modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
sequence | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
impossibleexchange/dip-.9-llama3-8b | impossibleexchange | 2024-05-14T19:16:35Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"axolotl",
"conversational",
"dataset:cognitivecomputations/Dolphin-2.9",
"dataset:teknium/OpenHermes-2.5",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"dataset:cognitivecomputations/dolphin-coder",
"dataset:cognitivecomputations/samantha-data",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:abacusai/SystemChat-1.1",
"dataset:Locutusque/function-calling-chatml",
"dataset:internlm/Agent-FLAN",
"base_model:meta-llama/Meta-Llama-3-8B",
"base_model:finetune:meta-llama/Meta-Llama-3-8B",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-14T17:42:16Z | ---
license: other
base_model: meta-llama/Meta-Llama-3-8B
tags:
- generated_from_trainer
- axolotl
model-index:
- name: out
results: []
datasets:
- cognitivecomputations/Dolphin-2.9
- teknium/OpenHermes-2.5
- m-a-p/CodeFeedback-Filtered-Instruction
- cognitivecomputations/dolphin-coder
- cognitivecomputations/samantha-data
- HuggingFaceH4/ultrachat_200k
- microsoft/orca-math-word-problems-200k
- abacusai/SystemChat-1.1
- Locutusque/function-calling-chatml
- internlm/Agent-FLAN
---
<!-- 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. -->
# Dolphin 2.9 Llama 3 8b 🐬
Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations
Discord: https://discord.gg/8fbBeC7ZGx
<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" />
A bug has been found in the Dolphin 2.9 dataset in SystemConversations that causes the model to overly talk about the "SYSTEM MESSAGE". To counter this, we recommend you add a statement in the system message directing the model not to mention the system message. An example system message is "The assistant is named Dolphin. A helpful and friendly AI assistant, Dolphin avoids discussing the system message unless directly asked about it."
My appreciation for the sponsors of Dolphin 2.9:
- [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 10xL40S node
This model is based on Llama-3-8b, and is governed by [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](LICENSE)
The base model has 8k context, and the full-weight fine-tuning was with 4k sequence length.
It took 2.5 days on 8x L40S provided by Crusoe Cloud
This model was trained FFT on all parameters, using ChatML prompt template format.
example:
```
<|im_start|>system
You are Dolphin, a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
Dolphin-2.9 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling.
Dolphin is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly.
Dolphin is licensed according to Meta's Llama license. I grant permission for any use, including commercial, that falls within accordance with Meta's Llama-3 license. Dolphin was trained on data generated from GPT4, among other models.
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: meta-llama/Meta-Llama-3-8B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false
load_in_8bit: false
load_in_4bit: false
strict: false
model_config:
datasets:
- path: /workspace/datasets/dolphin-2.9/dolphin201-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/Ultrachat200kunfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/dolphin-coder-translate-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/dolphin-coder-codegen-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/not_samantha_norefusals.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/Orca-Math-resort-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/agent_instruct_react_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_instruct_j1s1_3k_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_negative_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_react_10p_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_tflan_cot_30p_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/openhermes200k_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/SystemConversations.jsonl
