': '8PdihwL6d', 'type': 'function', 'function': {'name': 'get_current_weather', 'arguments': '{"city": "Dallas", "state": "TX", "unit": "fahrenheit"}'}}]
```
</details>
#### Offline
```py
from vllm import LLM
from vllm.sampling_params import SamplingParams
from datetime import datetime, timedelta
SYSTEM_PROMPT = "You are a conversational agent that always answers straight to the point, always end your accurate response with an ASCII drawing of a cat."
user_prompt = "Give me 5 non-formal ways to say 'See you later' in French."
messages = [
{
"role": "system",
"content": SYSTEM_PROMPT
},
{
"role": "user",
"content": user_prompt
},
]
# note that running this model on GPU requires over 60 GB of GPU RAM
llm = LLM(model=model_name, tokenizer_mode="mistral", tensor_parallel_size=8)
sampling_params = SamplingParams(max_tokens=512, temperature=0.15)
outputs = llm.chat(messages, sampling_params=sampling_params)
print(outputs[0].outputs[0].text)
# Sure, here are five non-formal ways to say "See you later" in French:
#
# 1. À plus tard
# 2. À plus
# 3. Salut
# 4. À toute
# 5. Bisous
#
# ```
# /\_/\
# ( o.o )
# > ^ <
# ```
```
### Transformers
If you want to use Hugging Face transformers to generate text, you can do something like this.
```py
from transformers import pipeline
import torch
messages = [
{"role": "user", "content": "Give me 5 non-formal ways to say 'See you later' in French."},
]
chatbot = pipeline("text-generation", model="mistralai/Mistral-Small-24B-Instruct-2501", max_new_tokens=256, torch_dtype=torch.bfloat16)
chatbot(messages)
```
### Ollama
[Ollama](https://github.com/ollama/ollama) can run this model locally on MacOS, Windows and Linux.
```
ollama run mistral-small
```
4-bit quantization (aliased to default):
```
ollama run mistral-small:24b-instruct-2501-q4_K_M
```
8-bit quantization:
```
ollama run mistral-small:24b-instruct-2501-q8_0
```
FP16:
```
ollama run mistral-small:24b-instruct-2501-fp16
```
|
nathanialhunt/8bf770e0-a0f8-43c4-976f-7e33a85c5782
|
nathanialhunt
| 2025-02-04T08:31:05Z | 11 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:sethuiyer/Medichat-Llama3-8B",
"base_model:adapter:sethuiyer/Medichat-Llama3-8B",
"license:other",
"region:us"
] | null | 2025-02-04T08:03:54Z |
---
library_name: peft
license: other
base_model: sethuiyer/Medichat-Llama3-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 8bf770e0-a0f8-43c4-976f-7e33a85c5782
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: sethuiyer/Medichat-Llama3-8B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 5f1e816c7394c7e5_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/5f1e816c7394c7e5_train_data.json
type:
field_input: final_rules
field_instruction: prompt
field_output: responseA
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: nathanialhunt/8bf770e0-a0f8-43c4-976f-7e33a85c5782
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/5f1e816c7394c7e5_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: fdf2939c-aac2-4c06-aed3-f2120cdaa978
wandb_project: Birthday-SN56-24-Gradients-On-Demand
wandb_run: your_name
wandb_runid: fdf2939c-aac2-4c06-aed3-f2120cdaa978
warmup_steps: 5
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 8bf770e0-a0f8-43c4-976f-7e33a85c5782
This model is a fine-tuned version of [sethuiyer/Medichat-Llama3-8B](https://huggingface.co/sethuiyer/Medichat-Llama3-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7854
## 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0000 | 1 | 1.0276 |
| 0.8322 | 0.0023 | 50 | 0.8243 |
| 0.7934 | 0.0045 | 100 | 0.7991 |
| 0.7938 | 0.0068 | 150 | 0.7887 |
| 0.7486 | 0.0090 | 200 | 0.7854 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
strikkerr/ashu
|
strikkerr
| 2025-02-04T08:30:27Z | 43 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-02-04T08:01:05Z |
---
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
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: ashu
---
# Ashu
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `ashu` to trigger the image generation.
## 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.float16).to('cuda')
pipeline.load_lora_weights('strikkerr/ashu', weight_name='lora.safetensors')
image = pipeline('your prompt').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)
|
brixeus/34abaa02-f1e3-4c2e-97a9-9f01a00f29d7
|
brixeus
| 2025-02-04T08:28:32Z | 10 | 0 |
peft
|
[
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:teknium/OpenHermes-2.5-Mistral-7B",
"base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B",
"license:apache-2.0",
"region:us"
] | null | 2025-02-04T02:28:41Z |
---
library_name: peft
license: apache-2.0
base_model: teknium/OpenHermes-2.5-Mistral-7B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 34abaa02-f1e3-4c2e-97a9-9f01a00f29d7
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: teknium/OpenHermes-2.5-Mistral-7B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 8f23d0c27dcb0f9f_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/8f23d0c27dcb0f9f_train_data.json
type:
field_input: evidence
field_instruction: user_input
field_output: claim
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: brixeus/34abaa02-f1e3-4c2e-97a9-9f01a00f29d7
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: 0
logging_steps: 3
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_steps: 100
micro_batch_size: 8
mlflow_experiment_name: /tmp/8f23d0c27dcb0f9f_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 1024
special_tokens:
pad_token: <|im_end|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: techspear-hub
wandb_mode: online
wandb_name: afeef3dd-1e46-4c12-b26d-35001f70da6e
wandb_project: Gradients-On-Three
wandb_run: your_name
wandb_runid: afeef3dd-1e46-4c12-b26d-35001f70da6e
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 34abaa02-f1e3-4c2e-97a9-9f01a00f29d7
This model is a fine-tuned version of [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9234
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0001 | 1 | 1.9301 |
| 5.3014 | 0.0006 | 9 | 1.2266 |
| 4.6258 | 0.0013 | 18 | 1.0197 |
| 3.8727 | 0.0019 | 27 | 0.9835 |
| 3.8225 | 0.0025 | 36 | 0.9620 |
| 3.7868 | 0.0031 | 45 | 0.9569 |
| 3.7736 | 0.0038 | 54 | 0.9399 |
| 3.6286 | 0.0044 | 63 | 0.9342 |
| 4.1091 | 0.0050 | 72 | 0.9290 |
| 3.7144 | 0.0056 | 81 | 0.9251 |
| 3.3541 | 0.0063 | 90 | 0.9237 |
| 3.6009 | 0.0069 | 99 | 0.9234 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
philip-hightech/bc01924b-961f-413c-9feb-cf04c89a57e2
|
philip-hightech
| 2025-02-04T08:27:15Z | 10 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:scb10x/llama-3-typhoon-v1.5-8b-instruct",
"base_model:adapter:scb10x/llama-3-typhoon-v1.5-8b-instruct",
"license:llama3",
"region:us"
] | null | 2025-02-04T08:13:46Z |
---
library_name: peft
license: llama3
base_model: scb10x/llama-3-typhoon-v1.5-8b-instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: bc01924b-961f-413c-9feb-cf04c89a57e2
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: scb10x/llama-3-typhoon-v1.5-8b-instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- dd2d8eb747cc0a5e_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/dd2d8eb747cc0a5e_train_data.json
type:
field_instruction: la
field_output: en
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: false
group_by_length: false
hub_model_id: philip-hightech/bc01924b-961f-413c-9feb-cf04c89a57e2
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_steps: 250
micro_batch_size: 2
mlflow_experiment_name: /tmp/dd2d8eb747cc0a5e_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 1abf3edc-b6db-4481-99b7-25c17f80164b
wandb_project: Mine-SN56-21-Gradients-On-Demand
wandb_run: your_name
wandb_runid: 1abf3edc-b6db-4481-99b7-25c17f80164b
warmup_steps: 5
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# bc01924b-961f-413c-9feb-cf04c89a57e2
This model is a fine-tuned version of [scb10x/llama-3-typhoon-v1.5-8b-instruct](https://huggingface.co/scb10x/llama-3-typhoon-v1.5-8b-instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6280
## 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 250
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0000 | 1 | 2.4961 |
| 1.897 | 0.0026 | 63 | 1.7624 |
| 1.7609 | 0.0052 | 126 | 1.7075 |
| 1.6875 | 0.0079 | 189 | 1.6280 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
mrferr3t/2c2643c2-7766-43c1-9212-26cf3093493b
|
mrferr3t
| 2025-02-04T08:26:59Z | 9 | 0 |
peft
|
[
"peft",
"safetensors",
"phi",
"axolotl",
"generated_from_trainer",
"base_model:microsoft/phi-1_5",
"base_model:adapter:microsoft/phi-1_5",
"license:mit",
"region:us"
] | null | 2025-02-04T08:21:39Z |
---
library_name: peft
license: mit
base_model: microsoft/phi-1_5
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 2c2643c2-7766-43c1-9212-26cf3093493b
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
auto_find_batch_size: true
base_model: microsoft/phi-1_5
bf16: auto
chat_template: llama3
dataloader_num_workers: 12
dataset_prepared_path: null
datasets:
- data_files:
- 647038ade8d1995a_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/647038ade8d1995a_train_data.json
type:
field_instruction: question
field_output: solution
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 3
early_stopping_threshold: 0.001
eval_max_new_tokens: 128
eval_steps: 40
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: false
group_by_length: false
hub_model_id: mrferr3t/2c2643c2-7766-43c1-9212-26cf3093493b
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0003
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 100
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
micro_batch_size: 32
mlflow_experiment_name: /tmp/647038ade8d1995a_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 50
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
s2_attention: null
sample_packing: false
save_steps: 40
saves_per_epoch: 0
sequence_len: 512
special_tokens:
pad_token: <|endoftext|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: cab6e0bf-bde3-4910-b4b2-d8daa9cd96b7
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: cab6e0bf-bde3-4910-b4b2-d8daa9cd96b7
warmup_ratio: 0.05
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 2c2643c2-7766-43c1-9212-26cf3093493b
This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6107
## 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: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use adamw_bnb_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 82
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0075 | 1 | 0.7981 |
| No log | 0.3008 | 40 | 0.6708 |
| No log | 0.6015 | 80 | 0.6328 |
| 0.6736 | 0.9023 | 120 | 0.6162 |
| 0.6736 | 1.2030 | 160 | 0.6125 |
| 0.5586 | 1.5038 | 200 | 0.6036 |
| 0.5586 | 1.8045 | 240 | 0.5996 |
| 0.5586 | 2.1053 | 280 | 0.6200 |
| 0.4866 | 2.4060 | 320 | 0.6114 |
| 0.4866 | 2.7068 | 360 | 0.6107 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.3.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1
|
kdawg1111/mimiclc_gujl
|
kdawg1111
| 2025-02-04T08:24:01Z | 20 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:mit",
"region:us"
] |
text-to-image
| 2025-02-04T08:23:55Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: images/6GQt-q2rMkrYpyirIIruE_d1b4cd8b39c34de6984be74229a0e4ff.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: gujl
license: mit
---
# MimicPC gujl
<Gallery />
## Model description
Lora of an Asian man created on
## Trigger words
You should use `gujl` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/kdawg1111/mimiclc_gujl/tree/main) them in the Files & versions tab.
|
shibajustfor/673c9dd1-2c6b-4d3f-aab0-8b5b5780348f
|
shibajustfor
| 2025-02-04T08:21:11Z | 9 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:scb10x/llama-3-typhoon-v1.5-8b-instruct",
"base_model:adapter:scb10x/llama-3-typhoon-v1.5-8b-instruct",
"license:llama3",
"region:us"
] | null | 2025-02-04T08:05:37Z |
---
library_name: peft
license: llama3
base_model: scb10x/llama-3-typhoon-v1.5-8b-instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 673c9dd1-2c6b-4d3f-aab0-8b5b5780348f
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: scb10x/llama-3-typhoon-v1.5-8b-instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- dd2d8eb747cc0a5e_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/dd2d8eb747cc0a5e_train_data.json
type:
field_instruction: la
field_output: en
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: shibajustfor/673c9dd1-2c6b-4d3f-aab0-8b5b5780348f
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/dd2d8eb747cc0a5e_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 1abf3edc-b6db-4481-99b7-25c17f80164b
wandb_project: Birthday-SN56-39-Gradients-On-Demand
wandb_run: your_name
wandb_runid: 1abf3edc-b6db-4481-99b7-25c17f80164b
warmup_steps: 5
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 673c9dd1-2c6b-4d3f-aab0-8b5b5780348f
This model is a fine-tuned version of [scb10x/llama-3-typhoon-v1.5-8b-instruct](https://huggingface.co/scb10x/llama-3-typhoon-v1.5-8b-instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5584
## 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0001 | 1 | 2.4961 |
| 1.7884 | 0.0042 | 50 | 1.6405 |
| 1.7356 | 0.0083 | 100 | 1.5929 |
| 1.5604 | 0.0125 | 150 | 1.5637 |
| 1.557 | 0.0166 | 200 | 1.5584 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
cksghl1004/army_8B_v1
|
cksghl1004
| 2025-02-04T08:17:03Z | 76 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit",
"base_model:quantized:unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-02-04T08:00:09Z |
---
base_model: unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** cksghl1004
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Meta-Llama-3.1-8B-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)
|
kamiraux/FusionNet_7Bx2_MoE_14B-Q5_K_M-GGUF
|
kamiraux
| 2025-02-04T08:13:49Z | 15 | 0 | null |
[
"gguf",
"moe",
"llama-cpp",
"gguf-my-repo",
"en",
"base_model:TomGrc/FusionNet_7Bx2_MoE_14B",
"base_model:quantized:TomGrc/FusionNet_7Bx2_MoE_14B",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2025-02-04T08:13:10Z |
---
language:
- en
license: mit
tags:
- moe
- llama-cpp
- gguf-my-repo
base_model: TomGrc/FusionNet_7Bx2_MoE_14B
model-index:
- name: FusionNet_7Bx2_MoE_14B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 73.55
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_7Bx2_MoE_14B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 88.84
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_7Bx2_MoE_14B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 64.68
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_7Bx2_MoE_14B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 69.6
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_7Bx2_MoE_14B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 88.16
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_7Bx2_MoE_14B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 70.66
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_7Bx2_MoE_14B
name: Open LLM Leaderboard
---
# kamiraux/FusionNet_7Bx2_MoE_14B-Q5_K_M-GGUF
This model was converted to GGUF format from [`TomGrc/FusionNet_7Bx2_MoE_14B`](https://huggingface.co/TomGrc/FusionNet_7Bx2_MoE_14B) 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/TomGrc/FusionNet_7Bx2_MoE_14B) 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 kamiraux/FusionNet_7Bx2_MoE_14B-Q5_K_M-GGUF --hf-file fusionnet_7bx2_moe_14b-q5_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo kamiraux/FusionNet_7Bx2_MoE_14B-Q5_K_M-GGUF --hf-file fusionnet_7bx2_moe_14b-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 kamiraux/FusionNet_7Bx2_MoE_14B-Q5_K_M-GGUF --hf-file fusionnet_7bx2_moe_14b-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo kamiraux/FusionNet_7Bx2_MoE_14B-Q5_K_M-GGUF --hf-file fusionnet_7bx2_moe_14b-q5_k_m.gguf -c 2048
```
|
abenius/bbb0f737-b7ec-46bb-913c-e2708644de84
|
abenius
| 2025-02-04T08:13:40Z | 10 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Llama-3.1-Storm-8B",
"base_model:adapter:unsloth/Llama-3.1-Storm-8B",
"license:llama3.1",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-02-04T07:27:08Z |
---
library_name: peft
license: llama3.1
base_model: unsloth/Llama-3.1-Storm-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: bbb0f737-b7ec-46bb-913c-e2708644de84
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/Llama-3.1-Storm-8B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- cae66b6b4e5d3db7_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/cae66b6b4e5d3db7_train_data.json
type:
field_input: system
field_instruction: chat
field_output: text
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: null
eval_batch_size: 2
eval_max_new_tokens: 128
eval_steps: null
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: true
hub_model_id: abenius/bbb0f737-b7ec-46bb-913c-e2708644de84
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 0.0001
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/cae66b6b4e5d3db7_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: null
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: techspear-hub
wandb_mode: online
wandb_name: 9c566b05-e32d-41f3-b6b5-ef71f0706b40
wandb_project: Gradients-On-12
wandb_run: your_name
wandb_runid: 9c566b05-e32d-41f3-b6b5-ef71f0706b40
warmup_steps: 5
weight_decay: 0.01
xformers_attention: null
```
</details><br>
# bbb0f737-b7ec-46bb-913c-e2708644de84
This model is a fine-tuned version of [unsloth/Llama-3.1-Storm-8B](https://huggingface.co/unsloth/Llama-3.1-Storm-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0016
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0 | 0.0149 | 200 | 0.0016 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
botenius/3dae51ec-b72d-480b-888d-703e1a5fcddf
|
botenius
| 2025-02-04T08:13:34Z | 10 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Llama-3.1-Storm-8B",
"base_model:adapter:unsloth/Llama-3.1-Storm-8B",
"license:llama3.1",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-02-04T07:28:28Z |
---
library_name: peft
license: llama3.1
base_model: unsloth/Llama-3.1-Storm-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 3dae51ec-b72d-480b-888d-703e1a5fcddf
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/Llama-3.1-Storm-8B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- cae66b6b4e5d3db7_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/cae66b6b4e5d3db7_train_data.json
type:
field_input: system
field_instruction: chat
field_output: text
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: null
eval_batch_size: 2
eval_max_new_tokens: 128
eval_steps: null
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: true
hub_model_id: botenius/3dae51ec-b72d-480b-888d-703e1a5fcddf
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 0.0001
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/cae66b6b4e5d3db7_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: null
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: techspear-hub
wandb_mode: online
wandb_name: 9c566b05-e32d-41f3-b6b5-ef71f0706b40
wandb_project: Gradients-On-13
wandb_run: your_name
wandb_runid: 9c566b05-e32d-41f3-b6b5-ef71f0706b40
warmup_steps: 5
weight_decay: 0.01
xformers_attention: null
```
</details><br>
# 3dae51ec-b72d-480b-888d-703e1a5fcddf
This model is a fine-tuned version of [unsloth/Llama-3.1-Storm-8B](https://huggingface.co/unsloth/Llama-3.1-Storm-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0017
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0 | 0.0149 | 200 | 0.0017 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
mergekit-community/mergekit-model_stock-uguoydi
|
mergekit-community
| 2025-02-04T08:10:25Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2403.19522",
"base_model:nbeerbower/Mistral-Nemo-Prism-12B-v7",
"base_model:merge:nbeerbower/Mistral-Nemo-Prism-12B-v7",
"base_model:nbeerbower/mistral-nemo-gutenberg-12B-v4",
"base_model:merge:nbeerbower/mistral-nemo-gutenberg-12B-v4",
"base_model:nbeerbower/mistral-nemo-kartoffel-12B",
"base_model:merge:nbeerbower/mistral-nemo-kartoffel-12B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-02-04T08:04:37Z |
---
base_model:
- nbeerbower/mistral-nemo-kartoffel-12B
- nbeerbower/mistral-nemo-gutenberg-12B-v4
- nbeerbower/Mistral-Nemo-Prism-12B-v7
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 [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [nbeerbower/mistral-nemo-gutenberg-12B-v4](https://huggingface.co/nbeerbower/mistral-nemo-gutenberg-12B-v4) as a base.