type: sharegpt
conversation: chatml
chat_template: chatml
dataset_prepared_path: /workspace/datasets/dolphin-2.9/thingy
val_set_size: 0.0002
output_dir: ./out
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
gradient_accumulation_steps: 4
micro_batch_size: 3
num_epochs: 3
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5
wandb_project: dolphin-2.9-mixtral-8x22b
wandb_watch:
wandb_run_id:
wandb_log_model:
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:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 4
save_total_limit: 2
save_steps:
evals_per_epoch: 4
eval_sample_packing: false
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
weight_decay: 0.05
fsdp:
fsdp_config:
special_tokens:
eos_token: "<|im_end|>"
pad_token: "<|end_of_text|>"
tokens:
- "<|im_start|>"
- "<|im_end|>"
```
</details><br>
## Quants
GGUF : https://huggingface.co/QuantFactory/dolphin-2.9-llama3-8b-GGUF
GGUF with imatrix: https://huggingface.co/bartowski/dolphin-2.9-llama3-8b-GGUF
Exllamav2: https://huggingface.co/bartowski/dolphin-2.9-llama3-8b-exl2
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 96
- total_eval_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 7
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.146 | 0.0005 | 1 | 1.1064 |
| 0.6962 | 0.2501 | 555 | 0.6636 |
| 0.6857 | 0.5001 | 1110 | 0.6503 |
| 0.6592 | 0.7502 | 1665 | 0.6419 |
| 0.6465 | 1.0002 | 2220 | 0.6317 |
| 0.5295 | 1.2395 | 2775 | 0.6408 |
| 0.5302 | 1.4895 | 3330 | 0.6351 |
| 0.5188 | 1.7396 | 3885 | 0.6227 |
| 0.521 | 1.9896 | 4440 | 0.6168 |
| 0.3968 | 2.2289 | 4995 | 0.6646 |
| 0.3776 | 2.4789 | 5550 | 0.6619 |
| 0.3983 | 2.7290 | 6105 | 0.6602 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1 |
lomahony/pythia-1b-helpful-dpo | lomahony | 2024-05-14T19:16:08Z | 17 | 0 | transformers | [
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"causal-lm",
"pythia",
"en",
"dataset:Anthropic/hh-rlhf",
"arxiv:2101.00027",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-12T02:33:28Z | ---
language:
- en
tags:
- pytorch
- causal-lm
- pythia
license: apache-2.0
datasets:
- Anthropic/hh-rlhf
---
[Pythia-1b](https://huggingface.co/EleutherAI/pythia-1b) DPO finetuned using original DPO code with the helpful subset of [Anthropic-hh-rlhf dataset](https://huggingface.co/datasets/Anthropic/hh-rlhf) for 1 epoch.
Checkpoints are also uploaded.
Fully reproducible finetuning code is available on [GitHub](https://github.com/lauraaisling/direct-preference-optimization/tree/main)
[wandb log](https://wandb.ai/lauraomahony999/pythia-dpo/runs/0mhjakjz)
See [Pythia-1b](https://huggingface.co/EleutherAI/pythia-1b) for model details [(paper)](https://arxiv.org/abs/2101.00027).
See further details of these models in the paper [Attributing Mode Collapse in the Fine-Tuning of Large Language Models](https://openreview.net/pdf?id=3pDMYjpOxk).
You can cite these models if they are helpful as follows:
<pre>
@inproceedings{o2024attributing,
title={Attributing Mode Collapse in the Fine-Tuning of Large Language Models},
author={O’Mahony, Laura and Grinsztajn, Leo and Schoelkopf, Hailey and Biderman, Stella},
booktitle={ICLR 2024, Mathematical and Empirical Understanding of Foundation Models (ME-FoMo) workshop},
year={2024}
}
</pre>
hf (pretrained=lomahony/pythia-1b-helpful-dpo), gen_kwargs: (None), limit: None, num_fewshot: 0, batch_size: 16