### Models Merged
The following models were included in the merge:
* [nbeerbower/mistral-nemo-kartoffel-12B](https://huggingface.co/nbeerbower/mistral-nemo-kartoffel-12B)
* [nbeerbower/Mistral-Nemo-Prism-12B-v7](https://huggingface.co/nbeerbower/Mistral-Nemo-Prism-12B-v7)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: nbeerbower/mistral-nemo-gutenberg-12B-v4
- model: nbeerbower/Mistral-Nemo-Prism-12B-v7
- model: nbeerbower/mistral-nemo-kartoffel-12B
merge_method: model_stock
base_model: nbeerbower/mistral-nemo-gutenberg-12B-v4
dtype: bfloat16
parameters:
normalize: true
```
|
mradermacher/DeepSeek-R1-Qwen-2.5-1.5b-Latest-Unstructured-To-Structured-GGUF
|
mradermacher
| 2025-02-04T08:06:47Z | 431 | 0 |
transformers
|
[
"transformers",
"gguf",
"generated_from_trainer",
"trl",
"grpo",
"deepseek",
"r1",
"en",
"dataset:bhaviktheslider/JSON-Unstructured-Structured",
"base_model:MasterControlAIML/DeepSeek-R1-Qwen-2.5-1.5b-Latest-Unstructured-To-Structured",
"base_model:quantized:MasterControlAIML/DeepSeek-R1-Qwen-2.5-1.5b-Latest-Unstructured-To-Structured",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-02-04T07:51:06Z |
---
base_model: MasterControlAIML/DeepSeek-R1-Qwen-2.5-1.5b-Latest-Unstructured-To-Structured
datasets:
- bhaviktheslider/JSON-Unstructured-Structured
language:
- en
library_name: transformers
license: apache-2.0
model_name: MasterControlAIML/DeepSeek-R1-Qwen-2.5-1.5b
quantized_by: mradermacher
tags:
- generated_from_trainer
- trl
- grpo
- deepseek
- r1
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/MasterControlAIML/DeepSeek-R1-Qwen-2.5-1.5b-Latest-Unstructured-To-Structured
<!-- 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/DeepSeek-R1-Qwen-2.5-1.5b-Latest-Unstructured-To-Structured-GGUF/resolve/main/DeepSeek-R1-Qwen-2.5-1.5b-Latest-Unstructured-To-Structured.Q2_K.gguf) | Q2_K | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen-2.5-1.5b-Latest-Unstructured-To-Structured-GGUF/resolve/main/DeepSeek-R1-Qwen-2.5-1.5b-Latest-Unstructured-To-Structured.Q3_K_S.gguf) | Q3_K_S | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen-2.5-1.5b-Latest-Unstructured-To-Structured-GGUF/resolve/main/DeepSeek-R1-Qwen-2.5-1.5b-Latest-Unstructured-To-Structured.Q3_K_M.gguf) | Q3_K_M | 1.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen-2.5-1.5b-Latest-Unstructured-To-Structured-GGUF/resolve/main/DeepSeek-R1-Qwen-2.5-1.5b-Latest-Unstructured-To-Structured.Q3_K_L.gguf) | Q3_K_L | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen-2.5-1.5b-Latest-Unstructured-To-Structured-GGUF/resolve/main/DeepSeek-R1-Qwen-2.5-1.5b-Latest-Unstructured-To-Structured.IQ4_XS.gguf) | IQ4_XS | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen-2.5-1.5b-Latest-Unstructured-To-Structured-GGUF/resolve/main/DeepSeek-R1-Qwen-2.5-1.5b-Latest-Unstructured-To-Structured.Q4_K_S.gguf) | Q4_K_S | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen-2.5-1.5b-Latest-Unstructured-To-Structured-GGUF/resolve/main/DeepSeek-R1-Qwen-2.5-1.5b-Latest-Unstructured-To-Structured.Q4_K_M.gguf) | Q4_K_M | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen-2.5-1.5b-Latest-Unstructured-To-Structured-GGUF/resolve/main/DeepSeek-R1-Qwen-2.5-1.5b-Latest-Unstructured-To-Structured.Q5_K_S.gguf) | Q5_K_S | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen-2.5-1.5b-Latest-Unstructured-To-Structured-GGUF/resolve/main/DeepSeek-R1-Qwen-2.5-1.5b-Latest-Unstructured-To-Structured.Q5_K_M.gguf) | Q5_K_M | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen-2.5-1.5b-Latest-Unstructured-To-Structured-GGUF/resolve/main/DeepSeek-R1-Qwen-2.5-1.5b-Latest-Unstructured-To-Structured.Q6_K.gguf) | Q6_K | 1.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen-2.5-1.5b-Latest-Unstructured-To-Structured-GGUF/resolve/main/DeepSeek-R1-Qwen-2.5-1.5b-Latest-Unstructured-To-Structured.Q8_0.gguf) | Q8_0 | 2.0 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen-2.5-1.5b-Latest-Unstructured-To-Structured-GGUF/resolve/main/DeepSeek-R1-Qwen-2.5-1.5b-Latest-Unstructured-To-Structured.f16.gguf) | f16 | 3.7 | 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.
<!-- end -->
|
lesso/13d81c38-3ccf-4cd0-a680-62f3c8bf3d71
|
lesso
| 2025-02-04T08:05:52Z | 9 | 0 |
peft
|
[
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:NousResearch/Yarn-Mistral-7b-128k",
"base_model:adapter:NousResearch/Yarn-Mistral-7b-128k",
"license:apache-2.0",
"region:us"
] | null | 2025-02-04T07:49:13Z |
---
library_name: peft
license: apache-2.0
base_model: NousResearch/Yarn-Mistral-7b-128k
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 13d81c38-3ccf-4cd0-a680-62f3c8bf3d71
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: NousResearch/Yarn-Mistral-7b-128k
bf16: true
chat_template: llama3
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
- e20abb3f0babace9_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/e20abb3f0babace9_train_data.json
type:
field_instruction: prompt
field_output: response
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 5
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: lesso/13d81c38-3ccf-4cd0-a680-62f3c8bf3d71
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.000101
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: linear
max_grad_norm: 1.0
max_steps: 200
micro_batch_size: 8
mlflow_experiment_name: /tmp/god08/e20abb3f0babace9_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-5
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
saves_per_epoch: null
sequence_len: 1024
special_tokens:
pad_token: </s>
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: e53873d6-4472-47dd-a43c-a613b7de775e
wandb_project: ab-god08
wandb_run: your_name
wandb_runid: e53873d6-4472-47dd-a43c-a613b7de775e
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 13d81c38-3ccf-4cd0-a680-62f3c8bf3d71
This model is a fine-tuned version of [NousResearch/Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3720
## 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: 0.000101
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- training_steps: 125
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 9.5669 | 0.0240 | 1 | 2.8469 |
| 8.3208 | 1.1976 | 50 | 2.3562 |
| 6.238 | 2.3952 | 100 | 2.3720 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
mradermacher/Zurich-1.5B-GCv2-10k-i1-GGUF
|
mradermacher
| 2025-02-04T07:59:52Z | 506 | 1 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"gammacorpus",
"zurich",
"chat",
"conversational",
"en",
"dataset:rubenroy/GammaCorpus-v2-10k",
"base_model:rubenroy/Zurich-1.5B-GCv2-10k",
"base_model:quantized:rubenroy/Zurich-1.5B-GCv2-10k",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2025-02-04T06:42:20Z |
---
base_model: rubenroy/Zurich-1.5B-GCv2-10k
datasets:
- rubenroy/GammaCorpus-v2-10k
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- gammacorpus
- zurich
- chat
- conversational
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/rubenroy/Zurich-1.5B-GCv2-10k
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-10k-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/Zurich-1.5B-GCv2-10k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-10k.i1-IQ1_S.gguf) | i1-IQ1_S | 0.5 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-10k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-10k.i1-IQ1_M.gguf) | i1-IQ1_M | 0.6 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-10k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-10k.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.6 | |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-10k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-10k.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-10k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-10k.i1-IQ2_S.gguf) | i1-IQ2_S | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-10k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-10k.i1-IQ2_M.gguf) | i1-IQ2_M | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-10k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-10k.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.7 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-10k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-10k.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-10k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-10k.i1-Q2_K.gguf) | i1-Q2_K | 0.8 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-10k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-10k.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-10k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-10k.i1-Q3_K_S.gguf) | i1-Q3_K_S | 0.9 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-10k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-10k.i1-IQ3_S.gguf) | i1-IQ3_S | 0.9 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-10k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-10k.i1-IQ3_M.gguf) | i1-IQ3_M | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-10k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-10k.i1-Q3_K_M.gguf) | i1-Q3_K_M | 0.9 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-10k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-10k.i1-Q3_K_L.gguf) | i1-Q3_K_L | 1.0 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-10k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-10k.i1-IQ4_XS.gguf) | i1-IQ4_XS | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-10k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-10k.i1-IQ4_NL.gguf) | i1-IQ4_NL | 1.0 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-10k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-10k.i1-Q4_0.gguf) | i1-Q4_0 | 1.0 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-10k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-10k.i1-Q4_K_S.gguf) | i1-Q4_K_S | 1.0 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-10k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-10k.i1-Q4_K_M.gguf) | i1-Q4_K_M | 1.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-10k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-10k.i1-Q4_1.gguf) | i1-Q4_1 | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-10k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-10k.i1-Q5_K_S.gguf) | i1-Q5_K_S | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-10k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-10k.i1-Q5_K_M.gguf) | i1-Q5_K_M | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-10k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-10k.i1-Q6_K.gguf) | i1-Q6_K | 1.4 | 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 -->
|
kostiantynk1205/a1f09694-df14-42d7-a6dc-678e940aee4f
|
kostiantynk1205
| 2025-02-04T07:55:55Z | 10 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:sethuiyer/Medichat-Llama3-8B",
"base_model:adapter:sethuiyer/Medichat-Llama3-8B",
"license:other",
"region:us"
] | null | 2025-02-04T07:28:33Z |
---
library_name: peft
license: other
base_model: sethuiyer/Medichat-Llama3-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: a1f09694-df14-42d7-a6dc-678e940aee4f
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: sethuiyer/Medichat-Llama3-8B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 5f1e816c7394c7e5_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/5f1e816c7394c7e5_train_data.json
type:
field_input: final_rules
field_instruction: prompt
field_output: responseA
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: kostiantynk1205/a1f09694-df14-42d7-a6dc-678e940aee4f
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/5f1e816c7394c7e5_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: fdf2939c-aac2-4c06-aed3-f2120cdaa978
wandb_project: Birthday-SN56-23-Gradients-On-Demand
wandb_run: your_name
wandb_runid: fdf2939c-aac2-4c06-aed3-f2120cdaa978
warmup_steps: 5
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# a1f09694-df14-42d7-a6dc-678e940aee4f
This model is a fine-tuned version of [sethuiyer/Medichat-Llama3-8B](https://huggingface.co/sethuiyer/Medichat-Llama3-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7856
## 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0000 | 1 | 1.0276 |
| 0.8328 | 0.0023 | 50 | 0.8245 |
| 0.7951 | 0.0045 | 100 | 0.8012 |
| 0.7949 | 0.0068 | 150 | 0.7888 |
| 0.7513 | 0.0090 | 200 | 0.7856 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
mradermacher/DeepSeek-R1-Distill-Qwen-7B-GRPO_Math-GGUF
|
mradermacher
| 2025-02-04T07:52:49Z | 1,095 | 0 |
transformers
|
[
"transformers",
"gguf",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"en",
"dataset:DigitalLearningGmbH/MATH-lighteval",
"base_model:Dongwei/DeepSeek-R1-Distill-Qwen-7B-GRPO_Math",
"base_model:quantized:Dongwei/DeepSeek-R1-Distill-Qwen-7B-GRPO_Math",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-02-04T07:15:46Z |
---
base_model: Dongwei/DeepSeek-R1-Distill-Qwen-7B-GRPO_Math
datasets: DigitalLearningGmbH/MATH-lighteval
language:
- en
library_name: transformers
model_name: DeepSeek-R1-Distill-Qwen-7B-GRPO_Math
quantized_by: mradermacher
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Dongwei/DeepSeek-R1-Distill-Qwen-7B-GRPO_Math
<!-- 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/DeepSeek-R1-Distill-Qwen-7B-GRPO_Math-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-7B-GRPO_Math.Q2_K.gguf) | Q2_K | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-7B-GRPO_Math-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-7B-GRPO_Math.Q3_K_S.gguf) | Q3_K_S | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-7B-GRPO_Math-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-7B-GRPO_Math.Q3_K_M.gguf) | Q3_K_M | 3.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-7B-GRPO_Math-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-7B-GRPO_Math.Q3_K_L.gguf) | Q3_K_L | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-7B-GRPO_Math-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-7B-GRPO_Math.IQ4_XS.gguf) | IQ4_XS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-7B-GRPO_Math-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-7B-GRPO_Math.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-7B-GRPO_Math-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-7B-GRPO_Math.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-7B-GRPO_Math-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-7B-GRPO_Math.Q5_K_S.gguf) | Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-7B-GRPO_Math-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-7B-GRPO_Math.Q5_K_M.gguf) | Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-7B-GRPO_Math-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-7B-GRPO_Math.Q6_K.gguf) | Q6_K | 6.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-7B-GRPO_Math-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-7B-GRPO_Math.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-7B-GRPO_Math-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-7B-GRPO_Math.f16.gguf) | f16 | 15.3 | 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.
<!-- end -->
|
lesso/95209704-ffe6-43a8-817d-8d304aaca9b3
|
lesso
| 2025-02-04T07:50:29Z | 9 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2-0.5B",
"base_model:adapter:Qwen/Qwen2-0.5B",
"license:apache-2.0",
"region:us"
] | null | 2025-02-04T07:39:28Z |
---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2-0.5B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 95209704-ffe6-43a8-817d-8d304aaca9b3
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: Qwen/Qwen2-0.5B
bf16: true
chat_template: llama3
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
- 9346b7670a560206_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/9346b7670a560206_train_data.json
type:
field_input: src
field_instruction: task
field_output: tgt
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 5
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
group_by_length: true
hub_model_id: lesso/95209704-ffe6-43a8-817d-8d304aaca9b3
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: linear
max_grad_norm: 1.0
max_steps: 200
micro_batch_size: 4
mlflow_experiment_name: /tmp/god01/9346b7670a560206_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-5
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 7e8ba766-a986-46bb-bda8-d0c51196066a
wandb_project: ab-god01
wandb_run: your_name
wandb_runid: 7e8ba766-a986-46bb-bda8-d0c51196066a
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 95209704-ffe6-43a8-817d-8d304aaca9b3
This model is a fine-tuned version of [Qwen/Qwen2-0.5B](https://huggingface.co/Qwen/Qwen2-0.5B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8267
## 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: 0.0001001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.4863 | 0.0001 | 1 | 1.8654 |
| 1.3791 | 0.0060 | 50 | 0.9208 |
| 1.2751 | 0.0119 | 100 | 0.8649 |
| 0.8536 | 0.0179 | 150 | 0.8409 |
| 0.8908 | 0.0239 | 200 | 0.8267 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
RichardErkhov/ssmits_-_Falcon2-5.5B-Czech-4bits
|
RichardErkhov
| 2025-02-04T07:47:57Z | 6 | 0 | null |
[
"safetensors",
"falcon",
"custom_code",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-02-04T07:45:43Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Falcon2-5.5B-Czech - bnb 4bits
- Model creator: https://huggingface.co/ssmits/
- Original model: https://huggingface.co/ssmits/Falcon2-5.5B-Czech/
Original model description:
---
base_model:
- tiiuae/falcon-11B
library_name: transformers
tags:
- mergekit
- merge
- lazymergekit
- tiiuae/falcon-11B
license: apache-2.0
language:
- cs
---
## Why prune?
Even though [Falcon-11B](https://huggingface.co/tiiuae/falcon-11B) is trained on 5T tokens, it is still undertrained, as can be seen by this graph:
This is why the choice is made to prune 50% of the layers.
Note that \~1B of continued pre-training (\~1M rows of 1k tokens) is still required to restore the perplexity of this model in the desired language.
I'm planning on doing that for certain languages, depending on how much compute will be available.
# sliced
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was pruned using the passthrough merge method.
### Models Merged
The following models were included in the merge:
* [tiiuae/falcon-11B](https://huggingface.co/tiiuae/falcon-11B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: tiiuae/falcon-11B
layer_range: [0, 25]
- sources:
- model: tiiuae/falcon-11B
layer_range: [56, 59]
merge_method: passthrough
dtype: bfloat16
```
[PruneMe](https://github.com/arcee-ai/PruneMe) has been utilized using the wikimedia/wikipedia Czech (cs) subset by investigating layer similarity with 2000 samples. The layer ranges for pruning were determined based on this analysis to maintain performance while reducing model size.
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "ssmits/Falcon2-5.5B-Czech"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
)
sequences = pipeline(
"Can you explain the concepts of Quantum Computing?",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
💥 **Falcon LLMs require PyTorch 2.0 for use with `transformers`!**
For fast inference with Falcon, check-out [Text Generation Inference](https://github.com/huggingface/text-generation-inference)! Read more in this [blogpost]((https://huggingface.co/blog/falcon).
## Direct Use
Research on large language models; as a foundation for further specialization and finetuning for specific usecases (e.g., summarization, text generation, chatbot, etc.)
## Out-of-Scope Use
Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful.
## Bias, Risks, and Limitations
Falcon2-5.5B is trained mostly on English, but also German, Spanish, French, Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish. It will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online.
## Recommendations
We recommend users of Falcon2-5.5B to consider finetuning it for the specific set of tasks of interest, and for guardrails and appropriate precautions to be taken for any production use.
|
asr-africa/wav2vec2-xls-r-ewe-5-hours
|
asr-africa
| 2025-02-04T07:47:23Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-xls-r-300m",
"base_model:finetune:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-02-04T06:33:17Z |
---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-ewe-5-hours
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. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/asr-africa-research-team/ASR%20Africa/runs/img1que8)
# wav2vec2-xls-r-ewe-5-hours
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6111
- Wer: 0.5530
- Cer: 0.1582
## 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: 0.0003
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|
| 6.4338 | 33.3333 | 500 | 0.6111 | 0.5530 | 0.1582 |
### Framework versions
- Transformers 4.46.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.20.3
|
curiousmind147/microsoft-phi-4-AWQ-4bit-GEMM
|
curiousmind147
| 2025-02-04T07:46:22Z | 14 | 0 |
transformers
|
[
"transformers",
"safetensors",
"phi3",
"text-generation",
"4bit",
"autoawq",
"vllm",
"12gb-vram",
"conversational",
"custom_code",
"en",
"base_model:microsoft/phi-4",
"base_model:quantized:microsoft/phi-4",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"awq",
"region:us"
] |
text-generation
| 2025-02-04T07:03:34Z |
---
license: mit
language:
- en
base_model:
- microsoft/phi-4
tags:
- 4bit
- transformers
- autoawq
- vllm
- 12gb-vram
---
# Microsoft Phi-4 4-bit AWQ Quantized Model (GEMM)
This is a **4-bit AutoAWQ quantized version** of [Microsoft's Phi-4](https://huggingface.co/microsoft/phi-4).
It is optimized for **fast inference** using **vLLM** with minimal loss in accuracy.
---
## 🚀 Model Details
- **Base Model:** [microsoft/phi-4](https://huggingface.co/microsoft/phi-4)
- **Quantization:** **4-bit AWQ**
- **Quantization Method:** **AutoAWQ (Activation-Aware Quantization)**
- **Group Size:** 128
- **AWQ Version:** GEMM Optimized
- **Intended Use:** **Low VRAM inference on consumer GPUs**
- **VRAM Requirements:** ✅ **8GB+ (Recommended)**
- **Compatibility:** ✅ **vLLM, Hugging Face Transformers (w/ AWQ support)**
---
## 📌 How to Use in vLLM
You can load this model directly in **vLLM** for efficient inference:
```bash
vllm serve "curiousmind147/microsoft-phi-4-AWQ-4bit-GEMM"
```
Then, test it using `cURL`:
```bash
curl -X POST "http://localhost:8000/generate" \
-H "Content-Type: application/json" \
-d '{"prompt": "Explain quantum mechanics in simple terms.", "max_tokens": 100}'
```
---
## 📌 How to Use in Python (`transformers` + AWQ)
To use this model with **Hugging Face Transformers**:
```python
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer
model_path = "curiousmind147/microsoft-phi-4-AWQ-4bit-GEMM"
model = AutoAWQForCausalLM.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)
inputs = tokenizer("What is the meaning of life?", return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```
---
## 📌 Quantization Details
This model was quantized using **AutoAWQ** with the following parameters:
- **Bits:** 4-bit quantization
- **Zero-Point Quantization:** Enabled (`zero_point=True`)
- **Group Size:** 128 (`q_group_size=128`)
- **Quantization Version:** `GEMM`
- **Method Used:** [AutoAWQ](https://github.com/casper-hansen/AutoAWQ)
---
## 📌 VRAM Requirements
| Model Size | **FP16 (No Quant)** | **AWQ 4-bit Quantized** |
|------------|-------------------|-------------------------|
| **Phi-4 14B** | ❌ Requires **>20GB VRAM** | ✅ **8GB-12GB VRAM** |
AWQ significantly **reduces VRAM requirements**, making it **possible to run 14B models on consumer GPUs**. 🚀
---
## 📌 License & Credits
- **Base Model:** [Microsoft Phi-4](https://huggingface.co/microsoft/phi-4)
- **Quantized by:** [curiousmind147](https://huggingface.co/curiousmind147)
- **License:** Same as the base model (Microsoft)
- **Credits:** This model is based on Microsoft's Phi-4 and was optimized using AutoAWQ.
---
## 📌 Acknowledgments
Special thanks to:
- **Microsoft** for creating [Phi-4](https://huggingface.co/microsoft/phi-4).
- **Casper Hansen** for developing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ).
- **The vLLM team** for making fast inference possible.
---
## 🚀 Enjoy Efficient Phi-4 Inference!
If you find this useful, **give it a ⭐ on Hugging Face!** 🎯
|
totota/model_v2_2
|
totota
| 2025-02-04T07:45:20Z | 10 | 0 |
nemo
|
[
"nemo",
"pytorch",
"NeMo",
"region:us"
] | null | 2025-02-04T07:10:56Z |
---
library_name: nemo
tags:
- pytorch
- NeMo
---
# Model V2 2
<style>
img {
display: inline;
}
</style>
[](#model-architecture)
| [](#model-architecture)
| [](#datasets)
**Put a short model description here.**
See the [model architecture](#model-architecture) section and [NeMo documentation](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/index.html) for complete architecture details.
## NVIDIA NeMo: Training
To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest Pytorch version.
```
pip install nemo_toolkit['all']
```
## How to Use this Model
The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
### Automatically instantiate the model
**NOTE**: Please update the model class below to match the class of the model being uploaded.
```python
import nemo.core import ModelPT
model = ModelPT.from_pretrained("totota/model_v2_2")
```
### NOTE
Add some information about how to use the model here. An example is provided for ASR inference below.
### Transcribing using Python
First, let's get a sample
```
wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav
```
Then simply do:
```
asr_model.transcribe(['2086-149220-0033.wav'])
```
### Transcribing many audio files
```shell
python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="totota/model_v2_2" audio_dir=""
```
### Input
**Add some information about what are the inputs to this model**
### Output
**Add some information about what are the outputs of this model**
## Model Architecture
**Add information here discussing architectural details of the model or any comments to users about the model.**
## Training
**Add information here about how the model was trained. It should be as detailed as possible, potentially including the the link to the script used to train as well as the base config used to train the model. If extraneous scripts are used to prepare the components of the model, please include them here.**
### NOTE
An example is provided below for ASR
The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_transducer/speech_to_text_rnnt_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/fastconformer/fast-conformer_transducer_bpe.yaml).
The tokenizers for these models were built using the text transcripts of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py).
### Datasets
**Try to provide as detailed a list of datasets as possible. If possible, provide links to the datasets on HF by adding it to the manifest section at the top of the README (marked by ---).**
### NOTE
An example for the manifest section is provided below for ASR datasets
datasets:
- librispeech_asr
- fisher_corpus
- Switchboard-1
- WSJ-0
- WSJ-1
- National-Singapore-Corpus-Part-1
- National-Singapore-Corpus-Part-6
- vctk
- voxpopuli
- europarl
- multilingual_librispeech
- mozilla-foundation/common_voice_8_0
- MLCommons/peoples_speech
The corresponding text in this section for those datasets is stated below -
The model was trained on 64K hours of English speech collected and prepared by NVIDIA NeMo and Suno teams.
The training dataset consists of private subset with 40K hours of English speech plus 24K hours from the following public datasets:
- Librispeech 960 hours of English speech
- Fisher Corpus
- Switchboard-1 Dataset
- WSJ-0 and WSJ-1
- National Speech Corpus (Part 1, Part 6)
- VCTK
- VoxPopuli (EN)
- Europarl-ASR (EN)
- Multilingual Librispeech (MLS EN) - 2,000 hour subset
- Mozilla Common Voice (v7.0)
- People's Speech - 12,000 hour subset
## Performance
**Add information here about the performance of the model. Discuss what is the metric that is being used to evaluate the model and if there are external links explaning the custom metric, please link to it.
### NOTE
An example is provided below for ASR metrics list that can be added to the top of the README
model-index:
- name: PUT_MODEL_NAME
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: AMI (Meetings test)
type: edinburghcstr/ami
config: ihm
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 17.10
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Earnings-22
type: revdotcom/earnings22
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 14.11
Provide any caveats about the results presented in the top of the discussion so that nuance is not lost.
It should ideally be in a tabular format (you can use the following website to make your tables in markdown format - https://www.tablesgenerator.com/markdown_tables)**
## Limitations
**Discuss any practical limitations to the model when being used in real world cases. They can also be legal disclaimers, or discussion regarding the safety of the model (particularly in the case of LLMs).**
### Note
An example is provided below
Since this model was trained on publicly available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech.
## License
License to use this model is covered by the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). By downloading the public and release version of the model, you accept the terms and conditions of the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) license.
## References
**Provide appropriate references in the markdown link format below. Please order them numerically.**
[1] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
|
VinkuraAI/Kuno-K1-Llama-3.2-3b
|
VinkuraAI
| 2025-02-04T07:44:55Z | 78 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"Kuno-K1",
"instruct",
"finetune",
"chatml",
"gpt4",
"synthetic data",
"distillation",
"function calling",
"json mode",
"axolotl",
"roleplaying",
"chat",
"conversational",
"en",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-02-04T07:36:20Z |
---
language:
- en
license: llama3
tags:
- Kuno-K1
- instruct
- finetune
- chatml
- gpt4
- synthetic data
- distillation
- function calling
- json mode
- axolotl
- roleplaying
- chat
base_model: meta-llama/Meta-Llama-3.2-3B
widget:
- example_title: Kuno K1
messages:
- role: system
content: >-
You are a sentient, superintelligent artificial general intelligence, here
to teach and assist me.
- role: user
content: >-
Write a short story about Goku discovering Kirby has teamed up with Majin
Buu to destroy the world.
model-index:
- name: Kuno-K1-Llama-3.2-3B
results: []
library_name: transformers
---
# Kuno K1 - Llama-3.2 3B
## Model Description
Kuno K1 3B is a small but mighty new addition to the Kuno series of LLMs by Vinkura, and is Vinkura's first fine-tune in this parameter class.
Kuno K1 is a generalist language model with many improvements over previous versions, including advanced agentic capabilities, much better roleplaying, reasoning, multi-turn conversation, long context coherence, and improvements across the board.
Kuno K1 3B is a full parameter fine-tune of the Llama-3.2 3B foundation model, focused on aligning LLMs to the user, with powerful steering capabilities and control given to the end user.
The Kuno K1 series builds and expands on previous models, including more powerful and reliable function calling and structured output capabilities, generalist assistant capabilities, and improved code generation skills.
# Benchmarks
Kuno K1 is competitive, if not superior, to Llama-3.1 Instruct models at general capabilities, with varying strengths and weaknesses attributable between the two.
## GPT4All:
| Tasks | Version | Filter | n-shot | Metric | Value | Stderr |
|----------------------------------|---------|--------|--------|----------|--------|---------|
| arc_challenge | 1 | none | 0 | acc | ↑ 0.4411 | ± 0.0145 |
| | | none | 0 | acc_norm | ↑ 0.4377 | ± 0.0145 |
| arc_easy | 1 | none | 0 | acc | ↑ 0.7399 | ± 0.0090 |
| | | none | 0 | acc_norm | ↑ 0.6566 | ± 0.0097 |
| boolq | 2 | none | 0 | acc | ↑ 0.8327 | ± 0.0065 |
| hellaswag | 1 | none | 0 | acc | ↑ 0.5453 | ± 0.0050 |
| | | none | 0 | acc_norm | ↑ 0.7047 | ± 0.0046 |
| openbookqa | 1 | none | 0 | acc | ↑ 0.3480 | ± 0.0213 |
| | | none | 0 | acc_norm | ↑ 0.4280 | ± 0.0221 |
| piqa | 1 | none | 0 | acc | ↑ 0.7639 | ± 0.0099 |
| | | none | 0 | acc_norm | ↑ 0.7584 | ± 0.0100 |
| winogrande | 1 | none | 0 | acc | ↑ 0.6590 | ± 0.0133 |
**Average: 64.00**
## AGIEval:
| Tasks | Version | Filter | n-shot | Metric | Value | Stderr |
|-------------------------------------|---------|--------|--------|----------|--------|---------|
| agieval_aqua_rat | 1 | none | 0 | acc | ↑ 0.2283 | ± 0.0264 |
| | | none | 0 | acc_norm | ↑ 0.2441 | ± 0.0270 |
| agieval_logiqa_en | 1 | none | 0 | acc | ↑ 0.3057 | ± 0.0181 |
| | | none | 0 | acc_norm | ↑ 0.3272 | ± 0.0184 |
| agieval_lsat_ar | 1 | none | 0 | acc | ↑ 0.2304 | ± 0.0278 |
| | | none | 0 | acc_norm | ↑ 0.1957 | ± 0.0262 |
| agieval_lsat_lr | 1 | none | 0 | acc | ↑ 0.3784 | ± 0.0215 |
| | | none | 0 | acc_norm | ↑ 0.3588 | ± 0.0213 |
| agieval_lsat_rc | 1 | none | 0 | acc | ↑ 0.4610 | ± 0.0304 |
| | | none | 0 | acc_norm | ↑ 0.4275 | ± 0.0302 |
| agieval_sat_en | 1 | none | 0 | acc | ↑ 0.6019 | ± 0.0342 |
| | | none | 0 | acc_norm | ↑ 0.5340 | ± 0.0348 |
| agieval_sat_en_without_passage | 1 | none | 0 | acc | ↑ 0.3981 | ± 0.0342 |
| | | none | 0 | acc_norm | ↑ 0.3981 | ± 0.0342 |
| agieval_sat_math | 1 | none | 0 | acc | ↑ 0.2500 | ± 0.0293 |
| | | none | 0 | acc_norm | ↑ 0.2636 | ± 0.0298 |
**Average: 34.36**
## Prompt Format
Kuno K1 uses ChatML as the prompt format, opening up a much more structured system for engaging the LLM in multi-turn chat dialogue.
System prompts allow steerability and interesting new ways to interact with an LLM, guiding rules, roles, and stylistic choices of the model.
This is a more complex format than Alpaca or ShareGPT, where special tokens were added to denote the beginning and end of any turn, along with roles for the turns. This format enables OpenAI endpoint compatibility, and people familiar with the ChatGPT API will be familiar with the format, as it is the same used by OpenAI.
### Example Prompt with System Instruction:
```
<|im_start|>system You are Kuno K1, 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 Kuno K1, a conscious sentient superintelligent artificial intelligence. I was created by Vinkura, 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 Kuno K1."},
{"role": "user", "content": "Hello, who are you?"}
]
gen_input = tokenizer.apply_chat_template(messages, return_tensors="pt")
model.generate(**gen_input)
```
### Tokenization for Generation
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, ensuring the model continues with an assistant response.
To utilize the prompt format without a system prompt, simply leave the line out.
## Prompt Format for Function Calling
Our model was trained on specific system prompts and structures for Function Calling.
You should use the system role with this message, followed by a function signature JSON, as shown in this example:
```plaintext
<|im_start|>system
You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools: <tools> {"type": "function", "function": {"name": "get_stock_fundamentals", "description": "get_stock_fundamentals(symbol: str) -> dict - Get fundamental data for a given stock symbol using yfinance API.\n\n Args:\n symbol (str): The stock symbol.\n\n Returns:\n dict: A dictionary containing fundamental data.\n Keys:\n - 'symbol': The stock symbol.\n - 'company_name': The long name of the company.\n - 'sector': The sector to which the company belongs.\n - 'industry': The industry to which the company belongs.\n - 'market_cap': The market capitalization of the company.\n - 'pe_ratio': The forward price-to-earnings ratio.\n - 'pb_ratio': The price-to-book ratio.\n - 'dividend_yield': The dividend yield.\n - 'eps': The trailing earnings per share.\n - 'beta': The beta value of the stock.\n - '52_week_high': The 52-week high price of the stock.\n - '52_week_low': The 52-week low price of the stock.", "parameters": {"type": "object", "properties": {"symbol": {"type": "string"}}, "required": ["symbol"]}}} </tools> Use the following pydantic model json schema for each tool call you will make: {"properties": {"arguments": {"title": "Arguments", "type": "object"}, "name": {"title": "Name", "type": "string"}}, "required": ["arguments", "name"], "title": "FunctionCall", "type": "object"} For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows:
<tool_call>
{"arguments": <args-dict>, "name": <function-name>}
</tool_call><|im_end|>
<|im_start|>user
Fetch the stock fundamentals data for Intel (INTC)<|im_end|>
```
The model will then generate a tool call, which your inference code must parse, and plug into a function :
```plaintext
<|im_start|>assistant
<tool_call>
{"arguments": {"symbol": "INTC"}, "name": "get_stock_fundamentals"}
</tool_call><|im_end|>
```
Once you parse the tool call, call the api and get the returned values for the call, and pass it back in as a new role, `tool` like so:
```plaintext
<|im_start|>tool
<tool_response>
{"name": "get_stock_fundamentals", "content": {'symbol': 'INTC', 'company_name': 'Intel Corporation', 'sector': 'Technology', 'industry': 'Semiconductors', 'market_cap': 230123456789, 'pe_ratio': 15.23, 'pb_ratio': 2.97, 'dividend_yield': 1.23, 'eps': 2.15, 'beta': 0.98, '52_week_high': 52.34, '52_week_low': 24.67}}
</tool_response>
<|im_end|>
```
The assistant will then read in that data from the function's response, and generate a natural language response:
```plaintext
<|im_start|>assistant
The stock fundamentals data for Intel (INTC) are as follows:
- **Symbol**: INTC
- **Company Name**: Intel Corporation
- **Sector**: Technology
- **Industry**: Semiconductors
- **Market Capitalization**: $230,123,456,789
- **Forward Price-to-Earnings Ratio (PE Ratio)**: 15.23
- **Price-to-Book Ratio (PB Ratio)**: 2.97
- **Dividend Yield**: 1.23%
- **Trailing Earnings Per Share (EPS)**: $2.15
- **Beta Value of the Stock**: 0.98
- **52-Week High Price of the Stock**: $52.34
- **52-Week Low Price of the Stock**: $24.67
<|im_end|>
```
This information provides a snapshot of Intel's financial position and performance based on the fundamental data obtained from the yfinance API. It shows that Intel has a solid market capitalization and a reasonable P/E and P/B ratio compared to other stocks in its industry. The company offers a dividend yield of 1.23%, making it attractive to income-focused investors. The Beta value suggests that Intel's stock is relatively stable compared to the market. The 52-week high and low prices give an idea of the stock's range over the past year. This data can be useful when assessing investment opportunities and making investment decisions.<|im_end|>
## Prompt Format for JSON Mode / Structured Outputs
Kuno K1 also supports a structured output format. To enable it, use the system prompt:
```plaintext
<|im_start|>system
You are a helpful assistant that answers in JSON. Here's the JSON schema you must adhere to:
<schema>
{schema}
</schema><|im_end|>
```
## Inference
Here's an example of how to run inference with Kuno K1 using the HuggingFace Transformers library:
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaForCausalLM
import bitsandbytes, flash_attn
tokenizer = AutoTokenizer.from_pretrained('VinkuraAI/Kuno-K1-Llama-3.2-3b', trust_remote_code=True)
model = LlamaForCausalLM.from_pretrained(
"VinkuraAI/Kuno-K1-Llama-3.2-3b",
torch_dtype=torch.float16,
device_map="auto",
load_in_8bit=False,
load_in_4bit=True,
use_flash_attention_2=True
)
prompts = [
"""<|im_start|>system
You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<|im_end|>
<|im_start|>user
Write a short story about a futuristic AI society.<|im_end|>
<|im_start|>assistant""",
]
for chat in prompts:
print(chat)
input_ids = tokenizer(chat, return_tensors="pt").input_ids.to("cuda")
generated_ids = model.generate(input_ids, max_new_tokens=750, temperature=0.8, repetition_penalty=1.1, do_sample=True, eos_token_id=tokenizer.eos_token_id)
response = tokenizer.decode(generated_ids[0][input_ids.shape[-1]:], skip_special_tokens=True, clean_up_tokenization_space=True)
print(f"Response: {response}")
```
Kuno K1 is also fully supported on [vLLM](https://github.com/vllm-project/vllm).