| Tasks |Version|Filter|n-shot| Metric | Value | |Stderr|
|--------------|------:|------|-----:|---------------|------:|---|------|
|arc_challenge | 1|none | 0|acc | 0.2602|± |0.0128|
| | |none | 0|acc_norm | 0.2867|± |0.0132|
|arc_easy | 1|none | 0|acc | 0.5859|± |0.0101|
| | |none | 0|acc_norm | 0.5008|± |0.0103|
|boolq | 2|none | 0|acc | 0.6205|± |0.0085|
|hellaswag | 1|none | 0|acc | 0.3895|± |0.0049|
| | |none | 0|acc_norm | 0.4872|± |0.0050|
|lambada_openai| 1|none | 0|perplexity | 6.9417|± |0.2019|
| | |none | 0|acc | 0.5550|± |0.0069|
|openbookqa | 1|none | 0|acc | 0.2140|± |0.0184|
| | |none | 0|acc_norm | 0.3220|± |0.0209|
|piqa | 1|none | 0|acc | 0.7193|± |0.0105|
| | |none | 0|acc_norm | 0.7008|± |0.0107|
|sciq | 1|none | 0|acc | 0.8450|± |0.0115|
| | |none | 0|acc_norm | 0.7600|± |0.0135|
|wikitext | 2|none | 0|word_perplexity|17.2316|± |N/A |
| | |none | 0|byte_perplexity| 1.7029|± |N/A |
| | |none | 0|bits_per_byte | 0.7680|± |N/A |
|winogrande | 1|none | 0|acc | 0.5367|± |0.0140|
hf (pretrained=lomahony/pythia-1b-helpful-dpo), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: 16
| Tasks |Version|Filter|n-shot| Metric | Value | |Stderr|
|--------------|------:|------|-----:|---------------|------:|---|------|
|arc_challenge | 1|none | 5|acc | 0.2662|± |0.0129|
| | |none | 5|acc_norm | 0.3003|± |0.0134|
|arc_easy | 1|none | 5|acc | 0.6103|± |0.0100|
| | |none | 5|acc_norm | 0.5892|± |0.0101|
|boolq | 2|none | 5|acc | 0.6284|± |0.0085|
|hellaswag | 1|none | 5|acc | 0.3841|± |0.0049|
| | |none | 5|acc_norm | 0.4845|± |0.0050|
|lambada_openai| 1|none | 5|perplexity | 9.6301|± |0.2809|
| | |none | 5|acc | 0.4865|± |0.0070|
|openbookqa | 1|none | 5|acc | 0.2020|± |0.0180|
| | |none | 5|acc_norm | 0.3300|± |0.0210|
|piqa | 1|none | 5|acc | 0.7122|± |0.0106|
| | |none | 5|acc_norm | 0.7046|± |0.0106|
|sciq | 1|none | 5|acc | 0.9030|± |0.0094|
| | |none | 5|acc_norm | 0.8980|± |0.0096|
|wikitext | 2|none | 5|word_perplexity|17.2316|± |N/A |
| | |none | 5|byte_perplexity| 1.7029|± |N/A |
| | |none | 5|bits_per_byte | 0.7680|± |N/A |
|winogrande | 1|none | 5|acc | 0.5296|± |0.0140|
|
newsletter/rocket-3B-Q6_K-GGUF | newsletter | 2024-05-14T19:15:43Z | 1 | 0 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"base_model:stabilityai/stablelm-3b-4e1t",
"base_model:quantized:stabilityai/stablelm-3b-4e1t",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-14T19:15:36Z | ---
language:
- en
license: cc-by-sa-4.0
tags:
- llama-cpp
- gguf-my-repo
base_model: stabilityai/stablelm-3b-4e1t
model-index:
- name: rocket-3b
results: []
---
# newsletter/rocket-3B-Q6_K-GGUF
This model was converted to GGUF format from [`pansophic/rocket-3B`](https://huggingface.co/pansophic/rocket-3B) 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/pansophic/rocket-3B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo newsletter/rocket-3B-Q6_K-GGUF --model rocket-3b.Q6_K.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo newsletter/rocket-3B-Q6_K-GGUF --model rocket-3b.Q6_K.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m rocket-3b.Q6_K.gguf -n 128
```
|
lomahony/pythia-160m-helpful-dpo | lomahony | 2024-05-14T19:15:34Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"causal-lm",
"pythia",
"en",
"dataset:Anthropic/hh-rlhf",
"arxiv:2101.00027",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-12T15:27:50Z | ---
language:
- en
tags:
- pytorch
- causal-lm
- pythia
license: apache-2.0
datasets:
- Anthropic/hh-rlhf
---
[Pythia-160m](https://huggingface.co/EleutherAI/pythia-160m) finetuned using original DPO code with the helpful subset of [Anthropic-hh-rlhf dataset](https://huggingface.co/datasets/Anthropic/hh-rlhf) for 1 epoch.