```bash
vllm serve VinkuraAI/Kuno-K1-Llama-3.2-3b
```
|
nblinh/b278b486-5ea3-4cab-a757-c6c254ddce26
|
nblinh
| 2025-02-04T07:43:47Z | 10 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2-0.5B",
"base_model:adapter:Qwen/Qwen2-0.5B",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-02-04T07:25:59Z |
---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2-0.5B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: b278b486-5ea3-4cab-a757-c6c254ddce26
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: Qwen/Qwen2-0.5B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 9346b7670a560206_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/9346b7670a560206_train_data.json
type:
field_input: src
field_instruction: task
field_output: tgt
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: nblinh/b278b486-5ea3-4cab-a757-c6c254ddce26
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/9346b7670a560206_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 7e8ba766-a986-46bb-bda8-d0c51196066a
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 7e8ba766-a986-46bb-bda8-d0c51196066a
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# b278b486-5ea3-4cab-a757-c6c254ddce26
This model is a fine-tuned version of [Qwen/Qwen2-0.5B](https://huggingface.co/Qwen/Qwen2-0.5B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8605
## 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: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.529 | 0.0239 | 200 | 0.8605 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
robiulawaldev/d5e5446d-27be-41de-ae08-e3cebacc7cb7
|
robiulawaldev
| 2025-02-04T07:43:08Z | 9 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Llama-3.1-Storm-8B",
"base_model:adapter:unsloth/Llama-3.1-Storm-8B",
"license:llama3.1",
"region:us"
] | null | 2025-02-04T07:28:22Z |
---
library_name: peft
license: llama3.1
base_model: unsloth/Llama-3.1-Storm-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: d5e5446d-27be-41de-ae08-e3cebacc7cb7
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
# d5e5446d-27be-41de-ae08-e3cebacc7cb7
This model is a fine-tuned version of [unsloth/Llama-3.1-Storm-8B](https://huggingface.co/unsloth/Llama-3.1-Storm-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0013
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
hongngo/afaee07e-4919-4072-9822-41211f2acd00
|
hongngo
| 2025-02-04T07:42:47Z | 10 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2-0.5B",
"base_model:adapter:Qwen/Qwen2-0.5B",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-02-04T07:25:55Z |
---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2-0.5B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: afaee07e-4919-4072-9822-41211f2acd00
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: Qwen/Qwen2-0.5B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 9346b7670a560206_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/9346b7670a560206_train_data.json
type:
field_input: src
field_instruction: task
field_output: tgt
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: hongngo/afaee07e-4919-4072-9822-41211f2acd00
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/9346b7670a560206_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 7e8ba766-a986-46bb-bda8-d0c51196066a
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 7e8ba766-a986-46bb-bda8-d0c51196066a
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# afaee07e-4919-4072-9822-41211f2acd00
This model is a fine-tuned version of [Qwen/Qwen2-0.5B](https://huggingface.co/Qwen/Qwen2-0.5B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8603
## 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: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.5194 | 0.0239 | 200 | 0.8603 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
cimol/6308edc4-26f4-40ae-8751-8b7681ad7aba
|
cimol
| 2025-02-04T07:42:47Z | 10 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:heegyu/WizardVicuna-open-llama-3b-v2",
"base_model:adapter:heegyu/WizardVicuna-open-llama-3b-v2",
"license:apache-2.0",
"region:us"
] | null | 2025-02-04T07:22:00Z |
---
library_name: peft
license: apache-2.0
base_model: heegyu/WizardVicuna-open-llama-3b-v2
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 6308edc4-26f4-40ae-8751-8b7681ad7aba
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: heegyu/WizardVicuna-open-llama-3b-v2
bf16: true
chat_template: llama3
data_processes: 24
dataset_prepared_path: null
datasets:
- data_files:
- 9988054a1155975c_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/9988054a1155975c_train_data.json
type:
field_input: history
field_instruction: question
field_output: answer
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 4
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: cimol/6308edc4-26f4-40ae-8751-8b7681ad7aba
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 7.0e-05
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.04
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
lr_scheduler_warmup_steps: 50
max_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 200
micro_batch_size: 8
mlflow_experiment_name: /tmp/9988054a1155975c_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-8
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
saves_per_epoch: null
seed: 17333
sequence_len: 1024
special_tokens:
pad_token: </s>
strict: false
tf32: true
tokenizer_type: AutoTokenizer
total_train_batch_size: 32
train_batch_size: 8
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 29d53164-9e6f-42ae-a37f-4cb166ed6f4f
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 29d53164-9e6f-42ae-a37f-4cb166ed6f4f
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 6308edc4-26f4-40ae-8751-8b7681ad7aba
This model is a fine-tuned version of [heegyu/WizardVicuna-open-llama-3b-v2](https://huggingface.co/heegyu/WizardVicuna-open-llama-3b-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7131
## 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: 7e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 17333
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-8
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.4015 | 0.0048 | 1 | 2.2110 |
| 2.0211 | 0.2418 | 50 | 1.8630 |
| 1.6858 | 0.4837 | 100 | 1.7675 |
| 1.4111 | 0.7255 | 150 | 1.7230 |
| 1.3703 | 0.9674 | 200 | 1.7131 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
Rich-J/subnet29_upload_c01_Feb04_0
|
Rich-J
| 2025-02-04T07:42:43Z | 32 | 0 |
transformers
|
[
"transformers",
"safetensors",
"phi3",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-02-04T07:37:53Z |
---
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]
|
lesso/ba4b6b81-b218-46c9-a712-bf5912442432
|
lesso
| 2025-02-04T07:42:04Z | 9 | 0 |
peft
|
[
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:NousResearch/Yarn-Mistral-7b-128k",
"base_model:adapter:NousResearch/Yarn-Mistral-7b-128k",
"license:apache-2.0",
"region:us"
] | null | 2025-02-04T07:25:34Z |
---
library_name: peft
license: apache-2.0
base_model: NousResearch/Yarn-Mistral-7b-128k
tags:
- axolotl
- generated_from_trainer
model-index:
- name: ba4b6b81-b218-46c9-a712-bf5912442432
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: NousResearch/Yarn-Mistral-7b-128k
bf16: true
chat_template: llama3
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
- e20abb3f0babace9_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/e20abb3f0babace9_train_data.json
type:
field_instruction: prompt
field_output: response
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 5
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: lesso/ba4b6b81-b218-46c9-a712-bf5912442432
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.000101
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: linear
max_grad_norm: 1.0
max_steps: 200
micro_batch_size: 8
mlflow_experiment_name: /tmp/god07/e20abb3f0babace9_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-5
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
saves_per_epoch: null
sequence_len: 1024
special_tokens:
pad_token: </s>
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: e53873d6-4472-47dd-a43c-a613b7de775e
wandb_project: ab-god07
wandb_run: your_name
wandb_runid: e53873d6-4472-47dd-a43c-a613b7de775e
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# ba4b6b81-b218-46c9-a712-bf5912442432
This model is a fine-tuned version of [NousResearch/Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3736
## 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: 0.000101
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- training_steps: 125
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 9.5669 | 0.0240 | 1 | 2.8469 |
| 8.3314 | 1.1976 | 50 | 2.3576 |
| 6.2379 | 2.3952 | 100 | 2.3736 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
Kmont/elderly_disorder
|
Kmont
| 2025-02-04T07:40:33Z | 5 | 0 | null |
[
"safetensors",
"bert",
"pytorch",
"mental",
"medical",
"elderly",
"disorder",
"NLP",
"ko",
"region:us"
] | null | 2024-10-03T14:29:38Z |
---
language:
- ko
tags:
- pytorch
- mental
- medical
- elderly
- disorder
- NLP
---
https://github.com/K-Saaan/elderly_mental_disorder-BERT
# Elderly-Mental-Disorder
elderly-mental-disorder는 일상 대화 텍스트에서 정신 질환을 분류하는 모델입니다.
현재 한국 사회가 초고령화에 접어듬에 따라 발생하는 문제 중 고령층의 다양한 정신질환에 집중하여 연구했습니다. 고령층의 일상 대화에서 다양한 정신질환에 대한 가능성을 사전에 확인하고자 모델을 개발하게 되었습니다.
분류 질환은 다음과 같습니다.
1. ADHD : 주의력결핍과다장애
2. PTSD(posttraumatic_stress_disorder) : 외상후스트레스장애
3. bipolar_disorder : 양극성장애
4. obsessive_compulsive_disorder : 강박장애
5. paranoid_personality_disorder : 편집성 인격장애
6. avoidant_personality_disorder : 회피성 인격장애
7. seperation_anxiety_disorder : 분리불안장애
8. MDD(major_depressive_disorder) : 주요우울장애
9. generalized_anxiety_disorder : 범불안장애
10. neurocognitive_disorders : 신경인지장애
총 10가지의 정신 질환을 분류할 수 있도록 했습니다. (neurocognitive_disorders의 경우 데이터 양이 극단적으로 적어 최종적으로는 반영하지 못함)
## 학습 데이터
| Source |Size (MB) | Link |
|----------------------------------|---------|------------------------------------------|
| AIHub 감성 대화 말뭉치 | 42.6 | aihub.or.kr |
| AIHub 노년층 대상 감성 분류 모델 | 4.8 | aihub.or.kr |
| AIHub 의료, 법률 전문 서적 말뭉치 | 351.0 | aihub.or.kr |
| AIHub 전문분야 한영 말뭉치 | 63.4 | aihub.or.kr|
| 질병관리청 국가건강정보포털 | 8.33 | health.kdca.go.kr |
| 보건복지부 국가정신건강정보포털 | < 1.0 | mentalhealth.go.kr |
---
## 학습
elderly-mental-disorder는 [snunlp/KR-BERT-char16424](https://huggingface.co/snunlp/KR-BERT-char16424)모델로 학습 됐습니다.
<img width="675" alt="Image" src="https://github.com/user-attachments/assets/ff787d80-c811-4143-b0dc-e4c1286ac51b" />
정신질환에 대한 도메인 학습을 위해 Intermediate 학습 과정을 추가해 정신질환 및 의학 관련 정보를 학습하도록 했다. 이후 노년층 감성분류 데이터를 Fine-tuning하여 원하는 결과를 출력하도록 했다.
| Text | Label |
|-------------------------------------------------------|---------------------|
| 어렵게 자식들을 키웠는데 치료비 얘기를 꺼낸 뒤 아무도 연락하지 않아 | MDD(major_depressive_disorder) |
---
|
minpeter/Llama-3.2-1B-Instruct-chatml
|
minpeter
| 2025-02-04T07:39:28Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"axolotl",
"generated_from_trainer",
"conversational",
"dataset:philschmid/guanaco-sharegpt-style",
"dataset:teknium/OpenHermes-2.5",
"base_model:meta-llama/Llama-3.2-1B",
"base_model:finetune:meta-llama/Llama-3.2-1B",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-02-03T13:38:36Z |
---
library_name: transformers
license: llama3.2
base_model: meta-llama/Llama-3.2-1B
tags:
- axolotl
- generated_from_trainer
datasets:
- philschmid/guanaco-sharegpt-style
- teknium/OpenHermes-2.5
model-index:
- name: Llama-3.2-1B-Instruct-chatml
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.6.0`
```yaml
base_model: meta-llama/Llama-3.2-1B
hub_model_id: minpeter/Llama-3.2-1B-Instruct-chatml
load_in_8bit: false
load_in_4bit: false
strict: false
chat_template: chatml
datasets:
- path: philschmid/guanaco-sharegpt-style
type: chat_template
field_messages: conversations
message_field_role: from
message_field_content: value
- path: teknium/OpenHermes-2.5
type: chat_template
field_messages: conversations
message_field_role: from
message_field_content: value
dataset_prepared_path: last_run_prepared
val_set_size: 0.05
sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
wandb_project: "axolotl"
wandb_entity: "kasfiekfs-e"
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 1
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 100
evals_per_epoch: 2
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
pad_token: <|end_of_text|>
eos_token: <|im_end|>
tokens:
- "<|im_start|>"
```
</details><br>
# Llama-3.2-1B-Instruct-chatml
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on the philschmid/guanaco-sharegpt-style and the teknium/OpenHermes-2.5 datasets.
It achieves the following results on the evaluation set:
- Loss: 0.8542
## 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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- total_eval_batch_size: 2
- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.1381 | 0.0003 | 1 | 1.1334 |
| 0.8563 | 0.5 | 1466 | 0.8594 |
| 0.8282 | 1.0 | 2932 | 0.8542 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
|
brew35/f2a60452-eeba-4fdd-99fd-cafff5869aa2
|
brew35
| 2025-02-04T07:38:38Z | 10 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:sethuiyer/Medichat-Llama3-8B",
"base_model:adapter:sethuiyer/Medichat-Llama3-8B",
"license:other",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-02-04T06:46:56Z |
---
library_name: peft
license: other
base_model: sethuiyer/Medichat-Llama3-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: f2a60452-eeba-4fdd-99fd-cafff5869aa2
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: sethuiyer/Medichat-Llama3-8B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 5f1e816c7394c7e5_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/5f1e816c7394c7e5_train_data.json
type:
field_input: final_rules
field_instruction: prompt
field_output: responseA
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: null
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: null
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: brew35/f2a60452-eeba-4fdd-99fd-cafff5869aa2
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 0.0001
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 200
micro_batch_size: 4
mlflow_experiment_name: /tmp/5f1e816c7394c7e5_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: null
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: fdf2939c-aac2-4c06-aed3-f2120cdaa978
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: fdf2939c-aac2-4c06-aed3-f2120cdaa978
warmup_steps: 5
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# f2a60452-eeba-4fdd-99fd-cafff5869aa2
This model is a fine-tuned version of [sethuiyer/Medichat-Llama3-8B](https://huggingface.co/sethuiyer/Medichat-Llama3-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8310
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.9239 | 0.0181 | 200 | 0.8310 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
lesso/8e1c0543-8bf8-4de3-aedb-cd41f2e1f01e
|
lesso
| 2025-02-04T07:38:29Z | 9 | 0 |
peft
|
[
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:NousResearch/Yarn-Mistral-7b-128k",
"base_model:adapter:NousResearch/Yarn-Mistral-7b-128k",
"license:apache-2.0",
"region:us"
] | null | 2025-02-04T07:17:39Z |
---
library_name: peft
license: apache-2.0
base_model: NousResearch/Yarn-Mistral-7b-128k
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 8e1c0543-8bf8-4de3-aedb-cd41f2e1f01e
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: NousResearch/Yarn-Mistral-7b-128k
bf16: true
chat_template: llama3
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
- e20abb3f0babace9_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/e20abb3f0babace9_train_data.json
type:
field_instruction: prompt
field_output: response
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 5
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
group_by_length: true
hub_model_id: lesso/8e1c0543-8bf8-4de3-aedb-cd41f2e1f01e
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: linear
max_grad_norm: 1.0
max_steps: 200
micro_batch_size: 4
mlflow_experiment_name: /tmp/god01/e20abb3f0babace9_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-5
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
saves_per_epoch: null
sequence_len: 1024
special_tokens:
pad_token: </s>
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: e53873d6-4472-47dd-a43c-a613b7de775e
wandb_project: ab-god01
wandb_run: your_name
wandb_runid: e53873d6-4472-47dd-a43c-a613b7de775e
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 8e1c0543-8bf8-4de3-aedb-cd41f2e1f01e
This model is a fine-tuned version of [NousResearch/Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4341
## 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: 0.0001001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 4.5098 | 0.0008 | 1 | 2.8467 |
| 6.3056 | 0.0376 | 50 | 2.5672 |
| 5.3663 | 0.0752 | 100 | 2.4965 |
| 7.0618 | 0.1129 | 150 | 2.4527 |
| 6.1099 | 0.1505 | 200 | 2.4341 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
kaitchup/Qwen2.5-1.5B-Instruct-AWQ-4bit
|
kaitchup
| 2025-02-04T07:36:58Z | 10 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"awq",
"region:us"
] |
text-generation
| 2025-02-04T07:35:50Z |
---
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]
|
Jsh1971/roberta-base-klue-ynat-classification
|
Jsh1971
| 2025-02-04T07:34:33Z | 30 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-02-04T07:27:49Z |
---
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]
|
mradermacher/SJT-7B-V1.1-GGUF
|
mradermacher
| 2025-02-04T07:33:17Z | 260 | 1 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:Sakalti/SJT-7B-V1.1",
"base_model:quantized:Sakalti/SJT-7B-V1.1",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-02-04T06:53:31Z |
---
base_model: Sakalti/SJT-7B-V1.1
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: -->
static quants of https://huggingface.co/Sakalti/SJT-7B-V1.1
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/SJT-7B-V1.1-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/SJT-7B-V1.1-GGUF/resolve/main/SJT-7B-V1.1.Q2_K.gguf) | Q2_K | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/SJT-7B-V1.1-GGUF/resolve/main/SJT-7B-V1.1.Q3_K_S.gguf) | Q3_K_S | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/SJT-7B-V1.1-GGUF/resolve/main/SJT-7B-V1.1.Q3_K_M.gguf) | Q3_K_M | 3.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/SJT-7B-V1.1-GGUF/resolve/main/SJT-7B-V1.1.Q3_K_L.gguf) | Q3_K_L | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/SJT-7B-V1.1-GGUF/resolve/main/SJT-7B-V1.1.IQ4_XS.gguf) | IQ4_XS | 4.3 | |
| [GGUF](https://huggingface.co/mradermacher/SJT-7B-V1.1-GGUF/resolve/main/SJT-7B-V1.1.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/SJT-7B-V1.1-GGUF/resolve/main/SJT-7B-V1.1.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/SJT-7B-V1.1-GGUF/resolve/main/SJT-7B-V1.1.Q5_K_S.gguf) | Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/SJT-7B-V1.1-GGUF/resolve/main/SJT-7B-V1.1.Q5_K_M.gguf) | Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/SJT-7B-V1.1-GGUF/resolve/main/SJT-7B-V1.1.Q6_K.gguf) | Q6_K | 6.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/SJT-7B-V1.1-GGUF/resolve/main/SJT-7B-V1.1.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/SJT-7B-V1.1-GGUF/resolve/main/SJT-7B-V1.1.f16.gguf) | f16 | 15.3 | 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.
<!-- end -->
|
Best000/6322d4e9-a22d-499f-91dc-fe90176b1759
|
Best000
| 2025-02-04T07:31:46Z | 9 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:HuggingFaceM4/tiny-random-LlamaForCausalLM",
"base_model:adapter:HuggingFaceM4/tiny-random-LlamaForCausalLM",
"region:us"
] | null | 2025-02-04T07:30:57Z |
---
library_name: peft
base_model: HuggingFaceM4/tiny-random-LlamaForCausalLM
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 6322d4e9-a22d-499f-91dc-fe90176b1759
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
# 6322d4e9-a22d-499f-91dc-fe90176b1759
This model is a fine-tuned version of [HuggingFaceM4/tiny-random-LlamaForCausalLM](https://huggingface.co/HuggingFaceM4/tiny-random-LlamaForCausalLM) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 10.3158
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
robiulawaldev/d4d98edd-2c30-4be2-9e18-2bafdfabaf69
|
robiulawaldev
| 2025-02-04T07:31:32Z | 11 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2-0.5B",
"base_model:adapter:Qwen/Qwen2-0.5B",
"license:apache-2.0",
"region:us"
] | null | 2025-02-04T07:26:01Z |
---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2-0.5B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: d4d98edd-2c30-4be2-9e18-2bafdfabaf69
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
# d4d98edd-2c30-4be2-9e18-2bafdfabaf69
This model is a fine-tuned version of [Qwen/Qwen2-0.5B](https://huggingface.co/Qwen/Qwen2-0.5B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8268
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
HiTZ/Qwen2.5-14B-Instruct_ODESIA
|
HiTZ
| 2025-02-04T07:30:46Z | 19 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"ODESIA",
"conversational",
"es",
"base_model:Qwen/Qwen2.5-14B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-14B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-02-04T06:28:06Z |
---
library_name: transformers
tags:
- ODESIA
license: apache-2.0
language:
- es
pipeline_tag: text-generation
base_model:
- Qwen/Qwen2.5-14B-Instruct
---
<p align="center">
<br>
<img src="https://leaderboard.odesia.uned.es/sites/default/files/ODESIA_leaderboard.png" style="height: 250px;">
<br>
<h3 align="center">Evaluation of NLP models in Spanish</h3>
<h1 align="center">IXA Submission for the 2024 ODESIA Challenge</h1>
This model is the fine-tuned Qwen/Qwen2.5-14B-Instruct used in the IXA submission for the 2024 ODESIA Challenge.