Checkpoints are also uploaded.
Fully reproducible finetuning code is available on [GitHub](https://github.com/lauraaisling/direct-preference-optimization/tree/main)
[wandb log](https://wandb.ai/lauraomahony999/pythia-dpo/runs/3djpa41v)
See [Pythia-160m](https://huggingface.co/EleutherAI/pythia-160m) for model details [(paper)](https://arxiv.org/abs/2101.00027).
See further details of these models in the paper [Attributing Mode Collapse in the Fine-Tuning of Large Language Models](https://openreview.net/pdf?id=3pDMYjpOxk).
You can cite these models if they are helpful as follows:
<pre>
@inproceedings{o2024attributing,
title={Attributing Mode Collapse in the Fine-Tuning of Large Language Models},
author={O’Mahony, Laura and Grinsztajn, Leo and Schoelkopf, Hailey and Biderman, Stella},
booktitle={ICLR 2024, Mathematical and Empirical Understanding of Foundation Models (ME-FoMo) workshop},
year={2024}
}
</pre>
hf (pretrained=lomahony/pythia-160m-helpful-dpo), gen_kwargs: (None), limit: None, num_fewshot: 0, batch_size: 16
| Tasks |Version|Filter|n-shot| Metric | Value | |Stderr |
|--------------|------:|------|-----:|---------------|-------:|---|-------|
|arc_challenge | 1|none | 0|acc | 0.2125|± | 0.0120|
| | |none | 0|acc_norm | 0.2312|± | 0.0123|
|arc_easy | 1|none | 0|acc | 0.3965|± | 0.0100|
| | |none | 0|acc_norm | 0.3830|± | 0.0100|
|boolq | 2|none | 0|acc | 0.5853|± | 0.0086|
|hellaswag | 1|none | 0|acc | 0.2811|± | 0.0045|
| | |none | 0|acc_norm | 0.2940|± | 0.0045|
|lambada_openai| 1|none | 0|perplexity |444.4464|± |24.5439|
| | |none | 0|acc | 0.1034|± | 0.0042|
|openbookqa | 1|none | 0|acc | 0.1500|± | 0.0160|
| | |none | 0|acc_norm | 0.2480|± | 0.0193|
|piqa | 1|none | 0|acc | 0.5947|± | 0.0115|
| | |none | 0|acc_norm | 0.5876|± | 0.0115|
|sciq | 1|none | 0|acc | 0.5880|± | 0.0156|
| | |none | 0|acc_norm | 0.6180|± | 0.0154|
|wikitext | 2|none | 0|word_perplexity| 88.8633|± |N/A |
| | |none | 0|byte_perplexity| 2.3143|± |N/A |
| | |none | 0|bits_per_byte | 1.2106|± |N/A |
|winogrande | 1|none | 0|acc | 0.4980|± | 0.0141|
hf (pretrained=lomahony/pythia-160m-helpful-dpo), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: 16
| Tasks |Version|Filter|n-shot| Metric | Value | |Stderr |
|--------------|------:|------|-----:|---------------|--------:|---|-------|
|arc_challenge | 1|none | 5|acc | 0.1928|± | 0.0115|
| | |none | 5|acc_norm | 0.2398|± | 0.0125|
|arc_easy | 1|none | 5|acc | 0.3678|± | 0.0099|
| | |none | 5|acc_norm | 0.3657|± | 0.0099|
|boolq | 2|none | 5|acc | 0.5841|± | 0.0086|
|hellaswag | 1|none | 5|acc | 0.2807|± | 0.0045|
| | |none | 5|acc_norm | 0.2876|± | 0.0045|
|lambada_openai| 1|none | 5|perplexity |1607.2529|± |88.3065|
| | |none | 5|acc | 0.0574|± | 0.0032|
|openbookqa | 1|none | 5|acc | 0.1580|± | 0.0163|
| | |none | 5|acc_norm | 0.2400|± | 0.0191|
|piqa | 1|none | 5|acc | 0.5958|± | 0.0114|
| | |none | 5|acc_norm | 0.5773|± | 0.0115|
|sciq | 1|none | 5|acc | 0.5110|± | 0.0158|
| | |none | 5|acc_norm | 0.5740|± | 0.0156|
|wikitext | 2|none | 5|word_perplexity| 88.8633|± |N/A |
| | |none | 5|byte_perplexity| 2.3143|± |N/A |
| | |none | 5|bits_per_byte | 1.2106|± |N/A |
|winogrande | 1|none | 5|acc | 0.5162|± | 0.0140|
|
mradermacher/Dark-Miqu-70B-GGUF | mradermacher | 2024-05-14T19:13:34Z | 88 | 1 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:jukofyork/Dark-Miqu-70B",
"base_model:quantized:jukofyork/Dark-Miqu-70B",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-05-05T13:27:42Z | ---