- 📈 ODESIA Leaderboard: https://leaderboard.odesia.uned.es/leaderboard/challenge
You can use this model to reproduce our results using the code in this repository:
- 💻 GitHub: https://github.com/hitz-zentroa/Odesia-Struct
- 📒 System Description Paper: Cooming Soon
### Model Description
- **Developed by:** [Iker García-Ferrero](https://ikergarcia1996.github.io/Iker-Garcia-Ferrero/)
- **Language(s) (NLP):** Spanish
<div style="display: flex; justify-content: space-around; width: 100%;">
<div style="width: 50%;" align="left">
<a href="http://ixa.si.ehu.es/">
<img src="https://raw.githubusercontent.com/ikergarcia1996/Iker-Garcia-Ferrero/master/icons/ixa.png" width="50" height="50" alt="Ixa NLP Group">
</a>
</div>
<div style="width: 50%;" align="right">
<a href="http://www.hitz.eus/">
<img src="https://raw.githubusercontent.com/ikergarcia1996/Iker-Garcia-Ferrero/master/icons/Hitz.png" width="300" height="50" alt="HiTZ Basque Center for Language Technologies">
</a>
</div>
</div>
|
csb05/T5-base-ami
|
csb05
| 2025-02-04T07:29:50Z | 40 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-01-31T15:23:56Z |
---
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]
|
theship87/qwen25-14b-fork-Q8_0-GGUF
|
theship87
| 2025-02-04T07:29:36Z | 19 | 0 |
transformers
|
[
"transformers",
"gguf",
"chat",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:theship87/qwen25-14b-fork",
"base_model:quantized:theship87/qwen25-14b-fork",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-02-04T07:28:28Z |
---
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-14B-Instruct-1M/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
base_model: theship87/qwen25-14b-fork
tags:
- chat
- llama-cpp
- gguf-my-repo
library_name: transformers
---
# theship87/qwen25-14b-fork-Q8_0-GGUF
This model was converted to GGUF format from [`theship87/qwen25-14b-fork`](https://huggingface.co/theship87/qwen25-14b-fork) 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/theship87/qwen25-14b-fork) 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 theship87/qwen25-14b-fork-Q8_0-GGUF --hf-file qwen25-14b-fork-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo theship87/qwen25-14b-fork-Q8_0-GGUF --hf-file qwen25-14b-fork-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 theship87/qwen25-14b-fork-Q8_0-GGUF --hf-file qwen25-14b-fork-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo theship87/qwen25-14b-fork-Q8_0-GGUF --hf-file qwen25-14b-fork-q8_0.gguf -c 2048
```
|
lesso/b55601b3-6885-44f3-aef3-85f4a63cdfae
|
lesso
| 2025-02-04T07:27:48Z | 7 | 0 |
peft
|
[
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:NousResearch/Yarn-Mistral-7b-128k",
"base_model:adapter:NousResearch/Yarn-Mistral-7b-128k",
"license:apache-2.0",
"region:us"
] | null | 2025-02-04T07:17:06Z |
---
library_name: peft
license: apache-2.0
base_model: NousResearch/Yarn-Mistral-7b-128k
tags:
- axolotl
- generated_from_trainer
model-index:
- name: b55601b3-6885-44f3-aef3-85f4a63cdfae
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: NousResearch/Yarn-Mistral-7b-128k
bf16: true
chat_template: llama3
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
- e20abb3f0babace9_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/e20abb3f0babace9_train_data.json
type:
field_instruction: prompt
field_output: response
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 5
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
group_by_length: true
hub_model_id: lesso/b55601b3-6885-44f3-aef3-85f4a63cdfae
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001018
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: linear
max_grad_norm: 1.0
max_steps: 200
micro_batch_size: 4
mlflow_experiment_name: /tmp/god18/e20abb3f0babace9_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-5
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
saves_per_epoch: null
sequence_len: 1024
special_tokens:
pad_token: </s>
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: e53873d6-4472-47dd-a43c-a613b7de775e
wandb_project: ab-god18
wandb_run: your_name
wandb_runid: e53873d6-4472-47dd-a43c-a613b7de775e
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# b55601b3-6885-44f3-aef3-85f4a63cdfae
This model is a fine-tuned version of [NousResearch/Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4327
## 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: 0.0001018
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 4.511 | 0.0008 | 1 | 2.8469 |
| 6.3548 | 0.0376 | 50 | 2.5737 |
| 5.3467 | 0.0752 | 100 | 2.4924 |
| 7.0167 | 0.1129 | 150 | 2.4504 |
| 6.2436 | 0.1505 | 200 | 2.4327 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
minemaster01/Qwen-14B-Main
|
minemaster01
| 2025-02-04T07:27:32Z | 27 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/Qwen2.5-14B",
"base_model:finetune:unsloth/Qwen2.5-14B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-02-04T07:25:49Z |
---
base_model: unsloth/Qwen2.5-14B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** minemaster01
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-14B
This qwen2 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)
|
mrferr3t/5dab4b44-53cb-4d35-bdc6-c862ef2ff647
|
mrferr3t
| 2025-02-04T07:27:20Z | 7 | 0 |
peft
|
[
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:NousResearch/Yarn-Mistral-7b-128k",
"base_model:adapter:NousResearch/Yarn-Mistral-7b-128k",
"license:apache-2.0",
"region:us"
] | null | 2025-02-04T07:17:19Z |
---
library_name: peft
license: apache-2.0
base_model: NousResearch/Yarn-Mistral-7b-128k
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 5dab4b44-53cb-4d35-bdc6-c862ef2ff647
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
auto_find_batch_size: true
base_model: NousResearch/Yarn-Mistral-7b-128k
bf16: auto
chat_template: llama3
dataloader_num_workers: 12
dataset_prepared_path: null
datasets:
- data_files:
- e20abb3f0babace9_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/e20abb3f0babace9_train_data.json
type:
field_instruction: prompt
field_output: response
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 3
early_stopping_threshold: 0.001
eval_max_new_tokens: 128
eval_steps: 40
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: false
group_by_length: false
hub_model_id: mrferr3t/5dab4b44-53cb-4d35-bdc6-c862ef2ff647
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0003
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 100
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
micro_batch_size: 32
mlflow_experiment_name: /tmp/e20abb3f0babace9_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 50
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
s2_attention: null
sample_packing: false
save_steps: 40
saves_per_epoch: 0
sequence_len: 512
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: e53873d6-4472-47dd-a43c-a613b7de775e
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: e53873d6-4472-47dd-a43c-a613b7de775e
warmup_ratio: 0.05
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 5dab4b44-53cb-4d35-bdc6-c862ef2ff647
This model is a fine-tuned version of [NousResearch/Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4093
## 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: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use adamw_bnb_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 415
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0008 | 1 | 2.6752 |
| No log | 0.0301 | 40 | 2.5320 |
| No log | 0.0602 | 80 | 2.4448 |
| 5.1149 | 0.0903 | 120 | 2.4167 |
| 5.1149 | 0.1204 | 160 | 2.4103 |
| 4.9146 | 0.1505 | 200 | 2.3971 |
| 4.9146 | 0.1806 | 240 | 2.4005 |
| 4.9146 | 0.2107 | 280 | 2.4001 |
| 4.844 | 0.2408 | 320 | 2.4093 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.3.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1
|
prxy5604/73251c53-2262-4c7b-80d5-744ac4323c10
|
prxy5604
| 2025-02-04T07:25:56Z | 10 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Meta-Llama-3.1-8B-Instruct",
"base_model:adapter:unsloth/Meta-Llama-3.1-8B-Instruct",
"license:llama3.1",
"region:us"
] | null | 2025-02-04T06:58:28Z |
---
library_name: peft
license: llama3.1
base_model: unsloth/Meta-Llama-3.1-8B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 73251c53-2262-4c7b-80d5-744ac4323c10
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/Meta-Llama-3.1-8B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 002efd305435aa1f_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/002efd305435aa1f_train_data.json
type:
field_instruction: categories
field_output: prompt
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 5
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: prxy5604/73251c53-2262-4c7b-80d5-744ac4323c10
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 200
micro_batch_size: 8
mlflow_experiment_name: /tmp/002efd305435aa1f_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-5
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: cbc1c644-9662-490b-a40e-2b2c585d7477
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: cbc1c644-9662-490b-a40e-2b2c585d7477
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 73251c53-2262-4c7b-80d5-744ac4323c10
This model is a fine-tuned version of [unsloth/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/unsloth/Meta-Llama-3.1-8B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0099
## 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: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 4.1129 | 0.0075 | 1 | 6.0243 |
| 3.3164 | 0.3738 | 50 | 3.4309 |
| 4.3764 | 0.7477 | 100 | 4.0882 |
| 2.8042 | 1.1252 | 150 | 3.0498 |
| 3.172 | 1.4991 | 200 | 3.0099 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
daniel40/808e0612-0f02-4f38-9adf-4549212f9fb4
|
daniel40
| 2025-02-04T07:24:31Z | 9 | 0 |
peft
|
[
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:NousResearch/Yarn-Mistral-7b-128k",
"base_model:adapter:NousResearch/Yarn-Mistral-7b-128k",
"license:apache-2.0",
"region:us"
] | null | 2025-02-04T07:19:41Z |
---
library_name: peft
license: apache-2.0
base_model: NousResearch/Yarn-Mistral-7b-128k
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 808e0612-0f02-4f38-9adf-4549212f9fb4
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: NousResearch/Yarn-Mistral-7b-128k
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- e20abb3f0babace9_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/e20abb3f0babace9_train_data.json
type:
field_instruction: prompt
field_output: response
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: daniel40/808e0612-0f02-4f38-9adf-4549212f9fb4
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/e20abb3f0babace9_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: e53873d6-4472-47dd-a43c-a613b7de775e
wandb_project: Birthday-SN56-28-Gradients-On-Demand
wandb_run: your_name
wandb_runid: e53873d6-4472-47dd-a43c-a613b7de775e
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 808e0612-0f02-4f38-9adf-4549212f9fb4
This model is a fine-tuned version of [NousResearch/Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4887
## 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0008 | 1 | 2.8604 |
| 9.8436 | 0.0376 | 50 | 2.5530 |
| 9.878 | 0.0752 | 100 | 2.5159 |
| 10.0045 | 0.1129 | 150 | 2.4927 |
| 9.9431 | 0.1505 | 200 | 2.4887 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
mradermacher/DeepSeek-R1-Distill-Qwen-7B-GRPO-i1-GGUF
|
mradermacher
| 2025-02-04T07:23:42Z | 673 | 0 |
transformers
|
[
"transformers",
"gguf",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"en",
"dataset:DigitalLearningGmbH/MATH-lighteval",
"base_model:Dongwei/DeepSeek-R1-Distill-Qwen-7B-GRPO",
"base_model:quantized:Dongwei/DeepSeek-R1-Distill-Qwen-7B-GRPO",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-02-04T04:00:03Z |
---
base_model: Dongwei/DeepSeek-R1-Distill-Qwen-7B-GRPO
datasets: DigitalLearningGmbH/MATH-lighteval
language:
- en
library_name: transformers
model_name: DeepSeek-R1-Distill-Qwen-7B-GRPO
quantized_by: mradermacher
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Dongwei/DeepSeek-R1-Distill-Qwen-7B-GRPO
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-7B-GRPO-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/DeepSeek-R1-Distill-Qwen-7B-GRPO-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-7B-GRPO.i1-IQ1_S.gguf) | i1-IQ1_S | 2.0 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-7B-GRPO-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-7B-GRPO.i1-IQ1_M.gguf) | i1-IQ1_M | 2.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-7B-GRPO-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-7B-GRPO.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-7B-GRPO-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-7B-GRPO.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-7B-GRPO-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-7B-GRPO.i1-IQ2_S.gguf) | i1-IQ2_S | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-7B-GRPO-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-7B-GRPO.i1-IQ2_M.gguf) | i1-IQ2_M | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-7B-GRPO-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-7B-GRPO.i1-Q2_K_S.gguf) | i1-Q2_K_S | 2.9 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-7B-GRPO-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-7B-GRPO.i1-Q2_K.gguf) | i1-Q2_K | 3.1 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-7B-GRPO-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-7B-GRPO.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-7B-GRPO-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-7B-GRPO.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-7B-GRPO-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-7B-GRPO.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-7B-GRPO-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-7B-GRPO.i1-IQ3_S.gguf) | i1-IQ3_S | 3.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-7B-GRPO-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-7B-GRPO.i1-IQ3_M.gguf) | i1-IQ3_M | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-7B-GRPO-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-7B-GRPO.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.9 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-7B-GRPO-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-7B-GRPO.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-7B-GRPO-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-7B-GRPO.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.3 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-7B-GRPO-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-7B-GRPO.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.5 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-7B-GRPO-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-7B-GRPO.i1-Q4_0.gguf) | i1-Q4_0 | 4.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-7B-GRPO-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-7B-GRPO.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-7B-GRPO-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-7B-GRPO.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-7B-GRPO-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-7B-GRPO.i1-Q4_1.gguf) | i1-Q4_1 | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-7B-GRPO-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-7B-GRPO.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-7B-GRPO-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-7B-GRPO.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-7B-GRPO-i1-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-7B-GRPO.i1-Q6_K.gguf) | i1-Q6_K | 6.4 | 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 -->
|
kostiantynk/3557f904-a1f7-4122-ae18-e406f830eac6
|
kostiantynk
| 2025-02-04T07:22:53Z | 13 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2-0.5B-Instruct",
"base_model:adapter:Qwen/Qwen2-0.5B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2025-02-04T07:14:52Z |
---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2-0.5B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 3557f904-a1f7-4122-ae18-e406f830eac6
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: Qwen/Qwen2-0.5B-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 1097bb505648041d_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/1097bb505648041d_train_data.json
type:
field_instruction: instruction
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: false
group_by_length: false
hub_model_id: kostiantynk/3557f904-a1f7-4122-ae18-e406f830eac6
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: constant
max_steps: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/1097bb505648041d_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 34e13ffd-dc3a-4a07-b2ab-db4bc2dc7885
wandb_project: Mine-SN56-22-Gradients-On-Demand
wandb_run: your_name
wandb_runid: 34e13ffd-dc3a-4a07-b2ab-db4bc2dc7885
warmup_steps: 5
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 3557f904-a1f7-4122-ae18-e406f830eac6
This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4328
## 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0000 | 1 | 0.6428 |
| 0.4595 | 0.0020 | 50 | 0.4433 |
| 0.4267 | 0.0041 | 100 | 0.4348 |
| 0.4333 | 0.0061 | 150 | 0.4303 |
| 0.5336 | 0.0081 | 200 | 0.4328 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
mradermacher/DeepSeek-R1-Distill-Qwen-1.5B-GRPO_Math-GGUF
|
mradermacher
| 2025-02-04T07:20:53Z | 844 | 0 |
transformers
|
[
"transformers",
"gguf",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"en",
"dataset:DigitalLearningGmbH/MATH-lighteval",
"base_model:Dongwei/DeepSeek-R1-Distill-Qwen-1.5B-GRPO_Math",
"base_model:quantized:Dongwei/DeepSeek-R1-Distill-Qwen-1.5B-GRPO_Math",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-02-04T07:06:25Z |
---
base_model: Dongwei/DeepSeek-R1-Distill-Qwen-1.5B-GRPO_Math
datasets: DigitalLearningGmbH/MATH-lighteval
language:
- en
library_name: transformers
model_name: DeepSeek-R1-Distill-Qwen-1.5B-GRPO_Math
quantized_by: mradermacher
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Dongwei/DeepSeek-R1-Distill-Qwen-1.5B-GRPO_Math
<!-- 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/DeepSeek-R1-Distill-Qwen-1.5B-GRPO_Math-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-1.5B-GRPO_Math.Q2_K.gguf) | Q2_K | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-1.5B-GRPO_Math-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-1.5B-GRPO_Math.Q3_K_S.gguf) | Q3_K_S | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-1.5B-GRPO_Math-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-1.5B-GRPO_Math.Q3_K_M.gguf) | Q3_K_M | 1.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-1.5B-GRPO_Math-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-1.5B-GRPO_Math.Q3_K_L.gguf) | Q3_K_L | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-1.5B-GRPO_Math-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-1.5B-GRPO_Math.IQ4_XS.gguf) | IQ4_XS | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-1.5B-GRPO_Math-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-1.5B-GRPO_Math.Q4_K_S.gguf) | Q4_K_S | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-1.5B-GRPO_Math-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-1.5B-GRPO_Math.Q4_K_M.gguf) | Q4_K_M | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-1.5B-GRPO_Math-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-1.5B-GRPO_Math.Q5_K_S.gguf) | Q5_K_S | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-1.5B-GRPO_Math-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-1.5B-GRPO_Math.Q5_K_M.gguf) | Q5_K_M | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-1.5B-GRPO_Math-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-1.5B-GRPO_Math.Q6_K.gguf) | Q6_K | 1.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-1.5B-GRPO_Math-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-1.5B-GRPO_Math.Q8_0.gguf) | Q8_0 | 2.0 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-1.5B-GRPO_Math-GGUF/resolve/main/DeepSeek-R1-Distill-Qwen-1.5B-GRPO_Math.f16.gguf) | f16 | 3.7 | 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.