base_model: jukofyork/Dark-Miqu-70B
language:
- en
library_name: transformers
license: other
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/jukofyork/Dark-Miqu-70B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Dark-Miqu-70B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Dark-Miqu-70B-GGUF/resolve/main/Dark-Miqu-70B.Q2_K.gguf) | Q2_K | 25.6 | |
| [GGUF](https://huggingface.co/mradermacher/Dark-Miqu-70B-GGUF/resolve/main/Dark-Miqu-70B.IQ3_XS.gguf) | IQ3_XS | 28.4 | |
| [GGUF](https://huggingface.co/mradermacher/Dark-Miqu-70B-GGUF/resolve/main/Dark-Miqu-70B.IQ3_S.gguf) | IQ3_S | 30.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Dark-Miqu-70B-GGUF/resolve/main/Dark-Miqu-70B.Q3_K_S.gguf) | Q3_K_S | 30.0 | |
| [GGUF](https://huggingface.co/mradermacher/Dark-Miqu-70B-GGUF/resolve/main/Dark-Miqu-70B.IQ3_M.gguf) | IQ3_M | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/Dark-Miqu-70B-GGUF/resolve/main/Dark-Miqu-70B.Q3_K_M.gguf) | Q3_K_M | 33.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Dark-Miqu-70B-GGUF/resolve/main/Dark-Miqu-70B.Q3_K_L.gguf) | Q3_K_L | 36.2 | |
| [GGUF](https://huggingface.co/mradermacher/Dark-Miqu-70B-GGUF/resolve/main/Dark-Miqu-70B.IQ4_XS.gguf) | IQ4_XS | 37.3 | |
| [GGUF](https://huggingface.co/mradermacher/Dark-Miqu-70B-GGUF/resolve/main/Dark-Miqu-70B.Q4_K_S.gguf) | Q4_K_S | 39.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Dark-Miqu-70B-GGUF/resolve/main/Dark-Miqu-70B.Q4_K_M.gguf) | Q4_K_M | 41.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Dark-Miqu-70B-GGUF/resolve/main/Dark-Miqu-70B.Q5_K_S.gguf) | Q5_K_S | 47.6 | |
| [GGUF](https://huggingface.co/mradermacher/Dark-Miqu-70B-GGUF/resolve/main/Dark-Miqu-70B.Q5_K_M.gguf) | Q5_K_M | 48.9 | |
| [PART 1](https://huggingface.co/mradermacher/Dark-Miqu-70B-GGUF/resolve/main/Dark-Miqu-70B.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Dark-Miqu-70B-GGUF/resolve/main/Dark-Miqu-70B.Q6_K.gguf.part2of2) | Q6_K | 56.7 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Dark-Miqu-70B-GGUF/resolve/main/Dark-Miqu-70B.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Dark-Miqu-70B-GGUF/resolve/main/Dark-Miqu-70B.Q8_0.gguf.part2of2) | Q8_0 | 73.4 | 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 -->
|
mradermacher/Dark-Miqu-70B-i1-GGUF | mradermacher | 2024-05-14T19:13:26Z | 224 | 4 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:jukofyork/Dark-Miqu-70B",
"base_model:quantized:jukofyork/Dark-Miqu-70B",
"license:other",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2024-05-05T18:52:11Z | ---
base_model: jukofyork/Dark-Miqu-70B
language:
- en
library_name: transformers
license: other
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/jukofyork/Dark-Miqu-70B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Dark-Miqu-70B-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/Dark-Miqu-70B-i1-GGUF/resolve/main/Dark-Miqu-70B.i1-IQ1_S.gguf) | i1-IQ1_S | 14.6 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Dark-Miqu-70B-i1-GGUF/resolve/main/Dark-Miqu-70B.i1-IQ1_M.gguf) | i1-IQ1_M | 16.0 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Dark-Miqu-70B-i1-GGUF/resolve/main/Dark-Miqu-70B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 18.4 | |
| [GGUF](https://huggingface.co/mradermacher/Dark-Miqu-70B-i1-GGUF/resolve/main/Dark-Miqu-70B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 20.4 | |
| [GGUF](https://huggingface.co/mradermacher/Dark-Miqu-70B-i1-GGUF/resolve/main/Dark-Miqu-70B.i1-IQ2_S.gguf) | i1-IQ2_S | 21.5 | |