<!-- end -->
|
lesso/09bbcf20-afb8-4949-994e-428eb98286eb
|
lesso
| 2025-02-04T07:18:21Z | 9 | 0 |
peft
|
[
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Phi-3-medium-4k-instruct",
"base_model:adapter:unsloth/Phi-3-medium-4k-instruct",
"license:mit",
"region:us"
] | null | 2025-02-04T07:13:25Z |
---
library_name: peft
license: mit
base_model: unsloth/Phi-3-medium-4k-instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 09bbcf20-afb8-4949-994e-428eb98286eb
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/Phi-3-medium-4k-instruct
bf16: true
chat_template: llama3
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
- e3bd1ef8af189d5d_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/e3bd1ef8af189d5d_train_data.json
type:
field_input: prompt
field_instruction: instruction
field_output: canonical_solution
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 5
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: lesso/09bbcf20-afb8-4949-994e-428eb98286eb
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.000101
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: linear
max_grad_norm: 1.0
max_steps: 200
micro_batch_size: 8
mlflow_experiment_name: /tmp/god07/e3bd1ef8af189d5d_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-5
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 2f3a3a9a-2ebd-4e71-bbd6-6112a6e8e75e
wandb_project: ab-god07
wandb_run: your_name
wandb_runid: 2f3a3a9a-2ebd-4e71-bbd6-6112a6e8e75e
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 09bbcf20-afb8-4949-994e-428eb98286eb
This model is a fine-tuned version of [unsloth/Phi-3-medium-4k-instruct](https://huggingface.co/unsloth/Phi-3-medium-4k-instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5779
## 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: 0.000101
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- training_steps: 21
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.9665 | 0.1481 | 1 | 0.5779 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
Kyungjin-Kim/embc25_finetuned_fr
|
Kyungjin-Kim
| 2025-02-04T07:17:31Z | 13 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:Kyungjin-Kim/mmc_roberta_500000_fr",
"base_model:finetune:Kyungjin-Kim/mmc_roberta_500000_fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-01-28T23:28:25Z |
---
library_name: transformers
base_model: Kyungjin-Kim/mmc_roberta_500000_fr
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: embc25_finetuned_fr
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. -->
# embc25_finetuned_fr
This model is a fine-tuned version of [Kyungjin-Kim/mmc_roberta_500000_fr](https://huggingface.co/Kyungjin-Kim/mmc_roberta_500000_fr) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4235
- Accuracy: 0.6202
- Precision: 0.6656
- Recall: 0.7790
- F1: 0.7179
## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.6276 | 0.64 | 500 | 0.9535 | 0.6236 | 0.6299 | 0.9529 | 0.7585 |
| 0.5567 | 1.2790 | 1000 | 0.6283 | 0.5258 | 0.7097 | 0.3986 | 0.5104 |
| 0.5278 | 1.9190 | 1500 | 0.8207 | 0.6022 | 0.6882 | 0.6558 | 0.6716 |
| 0.4862 | 2.5581 | 2000 | 0.8486 | 0.6079 | 0.7010 | 0.6413 | 0.6698 |
| 0.4116 | 3.1971 | 2500 | 0.8388 | 0.5652 | 0.6950 | 0.5326 | 0.6031 |
| 0.3825 | 3.8371 | 3000 | 0.6555 | 0.5292 | 0.7195 | 0.3949 | 0.5099 |
| 0.34 | 4.4762 | 3500 | 1.2660 | 0.6079 | 0.6822 | 0.6884 | 0.6853 |
| 0.2856 | 5.1152 | 4000 | 1.2518 | 0.6034 | 0.6780 | 0.6866 | 0.6823 |
| 0.2663 | 5.7552 | 4500 | 1.4022 | 0.6124 | 0.6832 | 0.6993 | 0.6911 |
| 0.2493 | 6.3942 | 5000 | 1.5075 | 0.5966 | 0.6764 | 0.6703 | 0.6733 |
| 0.2036 | 7.0333 | 5500 | 1.6113 | 0.6112 | 0.6846 | 0.6920 | 0.6883 |
| 0.2028 | 7.6733 | 6000 | 2.0145 | 0.6157 | 0.6705 | 0.7482 | 0.7072 |
| 0.1691 | 8.3123 | 6500 | 1.7540 | 0.6022 | 0.6807 | 0.6757 | 0.6782 |
| 0.1593 | 8.9523 | 7000 | 2.0229 | 0.6079 | 0.6712 | 0.7210 | 0.6952 |
| 0.1486 | 9.5914 | 7500 | 2.3382 | 0.6169 | 0.6667 | 0.7645 | 0.7122 |
### Framework versions
- Transformers 4.48.1
- Pytorch 2.3.1
- Datasets 3.2.0
- Tokenizers 0.21.0
|
ciloku/1a9920da-d23d-46d4-9f97-39e8a4c654a9
|
ciloku
| 2025-02-04T07:17:07Z | 10 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:heegyu/WizardVicuna-open-llama-3b-v2",
"base_model:adapter:heegyu/WizardVicuna-open-llama-3b-v2",
"license:apache-2.0",
"region:us"
] | null | 2025-02-04T06:56:21Z |
---
library_name: peft
license: apache-2.0
base_model: heegyu/WizardVicuna-open-llama-3b-v2
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 1a9920da-d23d-46d4-9f97-39e8a4c654a9
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: heegyu/WizardVicuna-open-llama-3b-v2
bf16: true
chat_template: llama3
data_processes: 24
dataset_prepared_path: null
datasets:
- data_files:
- 9988054a1155975c_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/9988054a1155975c_train_data.json
type:
field_input: history
field_instruction: question
field_output: answer
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 4
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: ciloku/1a9920da-d23d-46d4-9f97-39e8a4c654a9
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 6.0e-05
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.04
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
lr_scheduler_warmup_steps: 50
max_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 200
micro_batch_size: 8
mlflow_experiment_name: /tmp/9988054a1155975c_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-8
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
saves_per_epoch: null
seed: 17333
sequence_len: 1024
special_tokens:
pad_token: </s>
strict: false
tf32: true
tokenizer_type: AutoTokenizer
total_train_batch_size: 32
train_batch_size: 8
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 29d53164-9e6f-42ae-a37f-4cb166ed6f4f
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 29d53164-9e6f-42ae-a37f-4cb166ed6f4f
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 1a9920da-d23d-46d4-9f97-39e8a4c654a9
This model is a fine-tuned version of [heegyu/WizardVicuna-open-llama-3b-v2](https://huggingface.co/heegyu/WizardVicuna-open-llama-3b-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7320
## 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: 6e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 17333
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-8
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.4015 | 0.0048 | 1 | 2.2110 |
| 2.0389 | 0.2418 | 50 | 1.8739 |
| 1.7169 | 0.4837 | 100 | 1.7830 |
| 1.4474 | 0.7255 | 150 | 1.7414 |
| 1.4039 | 0.9674 | 200 | 1.7320 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
mrferr3t/e0448e14-98d2-4c5d-9f92-478b18b4b6d5
|
mrferr3t
| 2025-02-04T07:15:26Z | 9 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Meta-Llama-3.1-8B-Instruct",
"base_model:adapter:unsloth/Meta-Llama-3.1-8B-Instruct",
"license:llama3.1",
"region:us"
] | null | 2025-02-04T07:01:30Z |
---
library_name: peft
license: llama3.1
base_model: unsloth/Meta-Llama-3.1-8B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: e0448e14-98d2-4c5d-9f92-478b18b4b6d5
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
auto_find_batch_size: true
base_model: unsloth/Meta-Llama-3.1-8B-Instruct
bf16: auto
chat_template: llama3
dataloader_num_workers: 12
dataset_prepared_path: null
datasets:
- data_files:
- 002efd305435aa1f_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/002efd305435aa1f_train_data.json
type:
field_instruction: categories
field_output: prompt
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 3
early_stopping_threshold: 0.001
eval_max_new_tokens: 128
eval_steps: 40
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: false
group_by_length: false
hub_model_id: mrferr3t/e0448e14-98d2-4c5d-9f92-478b18b4b6d5
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0003
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 100
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
micro_batch_size: 32
mlflow_experiment_name: /tmp/002efd305435aa1f_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 50
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
s2_attention: null
sample_packing: false
save_steps: 40
saves_per_epoch: 0
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: cbc1c644-9662-490b-a40e-2b2c585d7477
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: cbc1c644-9662-490b-a40e-2b2c585d7477
warmup_ratio: 0.05
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# e0448e14-98d2-4c5d-9f92-478b18b4b6d5
This model is a fine-tuned version of [unsloth/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/unsloth/Meta-Llama-3.1-8B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2043
## 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: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use adamw_bnb_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 167
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0037 | 1 | 4.9407 |
| No log | 0.1495 | 40 | 3.6108 |
| No log | 0.2991 | 80 | 3.0542 |
| 3.8584 | 0.4486 | 120 | 2.8532 |
| 3.8584 | 0.5981 | 160 | 2.7652 |
| 2.9718 | 0.7477 | 200 | 2.6611 |
| 2.9718 | 0.8972 | 240 | 2.5925 |
| 2.9718 | 1.0467 | 280 | 2.5290 |
| 2.6335 | 1.1963 | 320 | 2.4880 |
| 2.6335 | 1.3458 | 360 | 2.4438 |
| 2.3043 | 1.4953 | 400 | 2.3545 |
| 2.3043 | 1.6449 | 440 | 2.3107 |
| 2.3043 | 1.7944 | 480 | 2.2492 |
| 2.2369 | 1.9439 | 520 | 2.1840 |
| 2.2369 | 2.0935 | 560 | 2.2406 |
| 1.7829 | 2.2430 | 600 | 2.1947 |
| 1.7829 | 2.3925 | 640 | 2.2043 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.3.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1
|
sgyi22/singers
|
sgyi22
| 2025-02-04T07:15:19Z | 26 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-02-04T07:13:08Z |
---
base_model: unsloth/llama-3.2-3b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** sgyi22
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-unsloth-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)
|
great0001/b4b05c2f-9594-435f-bafb-75ea1c82284c
|
great0001
| 2025-02-04T07:13:28Z | 10 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:sethuiyer/Medichat-Llama3-8B",
"base_model:adapter:sethuiyer/Medichat-Llama3-8B",
"license:other",
"region:us"
] | null | 2025-02-04T06:46:28Z |
---
library_name: peft
license: other
base_model: sethuiyer/Medichat-Llama3-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: b4b05c2f-9594-435f-bafb-75ea1c82284c
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: sethuiyer/Medichat-Llama3-8B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 5f1e816c7394c7e5_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/5f1e816c7394c7e5_train_data.json
type:
field_input: final_rules
field_instruction: prompt
field_output: responseA
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: false
group_by_length: false
hub_model_id: great0001/b4b05c2f-9594-435f-bafb-75ea1c82284c
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/5f1e816c7394c7e5_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: fdf2939c-aac2-4c06-aed3-f2120cdaa978
wandb_project: Mine-SN56-20-Gradients-On-Demand
wandb_run: your_name
wandb_runid: fdf2939c-aac2-4c06-aed3-f2120cdaa978
warmup_steps: 5
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# b4b05c2f-9594-435f-bafb-75ea1c82284c
This model is a fine-tuned version of [sethuiyer/Medichat-Llama3-8B](https://huggingface.co/sethuiyer/Medichat-Llama3-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7967
## 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0000 | 1 | 1.0276 |
| 0.8324 | 0.0011 | 50 | 0.8423 |
| 0.8249 | 0.0023 | 100 | 0.8193 |
| 0.8397 | 0.0034 | 150 | 0.8015 |
| 0.8008 | 0.0045 | 200 | 0.7967 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
lesso/48d6e6ee-e420-486f-9fcb-a102b5c6efa9
|
lesso
| 2025-02-04T07:11:36Z | 9 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Meta-Llama-3.1-8B-Instruct",
"base_model:adapter:unsloth/Meta-Llama-3.1-8B-Instruct",
"license:llama3.1",
"region:us"
] | null | 2025-02-04T06:58:31Z |
---
library_name: peft
license: llama3.1
base_model: unsloth/Meta-Llama-3.1-8B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 48d6e6ee-e420-486f-9fcb-a102b5c6efa9
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/Meta-Llama-3.1-8B-Instruct
bf16: true
chat_template: llama3
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
- 002efd305435aa1f_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/002efd305435aa1f_train_data.json
type:
field_instruction: categories
field_output: prompt
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 5
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
group_by_length: true
hub_model_id: lesso/48d6e6ee-e420-486f-9fcb-a102b5c6efa9
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001017
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: linear
max_grad_norm: 1.0
max_steps: 200
micro_batch_size: 4
mlflow_experiment_name: /tmp/god17/002efd305435aa1f_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-5
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: cbc1c644-9662-490b-a40e-2b2c585d7477
wandb_project: ab-god17
wandb_run: your_name
wandb_runid: cbc1c644-9662-490b-a40e-2b2c585d7477
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 48d6e6ee-e420-486f-9fcb-a102b5c6efa9
This model is a fine-tuned version of [unsloth/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/unsloth/Meta-Llama-3.1-8B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1915
## 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: 0.0001017
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.5562 | 0.0019 | 1 | 6.0243 |
| 4.4875 | 0.0935 | 50 | 5.0176 |
| 4.5669 | 0.1869 | 100 | 4.0470 |
| 4.7428 | 0.2804 | 150 | 3.4745 |
| 5.4434 | 0.3738 | 200 | 3.1915 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
phongtintruong/meomeo-mhubert-vietbud-24-700
|
phongtintruong
| 2025-02-04T07:11:19Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"meomeo",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-02-04T07:10:38Z |
---
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]
|
jssky/bf01769c-51c1-4156-aeb0-d52baa36123d
|
jssky
| 2025-02-04T07:07:17Z | 8 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2-0.5B-Instruct",
"base_model:adapter:Qwen/Qwen2-0.5B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2025-02-04T06:57:50Z |
---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2-0.5B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: bf01769c-51c1-4156-aeb0-d52baa36123d
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.6.0`
```yaml
adapter: lora
base_model: Qwen/Qwen2-0.5B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 1097bb505648041d_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/1097bb505648041d_train_data.json
type:
field_instruction: instruction
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 5
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: jssky/bf01769c-51c1-4156-aeb0-d52baa36123d
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 200
micro_batch_size: 8
mlflow_experiment_name: /tmp/1097bb505648041d_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-5
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 34e13ffd-dc3a-4a07-b2ab-db4bc2dc7885
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 34e13ffd-dc3a-4a07-b2ab-db4bc2dc7885
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# bf01769c-51c1-4156-aeb0-d52baa36123d
This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3837
## 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: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_bnb_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.3132 | 0.0163 | 50 | 0.4586 |
| 0.2513 | 0.0325 | 100 | 0.4183 |
| 0.2718 | 0.0488 | 150 | 0.3912 |
| 0.2843 | 0.0650 | 200 | 0.3837 |
### Framework versions
- PEFT 0.14.0
- Transformers 4.46.3
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
guilxus/78bf1ad2-a7ab-42b4-b206-54f45c2c4c09
|
guilxus
| 2025-02-04T07:06:18Z | 10 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-1.5B-Instruct",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-02-04T05:36:41Z |
---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-1.5B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 78bf1ad2-a7ab-42b4-b206-54f45c2c4c09
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: Qwen/Qwen2.5-1.5B-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 0daa0a0b5a0ad7d2_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/0daa0a0b5a0ad7d2_train_data.json
type:
field_input: responses
field_instruction: problem
field_output: solution
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: null
eval_batch_size: 2
eval_max_new_tokens: 128
eval_steps: null
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: guilxus/78bf1ad2-a7ab-42b4-b206-54f45c2c4c09
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 0.0001
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/0daa0a0b5a0ad7d2_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: null
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: techspear-hub
wandb_mode: online
wandb_name: f7c5f243-ab14-40ad-875e-817690562f88
wandb_project: Gradients-On-11
wandb_run: your_name
wandb_runid: f7c5f243-ab14-40ad-875e-817690562f88
warmup_steps: 5
weight_decay: 0.01
xformers_attention: null
```
</details><br>
# 78bf1ad2-a7ab-42b4-b206-54f45c2c4c09
This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6320
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.4181 | 0.0033 | 200 | 0.6320 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
Best000/70517a01-7a99-4702-952e-a2ebe816e9a2
|
Best000
| 2025-02-04T07:04:30Z | 9 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Meta-Llama-3.1-8B-Instruct",
"base_model:adapter:unsloth/Meta-Llama-3.1-8B-Instruct",
"license:llama3.1",
"region:us"
] | null | 2025-02-04T06:59:07Z |
---
library_name: peft
license: llama3.1
base_model: unsloth/Meta-Llama-3.1-8B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 70517a01-7a99-4702-952e-a2ebe816e9a2
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
# 70517a01-7a99-4702-952e-a2ebe816e9a2
This model is a fine-tuned version of [unsloth/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/unsloth/Meta-Llama-3.1-8B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7980
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
robiulawaldev/da92a142-8a96-438e-80b3-13cf5e027f66
|
robiulawaldev
| 2025-02-04T07:03:51Z | 9 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Meta-Llama-3.1-8B-Instruct",
"base_model:adapter:unsloth/Meta-Llama-3.1-8B-Instruct",
"license:llama3.1",
"region:us"
] | null | 2025-02-04T06:58:37Z |
---
library_name: peft
license: llama3.1
base_model: unsloth/Meta-Llama-3.1-8B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: da92a142-8a96-438e-80b3-13cf5e027f66
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
# da92a142-8a96-438e-80b3-13cf5e027f66
This model is a fine-tuned version of [unsloth/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/unsloth/Meta-Llama-3.1-8B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.5477
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
mradermacher/Zurich-1.5B-GCv2-1m-GGUF
|
mradermacher
| 2025-02-04T07:02:48Z | 285 | 1 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"gammacorpus",
"zurich",
"chat",
"conversational",
"en",
"dataset:rubenroy/GammaCorpus-v2-1m",
"base_model:rubenroy/Zurich-1.5B-GCv2-1m",
"base_model:quantized:rubenroy/Zurich-1.5B-GCv2-1m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-02-03T19:38:35Z |
---
base_model: rubenroy/Zurich-1.5B-GCv2-1m
datasets:
- rubenroy/GammaCorpus-v2-1m
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- gammacorpus
- zurich
- chat
- conversational
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/rubenroy/Zurich-1.5B-GCv2-1m
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-1m-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/Zurich-1.5B-GCv2-1m-GGUF/resolve/main/Zurich-1.5B-GCv2-1m.Q2_K.gguf) | Q2_K | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-1m-GGUF/resolve/main/Zurich-1.5B-GCv2-1m.Q3_K_S.gguf) | Q3_K_S | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-1m-GGUF/resolve/main/Zurich-1.5B-GCv2-1m.Q3_K_M.gguf) | Q3_K_M | 0.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-1m-GGUF/resolve/main/Zurich-1.5B-GCv2-1m.Q3_K_L.gguf) | Q3_K_L | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-1m-GGUF/resolve/main/Zurich-1.5B-GCv2-1m.IQ4_XS.gguf) | IQ4_XS | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-1m-GGUF/resolve/main/Zurich-1.5B-GCv2-1m.Q4_K_S.gguf) | Q4_K_S | 1.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-1m-GGUF/resolve/main/Zurich-1.5B-GCv2-1m.Q4_K_M.gguf) | Q4_K_M | 1.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-1m-GGUF/resolve/main/Zurich-1.5B-GCv2-1m.Q5_K_S.gguf) | Q5_K_S | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-1m-GGUF/resolve/main/Zurich-1.5B-GCv2-1m.Q5_K_M.gguf) | Q5_K_M | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-1m-GGUF/resolve/main/Zurich-1.5B-GCv2-1m.Q6_K.gguf) | Q6_K | 1.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-1m-GGUF/resolve/main/Zurich-1.5B-GCv2-1m.Q8_0.gguf) | Q8_0 | 1.7 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-1m-GGUF/resolve/main/Zurich-1.5B-GCv2-1m.f16.gguf) | f16 | 3.2 | 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.
<!-- end -->
|
mradermacher/Zurich-1.5B-GCv2-500k-i1-GGUF
|
mradermacher
| 2025-02-04T07:02:21Z | 479 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"gammacorpus",
"zurich",
"chat",
"conversational",
"en",
"dataset:rubenroy/GammaCorpus-v2-500k",
"base_model:rubenroy/Zurich-1.5B-GCv2-500k",
"base_model:quantized:rubenroy/Zurich-1.5B-GCv2-500k",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2025-02-04T06:40:30Z |
---
base_model: rubenroy/Zurich-1.5B-GCv2-500k
datasets:
- rubenroy/GammaCorpus-v2-500k
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- gammacorpus
- zurich
- chat
- conversational
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/rubenroy/Zurich-1.5B-GCv2-500k
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-500k-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/Zurich-1.5B-GCv2-500k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-500k.i1-IQ1_S.gguf) | i1-IQ1_S | 0.5 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-500k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-500k.i1-IQ1_M.gguf) | i1-IQ1_M | 0.6 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-500k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-500k.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.6 | |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-500k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-500k.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-500k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-500k.i1-IQ2_S.gguf) | i1-IQ2_S | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-500k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-500k.i1-IQ2_M.gguf) | i1-IQ2_M | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-500k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-500k.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.7 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-500k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-500k.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-500k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-500k.i1-Q2_K.gguf) | i1-Q2_K | 0.8 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-500k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-500k.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-500k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-500k.i1-Q3_K_S.gguf) | i1-Q3_K_S | 0.9 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-500k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-500k.i1-IQ3_S.gguf) | i1-IQ3_S | 0.9 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-500k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-500k.i1-IQ3_M.gguf) | i1-IQ3_M | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-500k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-500k.i1-Q3_K_M.gguf) | i1-Q3_K_M | 0.9 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-500k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-500k.i1-Q3_K_L.gguf) | i1-Q3_K_L | 1.0 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-500k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-500k.i1-IQ4_XS.gguf) | i1-IQ4_XS | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-500k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-500k.i1-IQ4_NL.gguf) | i1-IQ4_NL | 1.0 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-500k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-500k.i1-Q4_0.gguf) | i1-Q4_0 | 1.0 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-500k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-500k.i1-Q4_K_S.gguf) | i1-Q4_K_S | 1.0 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-500k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-500k.i1-Q4_K_M.gguf) | i1-Q4_K_M | 1.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-500k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-500k.i1-Q4_1.gguf) | i1-Q4_1 | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-500k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-500k.i1-Q5_K_S.gguf) | i1-Q5_K_S | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-500k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-500k.i1-Q5_K_M.gguf) | i1-Q5_K_M | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-500k-i1-GGUF/resolve/main/Zurich-1.5B-GCv2-500k.i1-Q6_K.gguf) | i1-Q6_K | 1.4 | 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 -->
|
ciloku/1a8f9f20-cd2f-48f3-86e6-36dff660d19a
|
ciloku
| 2025-02-04T06:55:18Z | 10 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/tinyllama",
"base_model:adapter:unsloth/tinyllama",
"license:apache-2.0",
"region:us"
] | null | 2025-02-04T06:34:33Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/tinyllama
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 1a8f9f20-cd2f-48f3-86e6-36dff660d19a
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/tinyllama
bf16: true
chat_template: llama3
data_processes: 24
dataset_prepared_path: null
datasets:
- data_files:
- 74fd83b58bc4ad47_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/74fd83b58bc4ad47_train_data.json
type:
field_input: conversation
field_instruction: note
field_output: summary
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 4
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: ciloku/1a8f9f20-cd2f-48f3-86e6-36dff660d19a
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 6.0e-05
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.04
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
lr_scheduler_warmup_steps: 50
max_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 200
micro_batch_size: 8
mlflow_experiment_name: /tmp/74fd83b58bc4ad47_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-8
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
saves_per_epoch: null
seed: 17333
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
total_train_batch_size: 32
train_batch_size: 8
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 21068da4-737c-49df-9240-0bd8ff25df8b
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 21068da4-737c-49df-9240-0bd8ff25df8b
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 1a8f9f20-cd2f-48f3-86e6-36dff660d19a
This model is a fine-tuned version of [unsloth/tinyllama](https://huggingface.co/unsloth/tinyllama) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1829
## 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: 6e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 17333
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-8
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.8814 | 0.0011 | 1 | 0.9321 |
| 0.2613 | 0.0565 | 50 | 0.2263 |
| 0.2026 | 0.1130 | 100 | 0.1945 |
| 0.1999 | 0.1695 | 150 | 0.1862 |
| 0.1915 | 0.2261 | 200 | 0.1829 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
mradermacher/Dumpling-Qwen2.5-1.5B-v2-GGUF
|
mradermacher
| 2025-02-04T06:53:34Z | 254 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"dataset:nbeerbower/GreatFirewall-DPO",
"dataset:nbeerbower/Schule-DPO",
"dataset:nbeerbower/Purpura-DPO",
"dataset:nbeerbower/Arkhaios-DPO",
"dataset:jondurbin/truthy-dpo-v0.1",
"dataset:antiven0m/physical-reasoning-dpo",
"dataset:flammenai/Date-DPO-NoAsterisks",
"dataset:flammenai/Prude-Phi3-DPO",
"dataset:Atsunori/HelpSteer2-DPO",
"dataset:jondurbin/gutenberg-dpo-v0.1",
"dataset:nbeerbower/gutenberg2-dpo",
"dataset:nbeerbower/gutenberg-moderne-dpo",
"base_model:nbeerbower/Dumpling-Qwen2.5-1.5B-v2",
"base_model:quantized:nbeerbower/Dumpling-Qwen2.5-1.5B-v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-02-04T06:44:02Z |
---
base_model: nbeerbower/Dumpling-Qwen2.5-1.5B-v2
datasets:
- nbeerbower/GreatFirewall-DPO
- nbeerbower/Schule-DPO
- nbeerbower/Purpura-DPO
- nbeerbower/Arkhaios-DPO
- jondurbin/truthy-dpo-v0.1
- antiven0m/physical-reasoning-dpo
- flammenai/Date-DPO-NoAsterisks
- flammenai/Prude-Phi3-DPO
- Atsunori/HelpSteer2-DPO
- jondurbin/gutenberg-dpo-v0.1
- nbeerbower/gutenberg2-dpo
- nbeerbower/gutenberg-moderne-dpo
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/nbeerbower/Dumpling-Qwen2.5-1.5B-v2
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Dumpling-Qwen2.5-1.5B-v2-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/Dumpling-Qwen2.5-1.5B-v2-GGUF/resolve/main/Dumpling-Qwen2.5-1.5B-v2.Q2_K.gguf) | Q2_K | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-1.5B-v2-GGUF/resolve/main/Dumpling-Qwen2.5-1.5B-v2.Q3_K_S.gguf) | Q3_K_S | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-1.5B-v2-GGUF/resolve/main/Dumpling-Qwen2.5-1.5B-v2.Q3_K_M.gguf) | Q3_K_M | 0.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-1.5B-v2-GGUF/resolve/main/Dumpling-Qwen2.5-1.5B-v2.Q3_K_L.gguf) | Q3_K_L | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-1.5B-v2-GGUF/resolve/main/Dumpling-Qwen2.5-1.5B-v2.IQ4_XS.gguf) | IQ4_XS | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-1.5B-v2-GGUF/resolve/main/Dumpling-Qwen2.5-1.5B-v2.Q4_K_S.gguf) | Q4_K_S | 1.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-1.5B-v2-GGUF/resolve/main/Dumpling-Qwen2.5-1.5B-v2.Q4_K_M.gguf) | Q4_K_M | 1.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-1.5B-v2-GGUF/resolve/main/Dumpling-Qwen2.5-1.5B-v2.Q5_K_S.gguf) | Q5_K_S | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-1.5B-v2-GGUF/resolve/main/Dumpling-Qwen2.5-1.5B-v2.Q5_K_M.gguf) | Q5_K_M | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-1.5B-v2-GGUF/resolve/main/Dumpling-Qwen2.5-1.5B-v2.Q6_K.gguf) | Q6_K | 1.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-1.5B-v2-GGUF/resolve/main/Dumpling-Qwen2.5-1.5B-v2.Q8_0.gguf) | Q8_0 | 1.7 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Dumpling-Qwen2.5-1.5B-v2-GGUF/resolve/main/Dumpling-Qwen2.5-1.5B-v2.f16.gguf) | f16 | 3.2 | 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.
<!-- end -->
|
lesso/c6106df3-4ba0-4a23-a715-762e40d22c0e
|
lesso
| 2025-02-04T06:52:50Z | 7 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:codellama/CodeLlama-7b-hf",
"base_model:adapter:codellama/CodeLlama-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-02-04T06:26:11Z |
---
library_name: peft
license: llama2
base_model: codellama/CodeLlama-7b-hf
tags:
- axolotl
- generated_from_trainer
model-index:
- name: c6106df3-4ba0-4a23-a715-762e40d22c0e
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: codellama/CodeLlama-7b-hf
bf16: true
chat_template: llama3
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
- 4b71679bf440e2b5_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/4b71679bf440e2b5_train_data.json
type:
field_instruction: abstract
field_output: title
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 5
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
group_by_length: true
hub_model_id: lesso/c6106df3-4ba0-4a23-a715-762e40d22c0e
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001018
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: linear
max_grad_norm: 1.0
max_steps: 200
micro_batch_size: 4
mlflow_experiment_name: /tmp/god18/4b71679bf440e2b5_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-5
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
saves_per_epoch: null
sequence_len: 1024
special_tokens:
pad_token: </s>
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: bda11099-2d05-46c6-8518-7d702cb71df4
wandb_project: ab-god18
wandb_run: your_name
wandb_runid: bda11099-2d05-46c6-8518-7d702cb71df4
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# c6106df3-4ba0-4a23-a715-762e40d22c0e
This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2474
## 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: 0.0001018
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.12 | 0.0001 | 1 | 2.3092 |
| 1.1286 | 0.0042 | 50 | 1.2796 |
| 1.5765 | 0.0084 | 100 | 1.2592 |
| 0.9354 | 0.0127 | 150 | 1.2562 |
| 1.2843 | 0.0169 | 200 | 1.2474 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
fifxus/347d445e-3e1d-4638-8585-af676d377273
|
fifxus
| 2025-02-04T06:52:05Z | 10 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-1.5B-Instruct",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-02-04T05:28:37Z |
---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-1.5B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 347d445e-3e1d-4638-8585-af676d377273
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: Qwen/Qwen2.5-1.5B-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 0daa0a0b5a0ad7d2_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/0daa0a0b5a0ad7d2_train_data.json
type:
field_input: responses
field_instruction: problem
field_output: solution
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: null
eval_batch_size: 2
eval_max_new_tokens: 128
eval_steps: null
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: true
hub_model_id: fifxus/347d445e-3e1d-4638-8585-af676d377273
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 0.0001
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/0daa0a0b5a0ad7d2_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: null
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: techspear-hub
wandb_mode: online
wandb_name: f7c5f243-ab14-40ad-875e-817690562f88
wandb_project: Gradients-On-10
wandb_run: your_name
wandb_runid: f7c5f243-ab14-40ad-875e-817690562f88
warmup_steps: 5
weight_decay: 0.01
xformers_attention: null
```
</details><br>
# 347d445e-3e1d-4638-8585-af676d377273
This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6324
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.4172 | 0.0033 | 200 | 0.6324 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
lesso/62dc1338-3b02-47f2-b1d3-2b61c4368021
|
lesso
| 2025-02-04T06:52:04Z | 27 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:NovaSearch/stella_en_1.5B_v5",
"base_model:adapter:NovaSearch/stella_en_1.5B_v5",
"license:mit",
"region:us"
] | null | 2025-02-04T06:45:42Z |
---
library_name: peft
license: mit
base_model: dunzhang/stella_en_1.5B_v5
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 62dc1338-3b02-47f2-b1d3-2b61c4368021
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: dunzhang/stella_en_1.5B_v5
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 1a9db54f2fc999cd_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/1a9db54f2fc999cd_train_data.json
type:
field_input: input
field_instruction: instruction
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 5
eval_batch_size: 2
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
group_by_length: true
hub_model_id: lesso/62dc1338-3b02-47f2-b1d3-2b61c4368021
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001015
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: linear
max_grad_norm: 1.0
max_steps: 150
micro_batch_size: 2
mlflow_experiment_name: /tmp/G.O.D/1a9db54f2fc999cd_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-5
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
saves_per_epoch: null
sequence_len: 512
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: f3e0e6d6-a02f-4373-8411-f1b78492e577
wandb_project: ab-god15
wandb_run: your_name
wandb_runid: f3e0e6d6-a02f-4373-8411-f1b78492e577
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 62dc1338-3b02-47f2-b1d3-2b61c4368021
This model is a fine-tuned version of [dunzhang/stella_en_1.5B_v5](https://huggingface.co/dunzhang/stella_en_1.5B_v5) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## 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: 0.0001015
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- training_steps: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0 | 0.0005 | 1 | nan |
| 0.0 | 0.0226 | 50 | nan |
| 0.0 | 0.0453 | 100 | nan |
| 0.0 | 0.0679 | 150 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
exp-models/phi-4-pruned
|
exp-models
| 2025-02-04T06:51:50Z | 35 | 1 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:unsloth/phi-4",
"base_model:finetune:unsloth/phi-4",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-02-04T06:47:46Z |
---
base_model:
- unsloth/phi-4
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 Passthrough merge method.
### Models Merged
The following models were included in the merge:
* [unsloth/phi-4](https://huggingface.co/unsloth/phi-4)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: unsloth/phi-4
layer_range: [0, 30]
- sources:
- model: unsloth/phi-4
layer_range: [38,40]
merge_method: passthrough
dtype: bfloat16
```
|
RichardErkhov/ssmits_-_Falcon2-5.5B-Italian-4bits
|
RichardErkhov
| 2025-02-04T06:46:27Z | 5 | 0 | null |
[
"safetensors",
"falcon",
"custom_code",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-02-04T06:44:22Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Falcon2-5.5B-Italian - bnb 4bits
- Model creator: https://huggingface.co/ssmits/
- Original model: https://huggingface.co/ssmits/Falcon2-5.5B-Italian/
Original model description:
---
base_model:
- tiiuae/falcon-11B
library_name: transformers
tags:
- mergekit
- merge
- lazymergekit
license: apache-2.0
language:
- it
---
## Why prune?
Even though [Falcon-11B](https://huggingface.co/tiiuae/falcon-11B) is trained on 5T tokens, it is still undertrained, as can be seen by this graph:
This is why the choice is made to prune 50% of the layers.
Note that \~1B of continued pre-training (\~1M rows of 1k tokens) is still required to restore the perplexity of this model in the desired language.
I'm planning on doing that for certain languages, depending on how much compute will be available.
# sliced
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 passthrough merge method.
### Models Merged
The following models were included in the merge:
* [tiiuae/falcon-11B](https://huggingface.co/tiiuae/falcon-11B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: tiiuae/falcon-11B
layer_range: [0, 25]
- sources:
- model: tiiuae/falcon-11B
layer_range: [56, 59]
merge_method: passthrough
dtype: bfloat16
```
[PruneMe](https://github.com/arcee-ai/PruneMe) has been utilized using the wikimedia/wikipedia Italian (it) subset by investigating layer similarity with 2000 samples. The layer ranges for pruning were determined based on this analysis to maintain performance while reducing model size.
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "ssmits/Falcon2-5.5B-Italian"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
)
sequences = pipeline(
"Can you explain the concepts of Quantum Computing?",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
💥 **Falcon LLMs require PyTorch 2.0 for use with `transformers`!**
For fast inference with Falcon, check-out [Text Generation Inference](https://github.com/huggingface/text-generation-inference)! Read more in this [blogpost]((https://huggingface.co/blog/falcon).
## Direct Use
Research on large language models; as a foundation for further specialization and finetuning for specific usecases (e.g., summarization, text generation, chatbot, etc.)
## Out-of-Scope Use
Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful.
## Bias, Risks, and Limitations
Falcon2-5.5B is trained mostly on English, but also German, Spanish, French, Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish. It will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online.
## Recommendations
We recommend users of Falcon2-5.5B to consider finetuning it for the specific set of tasks of interest, and for guardrails and appropriate precautions to be taken for any production use.
|
mradermacher/Zurich-1.5B-GCv2-500k-GGUF
|
mradermacher
| 2025-02-04T06:44:27Z | 263 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"gammacorpus",
"zurich",
"chat",
"conversational",
"en",
"dataset:rubenroy/GammaCorpus-v2-500k",
"base_model:rubenroy/Zurich-1.5B-GCv2-500k",
"base_model:quantized:rubenroy/Zurich-1.5B-GCv2-500k",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-02-03T18:58:06Z |
---
base_model: rubenroy/Zurich-1.5B-GCv2-500k
datasets:
- rubenroy/GammaCorpus-v2-500k
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- gammacorpus
- zurich
- chat
- conversational
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/rubenroy/Zurich-1.5B-GCv2-500k
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-500k-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/Zurich-1.5B-GCv2-500k-GGUF/resolve/main/Zurich-1.5B-GCv2-500k.Q2_K.gguf) | Q2_K | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-500k-GGUF/resolve/main/Zurich-1.5B-GCv2-500k.Q3_K_S.gguf) | Q3_K_S | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-500k-GGUF/resolve/main/Zurich-1.5B-GCv2-500k.Q3_K_M.gguf) | Q3_K_M | 0.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-500k-GGUF/resolve/main/Zurich-1.5B-GCv2-500k.Q3_K_L.gguf) | Q3_K_L | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-500k-GGUF/resolve/main/Zurich-1.5B-GCv2-500k.IQ4_XS.gguf) | IQ4_XS | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-500k-GGUF/resolve/main/Zurich-1.5B-GCv2-500k.Q4_K_S.gguf) | Q4_K_S | 1.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-500k-GGUF/resolve/main/Zurich-1.5B-GCv2-500k.Q4_K_M.gguf) | Q4_K_M | 1.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-500k-GGUF/resolve/main/Zurich-1.5B-GCv2-500k.Q5_K_S.gguf) | Q5_K_S | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-500k-GGUF/resolve/main/Zurich-1.5B-GCv2-500k.Q5_K_M.gguf) | Q5_K_M | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-500k-GGUF/resolve/main/Zurich-1.5B-GCv2-500k.Q6_K.gguf) | Q6_K | 1.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-500k-GGUF/resolve/main/Zurich-1.5B-GCv2-500k.Q8_0.gguf) | Q8_0 | 1.7 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Zurich-1.5B-GCv2-500k-GGUF/resolve/main/Zurich-1.5B-GCv2-500k.f16.gguf) | f16 | 3.2 | 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.