| [GGUF](https://huggingface.co/mradermacher/Dark-Miqu-70B-i1-GGUF/resolve/main/Dark-Miqu-70B.i1-IQ2_M.gguf) | i1-IQ2_M | 23.3 | |
| [GGUF](https://huggingface.co/mradermacher/Dark-Miqu-70B-i1-GGUF/resolve/main/Dark-Miqu-70B.i1-Q2_K.gguf) | i1-Q2_K | 25.6 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Dark-Miqu-70B-i1-GGUF/resolve/main/Dark-Miqu-70B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 26.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Dark-Miqu-70B-i1-GGUF/resolve/main/Dark-Miqu-70B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 28.4 | |
| [GGUF](https://huggingface.co/mradermacher/Dark-Miqu-70B-i1-GGUF/resolve/main/Dark-Miqu-70B.i1-IQ3_S.gguf) | i1-IQ3_S | 30.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Dark-Miqu-70B-i1-GGUF/resolve/main/Dark-Miqu-70B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 30.0 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Dark-Miqu-70B-i1-GGUF/resolve/main/Dark-Miqu-70B.i1-IQ3_M.gguf) | i1-IQ3_M | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/Dark-Miqu-70B-i1-GGUF/resolve/main/Dark-Miqu-70B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 33.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Dark-Miqu-70B-i1-GGUF/resolve/main/Dark-Miqu-70B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 36.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Dark-Miqu-70B-i1-GGUF/resolve/main/Dark-Miqu-70B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 36.9 | |
| [GGUF](https://huggingface.co/mradermacher/Dark-Miqu-70B-i1-GGUF/resolve/main/Dark-Miqu-70B.i1-Q4_0.gguf) | i1-Q4_0 | 39.1 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Dark-Miqu-70B-i1-GGUF/resolve/main/Dark-Miqu-70B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 39.3 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Dark-Miqu-70B-i1-GGUF/resolve/main/Dark-Miqu-70B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 41.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Dark-Miqu-70B-i1-GGUF/resolve/main/Dark-Miqu-70B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 47.6 | |
| [GGUF](https://huggingface.co/mradermacher/Dark-Miqu-70B-i1-GGUF/resolve/main/Dark-Miqu-70B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 48.9 | |
| [PART 1](https://huggingface.co/mradermacher/Dark-Miqu-70B-i1-GGUF/resolve/main/Dark-Miqu-70B.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Dark-Miqu-70B-i1-GGUF/resolve/main/Dark-Miqu-70B.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 56.7 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
AlkQ/ppo-PyramidsRND | AlkQ | 2024-05-14T19:13:25Z | 5 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | 2024-05-14T17:29:37Z | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: AlkQ/ppo-PyramidsRND
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
lomahony/pythia-2.8b-helpful-sft | lomahony | 2024-05-14T19:11:57Z | 107 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"gpt_neox",
"text-generation",
"causal-lm",
"pythia",
"en",
"dataset:Anthropic/hh-rlhf",
"arxiv:2101.00027",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-11-08T15:50:41Z | ---
language:
- en
tags:
- pytorch
- causal-lm
- pythia
license: apache-2.0
datasets:
- Anthropic/hh-rlhf
---
[Pythia-2.8b](https://huggingface.co/EleutherAI/pythia-410m) supervised finetuned using TRLx library with the helpful subset of [Anthropic-hh-rlhf dataset](https://huggingface.co/datasets/Anthropic/hh-rlhf) for 1 epoch.
Checkpoints are also uploaded.
Fully reproducible finetuning code is available on [GitHub](https://github.com/lauraaisling/trlx-pythia/tree/main)
[wandb log](https://wandb.ai/lauraomahony999/pythia-sft/runs/3b0ltx73)
See [Pythia-2.8b](https://huggingface.co/EleutherAI/pythia-2.8b) for model details [(paper)](https://arxiv.org/abs/2101.00027).
See further details of these models in the paper [Attributing Mode Collapse in the Fine-Tuning of Large Language Models](https://openreview.net/pdf?id=3pDMYjpOxk).
You can cite these models if they are helpful as follows:
<pre>
@inproceedings{o2024attributing,
title={Attributing Mode Collapse in the Fine-Tuning of Large Language Models},
author={O’Mahony, Laura and Grinsztajn, Leo and Schoelkopf, Hailey and Biderman, Stella},
booktitle={ICLR 2024, Mathematical and Empirical Understanding of Foundation Models (ME-FoMo) workshop},
year={2024}
}
</pre>
hf (pretrained=lomahony/pythia-2.8b-helpful-sft), gen_kwargs: (None), limit: None, num_fewshot: 0, batch_size: 16
| Tasks |Version|Filter|n-shot| Metric | Value | |Stderr|
|--------------|------:|------|-----:|---------------|------:|---|------|
|arc_challenge | 1|none | 0|acc | 0.2901|± |0.0133|
| | |none | 0|acc_norm | 0.3404|± |0.0138|
|arc_easy | 1|none | 0|acc | 0.6469|± |0.0098|
| | |none | 0|acc_norm | 0.5766|± |0.0101|
|boolq | 2|none | 0|acc | 0.6361|± |0.0084|
|hellaswag | 1|none | 0|acc | 0.4557|± |0.0050|
| | |none | 0|acc_norm | 0.5984|± |0.0049|
|lambada_openai| 1|none | 0|perplexity | 5.2226|± |0.1377|
| | |none | 0|acc | 0.6210|± |0.0068|
|openbookqa | 1|none | 0|acc | 0.2640|± |0.0197|
| | |none | 0|acc_norm | 0.3760|± |0.0217|
|piqa | 1|none | 0|acc | 0.7481|± |0.0101|
| | |none | 0|acc_norm | 0.7481|± |0.0101|
|sciq | 1|none | 0|acc | 0.8800|± |0.0103|
| | |none | 0|acc_norm | 0.8180|± |0.0122|
|wikitext | 2|none | 0|word_perplexity|13.4928|± |N/A |
| | |none | 0|byte_perplexity| 1.6268|± |N/A |
| | |none | 0|bits_per_byte | 0.7020|± |N/A |
|winogrande | 1|none | 0|acc | 0.6125|± |0.0137|
hf (pretrained=lomahony/pythia-2.8b-helpful-sft), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: 16
| Tasks |Version|Filter|n-shot| Metric | Value | |Stderr|
|--------------|------:|------|-----:|---------------|------:|---|------|
|arc_challenge | 1|none | 5|acc | 0.3285|± |0.0137|
| | |none | 5|acc_norm | 0.3677|± |0.0141|
|arc_easy | 1|none | 5|acc | 0.6873|± |0.0095|
| | |none | 5|acc_norm | 0.6835|± |0.0095|
|boolq | 2|none | 5|acc | 0.6670|± |0.0082|
|hellaswag | 1|none | 5|acc | 0.4542|± |0.0050|
| | |none | 5|acc_norm | 0.5963|± |0.0049|
|lambada_openai| 1|none | 5|perplexity | 7.4076|± |0.2095|
| | |none | 5|acc | 0.5486|± |0.0069|
|openbookqa | 1|none | 5|acc | 0.2680|± |0.0198|
| | |none | 5|acc_norm | 0.3620|± |0.0215|
|piqa | 1|none | 5|acc | 0.7568|± |0.0100|
| | |none | 5|acc_norm | 0.7486|± |0.0101|
|sciq | 1|none | 5|acc | 0.9380|± |0.0076|
| | |none | 5|acc_norm | 0.9330|± |0.0079|
|wikitext | 2|none | 5|word_perplexity|13.4928|± |N/A |
| | |none | 5|byte_perplexity| 1.6268|± |N/A |