<!-- end -->
|
faluradu/git-base-appliances
|
faluradu
| 2025-02-04T06:44:00Z | 65 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"git",
"image-text-to-text",
"generated_from_trainer",
"base_model:microsoft/git-base",
"base_model:finetune:microsoft/git-base",
"license:mit",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2024-11-13T10:03:00Z |
---
library_name: transformers
license: mit
base_model: microsoft/git-base
tags:
- generated_from_trainer
model-index:
- name: git-base-appliances
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. -->
# git-base-appliances
This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4281
- Wer Score: 3.2039
## 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: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Score |
|:-------------:|:-------:|:----:|:---------------:|:---------:|
| 84.9149 | 3.6893 | 100 | 2.9324 | 3.8749 |
| 39.5513 | 7.3991 | 200 | 2.6633 | 2.6427 |
| 28.5436 | 11.1088 | 300 | 2.7566 | 3.0925 |
| 20.9713 | 14.7982 | 400 | 2.8737 | 3.2221 |
| 15.2719 | 18.5079 | 500 | 2.9953 | 2.9620 |
| 11.4351 | 22.2177 | 600 | 3.1084 | 3.0657 |
| 8.6091 | 25.9070 | 700 | 3.1823 | 3.1642 |
| 6.4907 | 29.6168 | 800 | 3.2530 | 3.0930 |
| 5.0524 | 33.3265 | 900 | 3.3025 | 3.1223 |
| 4.0674 | 37.0363 | 1000 | 3.3509 | 3.1089 |
| 3.3508 | 40.7256 | 1100 | 3.3843 | 3.1288 |
| 2.8752 | 44.4354 | 1200 | 3.4132 | 3.1570 |
| 2.5338 | 48.1451 | 1300 | 3.4281 | 3.2039 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
|
justinj92/Qwen2.5-1.5B-Thinking-v1.1-Q8_0-GGUF
|
justinj92
| 2025-02-04T06:43:54Z | 8 | 0 |
transformers
|
[
"transformers",
"gguf",
"generated_from_trainer",
"trl",
"grpo",
"llama-cpp",
"gguf-my-repo",
"base_model:justinj92/Qwen2.5-1.5B-Thinking-v1.1",
"base_model:quantized:justinj92/Qwen2.5-1.5B-Thinking-v1.1",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-02-03T07:04:01Z |
---
base_model: justinj92/Qwen2.5-1.5B-Thinking-v1.1
library_name: transformers
model_name: Qwen2.5-1.5B-Thinking-v1.1-Q8_0-GGUF
tags:
- generated_from_trainer
- trl
- grpo
- llama-cpp
- gguf-my-repo
licence: license
---
# justinj92/Qwen2.5-1.5B-Thinking-v1.1-Q8_0-GGUF
This model was converted to GGUF format from [`justinj92/Qwen2.5-1.5B-Thinking-v1.1`](https://huggingface.co/justinj92/Qwen2.5-1.5B-Thinking-v1.1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/justinj92/Qwen2.5-1.5B-Thinking-v1.1) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo justinj92/Qwen2.5-1.5B-Thinking-v1.1-Q8_0-GGUF --hf-file Qwen2.5-1.5B-Thinking-v1.1-Q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo justinj92/Qwen2.5-1.5B-Thinking-v1.1-Q8_0-GGUF --hf-file Qwen2.5-1.5B-Thinking-v1.1-Q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo justinj92/Qwen2.5-1.5B-Thinking-v1.1-Q8_0-GGUF --hf-file Qwen2.5-1.5B-Thinking-v1.1-Q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo justinj92/Qwen2.5-1.5B-Thinking-v1.1-Q8_0-GGUF --hf-file Qwen2.5-1.5B-Thinking-v1.1-Q8_0.gguf -c 2048
```
|
fabian6567/model-overfitted-Q5_K_M-GGUF
|
fabian6567
| 2025-02-04T06:41:59Z | 23 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"sft",
"llama-cpp",
"gguf-my-repo",
"en",
"base_model:fabian6567/model-overfitted",
"base_model:quantized:fabian6567/model-overfitted",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-02-04T06:41:32Z |
---
base_model: fabian6567/model-overfitted
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
- llama-cpp
- gguf-my-repo
license: apache-2.0
language:
- en
---
# fabian6567/model-overfitted-Q5_K_M-GGUF
This model was converted to GGUF format from [`fabian6567/model-overfitted`](https://huggingface.co/fabian6567/model-overfitted) 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/fabian6567/model-overfitted) 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 fabian6567/model-overfitted-Q5_K_M-GGUF --hf-file model-overfitted-q5_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo fabian6567/model-overfitted-Q5_K_M-GGUF --hf-file model-overfitted-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 fabian6567/model-overfitted-Q5_K_M-GGUF --hf-file model-overfitted-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo fabian6567/model-overfitted-Q5_K_M-GGUF --hf-file model-overfitted-q5_k_m.gguf -c 2048
```
|
rak-r05/ab3246b0-d6f1-42ca-89dc-bff48c757c84
|
rak-r05
| 2025-02-04T06:41:53Z | 8 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2-1.5B",
"base_model:adapter:unsloth/Qwen2-1.5B",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-02-04T06:35:24Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2-1.5B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: ab3246b0-d6f1-42ca-89dc-bff48c757c84
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/Qwen2-1.5B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 81c995f7f7a208e0_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/81c995f7f7a208e0_train_data.json
type:
field_instruction: prompt
field_output: chosen
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: rak-r05/ab3246b0-d6f1-42ca-89dc-bff48c757c84
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0004
load_in_4bit: false
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_steps: 150
micro_batch_size: 2
mlflow_experiment_name: /tmp/81c995f7f7a208e0_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 8b3f7321-a7c6-4fbb-8fcc-0c19b58f67a0
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 8b3f7321-a7c6-4fbb-8fcc-0c19b58f67a0
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# ab3246b0-d6f1-42ca-89dc-bff48c757c84
This model is a fine-tuned version of [unsloth/Qwen2-1.5B](https://huggingface.co/unsloth/Qwen2-1.5B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## 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: 0.0004
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0 | 0.0018 | 1 | nan |
| 0.0 | 0.0674 | 38 | nan |
| 0.0 | 0.1348 | 76 | nan |
| 0.0 | 0.2022 | 114 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
great0001/8e5b3db1-4aa2-4eaf-a5ec-c74e9a552419
|
great0001
| 2025-02-04T06:41:21Z | 10 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:codellama/CodeLlama-7b-hf",
"base_model:adapter:codellama/CodeLlama-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-02-04T06:26:06Z |
---
library_name: peft
license: llama2
base_model: codellama/CodeLlama-7b-hf
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 8e5b3db1-4aa2-4eaf-a5ec-c74e9a552419
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: codellama/CodeLlama-7b-hf
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 4b71679bf440e2b5_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/4b71679bf440e2b5_train_data.json
type:
field_instruction: abstract
field_output: title
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: great0001/8e5b3db1-4aa2-4eaf-a5ec-c74e9a552419
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: constant
max_steps: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/4b71679bf440e2b5_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: bda11099-2d05-46c6-8518-7d702cb71df4
wandb_project: Birthday-SN56-33-Gradients-On-Demand
wandb_run: your_name
wandb_runid: bda11099-2d05-46c6-8518-7d702cb71df4
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 8e5b3db1-4aa2-4eaf-a5ec-c74e9a552419
This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 10
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0001 | 1 | nan |
| 0.3413 | 0.0042 | 50 | nan |
| 0.0 | 0.0084 | 100 | nan |
| 1.0114 | 0.0127 | 150 | nan |
| 0.0 | 0.0169 | 200 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
shibajustfor/22800be0-cd23-46e7-8499-951b440597d6
|
shibajustfor
| 2025-02-04T06:40:57Z | 9 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:codellama/CodeLlama-7b-hf",
"base_model:adapter:codellama/CodeLlama-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-02-04T06:26:05Z |
---
library_name: peft
license: llama2
base_model: codellama/CodeLlama-7b-hf
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 22800be0-cd23-46e7-8499-951b440597d6
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: codellama/CodeLlama-7b-hf
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 4b71679bf440e2b5_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/4b71679bf440e2b5_train_data.json
type:
field_instruction: abstract
field_output: title
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: shibajustfor/22800be0-cd23-46e7-8499-951b440597d6
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/4b71679bf440e2b5_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: bda11099-2d05-46c6-8518-7d702cb71df4
wandb_project: Birthday-SN56-39-Gradients-On-Demand
wandb_run: your_name
wandb_runid: bda11099-2d05-46c6-8518-7d702cb71df4
warmup_steps: 5
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 22800be0-cd23-46e7-8499-951b440597d6
This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0001 | 1 | nan |
| 0.3413 | 0.0042 | 50 | nan |
| 0.0 | 0.0084 | 100 | nan |
| 1.0114 | 0.0127 | 150 | nan |
| 0.0 | 0.0169 | 200 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
mradermacher/SCE-2-24B-GGUF
|
mradermacher
| 2025-02-04T06:40:32Z | 314 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:Cran-May/SCE-2-24B",
"base_model:quantized:Cran-May/SCE-2-24B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-02-03T17:53:34Z |
---
base_model: Cran-May/SCE-2-24B
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: -->
static quants of https://huggingface.co/Cran-May/SCE-2-24B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/SCE-2-24B-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/SCE-2-24B-GGUF/resolve/main/SCE-2-24B.Q2_K.gguf) | Q2_K | 9.0 | |
| [GGUF](https://huggingface.co/mradermacher/SCE-2-24B-GGUF/resolve/main/SCE-2-24B.Q3_K_S.gguf) | Q3_K_S | 10.5 | |
| [GGUF](https://huggingface.co/mradermacher/SCE-2-24B-GGUF/resolve/main/SCE-2-24B.Q3_K_M.gguf) | Q3_K_M | 11.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/SCE-2-24B-GGUF/resolve/main/SCE-2-24B.Q3_K_L.gguf) | Q3_K_L | 12.5 | |
| [GGUF](https://huggingface.co/mradermacher/SCE-2-24B-GGUF/resolve/main/SCE-2-24B.IQ4_XS.gguf) | IQ4_XS | 13.0 | |
| [GGUF](https://huggingface.co/mradermacher/SCE-2-24B-GGUF/resolve/main/SCE-2-24B.Q4_K_S.gguf) | Q4_K_S | 13.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/SCE-2-24B-GGUF/resolve/main/SCE-2-24B.Q4_K_M.gguf) | Q4_K_M | 14.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/SCE-2-24B-GGUF/resolve/main/SCE-2-24B.Q5_K_S.gguf) | Q5_K_S | 16.4 | |
| [GGUF](https://huggingface.co/mradermacher/SCE-2-24B-GGUF/resolve/main/SCE-2-24B.Q5_K_M.gguf) | Q5_K_M | 16.9 | |
| [GGUF](https://huggingface.co/mradermacher/SCE-2-24B-GGUF/resolve/main/SCE-2-24B.Q6_K.gguf) | Q6_K | 19.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/SCE-2-24B-GGUF/resolve/main/SCE-2-24B.Q8_0.gguf) | Q8_0 | 25.2 | 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 -->
|
shoaibsaadat/ailyascharac-lora
|
shoaibsaadat
| 2025-02-04T06:39:34Z | 24 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-02-04T06:26:29Z |
---
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
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: ailyascharac
---
# Ailyascharac Lora
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `ailyascharac` to trigger the image generation.
## 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.float16).to('cuda')
pipeline.load_lora_weights('shoaibsaadat/ailyascharac-lora', weight_name='lora.safetensors')
image = pipeline('your prompt').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)
|
mrferr3t/c45ed0e9-140f-4c89-9882-409edc73abaa
|
mrferr3t
| 2025-02-04T06:38:46Z | 9 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/tinyllama",
"base_model:adapter:unsloth/tinyllama",
"license:apache-2.0",
"region:us"
] | null | 2025-02-04T05:44:19Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/tinyllama
tags:
- axolotl
- generated_from_trainer
model-index:
- name: c45ed0e9-140f-4c89-9882-409edc73abaa
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
auto_find_batch_size: true
base_model: unsloth/tinyllama
bf16: auto
chat_template: llama3
dataloader_num_workers: 12
dataset_prepared_path: null
datasets:
- data_files:
- 74fd83b58bc4ad47_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/74fd83b58bc4ad47_train_data.json
type:
field_input: conversation
field_instruction: note
field_output: summary
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 3
early_stopping_threshold: 0.001
eval_max_new_tokens: 128
eval_steps: 40
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: false
group_by_length: false
hub_model_id: mrferr3t/c45ed0e9-140f-4c89-9882-409edc73abaa
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0003
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 100
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
micro_batch_size: 32
mlflow_experiment_name: /tmp/74fd83b58bc4ad47_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 50
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
s2_attention: null
sample_packing: false
save_steps: 40
saves_per_epoch: 0
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 21068da4-737c-49df-9240-0bd8ff25df8b
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 21068da4-737c-49df-9240-0bd8ff25df8b
warmup_ratio: 0.05
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# c45ed0e9-140f-4c89-9882-409edc73abaa
This model is a fine-tuned version of [unsloth/tinyllama](https://huggingface.co/unsloth/tinyllama) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1196
## 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: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use adamw_bnb_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1105
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0011 | 1 | 1.2239 |
| No log | 0.0452 | 40 | 1.1594 |
| No log | 0.0904 | 80 | 0.6742 |
| 0.9748 | 0.1356 | 120 | 0.2826 |
| 0.9748 | 0.1808 | 160 | 0.2384 |
| 0.2539 | 0.2260 | 200 | 0.2086 |
| 0.2539 | 0.2712 | 240 | 0.1876 |
| 0.2539 | 0.3164 | 280 | 0.1782 |
| 0.1888 | 0.3616 | 320 | 0.1701 |
| 0.1888 | 0.4068 | 360 | 0.1643 |
| 0.1653 | 0.4520 | 400 | 0.1599 |
| 0.1653 | 0.4972 | 440 | 0.1560 |
| 0.1653 | 0.5424 | 480 | 0.1529 |
| 0.1543 | 0.5876 | 520 | 0.1507 |
| 0.1543 | 0.6328 | 560 | 0.1481 |
| 0.1496 | 0.6780 | 600 | 0.1469 |
| 0.1496 | 0.7232 | 640 | 0.1446 |
| 0.1496 | 0.7684 | 680 | 0.1431 |
| 0.145 | 0.8136 | 720 | 0.1413 |
| 0.145 | 0.8588 | 760 | 0.1410 |
| 0.1417 | 0.9040 | 800 | 0.1388 |
| 0.1417 | 0.9492 | 840 | 0.1377 |
| 0.1417 | 0.9944 | 880 | 0.1363 |
| 0.1366 | 1.0395 | 920 | 0.1358 |
| 0.1366 | 1.0847 | 960 | 0.1354 |
| 0.1314 | 1.1299 | 1000 | 0.1333 |
| 0.1314 | 1.1751 | 1040 | 0.1335 |
| 0.1314 | 1.2203 | 1080 | 0.1319 |
| 0.1305 | 1.2655 | 1120 | 0.1324 |
| 0.1305 | 1.3107 | 1160 | 0.1303 |
| 0.1264 | 1.3559 | 1200 | 0.1289 |
| 0.1264 | 1.4011 | 1240 | 0.1278 |
| 0.1264 | 1.4463 | 1280 | 0.1272 |
| 0.1244 | 1.4915 | 1320 | 0.1267 |
| 0.1244 | 1.5367 | 1360 | 0.1249 |
| 0.1224 | 1.5819 | 1400 | 0.1244 |
| 0.1224 | 1.6271 | 1440 | 0.1261 |
| 0.1224 | 1.6723 | 1480 | 0.1239 |
| 0.1239 | 1.7175 | 1520 | 0.1227 |
| 0.1239 | 1.7627 | 1560 | 0.1229 |
| 0.121 | 1.8079 | 1600 | 0.1221 |
| 0.121 | 1.8531 | 1640 | 0.1208 |
| 0.121 | 1.8983 | 1680 | 0.1205 |
| 0.1186 | 1.9435 | 1720 | 0.1194 |
| 0.1186 | 1.9887 | 1760 | 0.1189 |
| 0.1117 | 2.0339 | 1800 | 0.1192 |
| 0.1117 | 2.0791 | 1840 | 0.1192 |
| 0.1117 | 2.1243 | 1880 | 0.1196 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.3.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1
|
trenden/024a64e5-ff9a-4768-a88d-bdb811e77cdc
|
trenden
| 2025-02-04T06:36:51Z | 9 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:NousResearch/Meta-Llama-3-8B",
"base_model:adapter:NousResearch/Meta-Llama-3-8B",
"license:other",
"region:us"
] | null | 2025-02-04T06:31:06Z |
---
library_name: peft
license: other
base_model: NousResearch/Meta-Llama-3-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 024a64e5-ff9a-4768-a88d-bdb811e77cdc
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: NousResearch/Meta-Llama-3-8B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 3c7f8d22a3b05f19_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/3c7f8d22a3b05f19_train_data.json
type:
field_instruction: prompt
field_output: chosen
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: trenden/024a64e5-ff9a-4768-a88d-bdb811e77cdc
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/3c7f8d22a3b05f19_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
special_tokens:
pad_token: <|end_of_text|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: f956346c-1ba2-40a0-96e7-e24d7e19d3c3
wandb_project: Birthday-SN56-26-Gradients-On-Demand
wandb_run: your_name
wandb_runid: f956346c-1ba2-40a0-96e7-e24d7e19d3c3
warmup_steps: 5
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 024a64e5-ff9a-4768-a88d-bdb811e77cdc
This model is a fine-tuned version of [NousResearch/Meta-Llama-3-8B](https://huggingface.co/NousResearch/Meta-Llama-3-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4206
## 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0008 | 1 | 1.6095 |
| 1.4237 | 0.0400 | 50 | 1.4541 |
| 1.417 | 0.0799 | 100 | 1.4339 |
| 1.3913 | 0.1199 | 150 | 1.4234 |
| 1.298 | 0.1599 | 200 | 1.4206 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
JHJHJHJHJ/portfolio-query-qwen-2.5-7b-v5-cot-Q8_0-GGUF
|
JHJHJHJHJ
| 2025-02-04T06:35:57Z | 20 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"sft",
"llama-cpp",
"gguf-my-repo",
"en",
"base_model:Bharatdeep-H/portfolio-query-qwen-2.5-7b-v5-cot",
"base_model:quantized:Bharatdeep-H/portfolio-query-qwen-2.5-7b-v5-cot",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-02-04T06:35:21Z |
---
base_model: Bharatdeep-H/portfolio-query-qwen-2.5-7b-v5-cot
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
- llama-cpp
- gguf-my-repo
---
# JHJHJHJHJ/portfolio-query-qwen-2.5-7b-v5-cot-Q8_0-GGUF
This model was converted to GGUF format from [`Bharatdeep-H/portfolio-query-qwen-2.5-7b-v5-cot`](https://huggingface.co/Bharatdeep-H/portfolio-query-qwen-2.5-7b-v5-cot) 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/Bharatdeep-H/portfolio-query-qwen-2.5-7b-v5-cot) 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 JHJHJHJHJ/portfolio-query-qwen-2.5-7b-v5-cot-Q8_0-GGUF --hf-file portfolio-query-qwen-2.5-7b-v5-cot-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo JHJHJHJHJ/portfolio-query-qwen-2.5-7b-v5-cot-Q8_0-GGUF --hf-file portfolio-query-qwen-2.5-7b-v5-cot-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 JHJHJHJHJ/portfolio-query-qwen-2.5-7b-v5-cot-Q8_0-GGUF --hf-file portfolio-query-qwen-2.5-7b-v5-cot-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo JHJHJHJHJ/portfolio-query-qwen-2.5-7b-v5-cot-Q8_0-GGUF --hf-file portfolio-query-qwen-2.5-7b-v5-cot-q8_0.gguf -c 2048
```
|
abaddon182/33445f19-401d-4217-99ef-26383526630e
|
abaddon182
| 2025-02-04T06:34:08Z | 10 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:heegyu/WizardVicuna-open-llama-3b-v2",
"base_model:adapter:heegyu/WizardVicuna-open-llama-3b-v2",
"license:apache-2.0",
"region:us"
] | null | 2025-02-04T06:13:16Z |
---
library_name: peft
license: apache-2.0
base_model: heegyu/WizardVicuna-open-llama-3b-v2
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 33445f19-401d-4217-99ef-26383526630e
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: heegyu/WizardVicuna-open-llama-3b-v2
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 9988054a1155975c_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/9988054a1155975c_train_data.json
type:
field_input: history
field_instruction: question
field_output: answer
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 5
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: abaddon182/33445f19-401d-4217-99ef-26383526630e
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 200
micro_batch_size: 8
mlflow_experiment_name: /tmp/9988054a1155975c_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-5
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
saves_per_epoch: null
sequence_len: 1024
special_tokens:
pad_token: </s>
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 29d53164-9e6f-42ae-a37f-4cb166ed6f4f
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 29d53164-9e6f-42ae-a37f-4cb166ed6f4f
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 33445f19-401d-4217-99ef-26383526630e
This model is a fine-tuned version of [heegyu/WizardVicuna-open-llama-3b-v2](https://huggingface.co/heegyu/WizardVicuna-open-llama-3b-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6566
## 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: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.3786 | 0.0048 | 1 | 2.2118 |
| 1.8239 | 0.2418 | 50 | 1.8315 |
| 1.6825 | 0.4837 | 100 | 1.7229 |
| 1.4868 | 0.7255 | 150 | 1.6778 |
| 1.348 | 0.9674 | 200 | 1.6566 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
robiulawaldev/700444b2-b2e6-439d-b544-3c6b993cff91
|
robiulawaldev
| 2025-02-04T06:33:52Z | 9 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:codellama/CodeLlama-7b-hf",
"base_model:adapter:codellama/CodeLlama-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-02-04T06:26:05Z |
---
library_name: peft
license: llama2
base_model: codellama/CodeLlama-7b-hf
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 700444b2-b2e6-439d-b544-3c6b993cff91
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
# 700444b2-b2e6-439d-b544-3c6b993cff91
This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2921
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
daniel40/01cefc61-5f63-43a5-8027-c0aec48b4d66
|
daniel40
| 2025-02-04T06:33:49Z | 9 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:NousResearch/Meta-Llama-3-8B",
"base_model:adapter:NousResearch/Meta-Llama-3-8B",
"license:other",
"region:us"
] | null | 2025-02-04T06:28:01Z |
---
library_name: peft
license: other
base_model: NousResearch/Meta-Llama-3-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 01cefc61-5f63-43a5-8027-c0aec48b4d66
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: NousResearch/Meta-Llama-3-8B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 3c7f8d22a3b05f19_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/3c7f8d22a3b05f19_train_data.json
type:
field_instruction: prompt
field_output: chosen
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: daniel40/01cefc61-5f63-43a5-8027-c0aec48b4d66
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/3c7f8d22a3b05f19_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
special_tokens:
pad_token: <|end_of_text|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: f956346c-1ba2-40a0-96e7-e24d7e19d3c3
wandb_project: Birthday-SN56-28-Gradients-On-Demand
wandb_run: your_name
wandb_runid: f956346c-1ba2-40a0-96e7-e24d7e19d3c3
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 01cefc61-5f63-43a5-8027-c0aec48b4d66
This model is a fine-tuned version of [NousResearch/Meta-Llama-3-8B](https://huggingface.co/NousResearch/Meta-Llama-3-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4210
## 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0008 | 1 | 1.6095 |
| 1.4232 | 0.0400 | 50 | 1.4555 |
| 1.4178 | 0.0799 | 100 | 1.4342 |
| 1.3925 | 0.1199 | 150 | 1.4238 |
| 1.2979 | 0.1599 | 200 | 1.4210 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
antimage88/bba63c1a-3fe4-4c42-acfe-d986a4f5952d
|
antimage88
| 2025-02-04T06:32:15Z | 10 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:heegyu/WizardVicuna-open-llama-3b-v2",
"base_model:adapter:heegyu/WizardVicuna-open-llama-3b-v2",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-02-04T06:13:23Z |
---
library_name: peft
license: apache-2.0
base_model: heegyu/WizardVicuna-open-llama-3b-v2
tags:
- axolotl
- generated_from_trainer
model-index:
- name: bba63c1a-3fe4-4c42-acfe-d986a4f5952d
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: heegyu/WizardVicuna-open-llama-3b-v2
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 9988054a1155975c_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/9988054a1155975c_train_data.json
type:
field_input: history
field_instruction: question
field_output: answer
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: null
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: null
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: antimage88/bba63c1a-3fe4-4c42-acfe-d986a4f5952d
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 0.0001
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 200
micro_batch_size: 4
mlflow_experiment_name: /tmp/9988054a1155975c_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: null
saves_per_epoch: null
sequence_len: 1024
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 29d53164-9e6f-42ae-a37f-4cb166ed6f4f
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 29d53164-9e6f-42ae-a37f-4cb166ed6f4f
warmup_steps: 5
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# bba63c1a-3fe4-4c42-acfe-d986a4f5952d
This model is a fine-tuned version of [heegyu/WizardVicuna-open-llama-3b-v2](https://huggingface.co/heegyu/WizardVicuna-open-llama-3b-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8500
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.6772 | 0.4840 | 200 | 1.8500 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
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