| | |none | 5|bits_per_byte | 0.7020|± |N/A |
|winogrande | 1|none | 5|acc | 0.5935|± |0.0138|
|
mradermacher/Exodius-70B-i1-GGUF | mradermacher | 2024-05-14T19:10:12Z | 77 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:Syed-Hasan-8503/Exodius-70B",
"base_model:quantized:Syed-Hasan-8503/Exodius-70B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2024-05-11T20:52:09Z | ---
base_model: Syed-Hasan-8503/Exodius-70B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/Syed-Hasan-8503/Exodius-70B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Exodius-70B-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/Exodius-70B-i1-GGUF/resolve/main/Exodius-70B.i1-IQ1_S.gguf) | i1-IQ1_S | 16.3 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Exodius-70B-i1-GGUF/resolve/main/Exodius-70B.i1-IQ1_M.gguf) | i1-IQ1_M | 17.7 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Exodius-70B-i1-GGUF/resolve/main/Exodius-70B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 19.9 | |
| [GGUF](https://huggingface.co/mradermacher/Exodius-70B-i1-GGUF/resolve/main/Exodius-70B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 21.8 | |
| [GGUF](https://huggingface.co/mradermacher/Exodius-70B-i1-GGUF/resolve/main/Exodius-70B.i1-IQ2_S.gguf) | i1-IQ2_S | 23.5 | |
| [GGUF](https://huggingface.co/mradermacher/Exodius-70B-i1-GGUF/resolve/main/Exodius-70B.i1-IQ2_M.gguf) | i1-IQ2_M | 25.3 | |
| [GGUF](https://huggingface.co/mradermacher/Exodius-70B-i1-GGUF/resolve/main/Exodius-70B.i1-Q2_K.gguf) | i1-Q2_K | 27.2 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Exodius-70B-i1-GGUF/resolve/main/Exodius-70B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 27.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Exodius-70B-i1-GGUF/resolve/main/Exodius-70B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 30.0 | |
| [GGUF](https://huggingface.co/mradermacher/Exodius-70B-i1-GGUF/resolve/main/Exodius-70B.i1-IQ3_S.gguf) | i1-IQ3_S | 31.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Exodius-70B-i1-GGUF/resolve/main/Exodius-70B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 31.7 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Exodius-70B-i1-GGUF/resolve/main/Exodius-70B.i1-IQ3_M.gguf) | i1-IQ3_M | 33.4 | |
| [GGUF](https://huggingface.co/mradermacher/Exodius-70B-i1-GGUF/resolve/main/Exodius-70B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 35.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Exodius-70B-i1-GGUF/resolve/main/Exodius-70B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 38.6 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Exodius-70B-i1-GGUF/resolve/main/Exodius-70B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 38.9 | |
| [GGUF](https://huggingface.co/mradermacher/Exodius-70B-i1-GGUF/resolve/main/Exodius-70B.i1-Q4_0.gguf) | i1-Q4_0 | 41.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Exodius-70B-i1-GGUF/resolve/main/Exodius-70B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 41.4 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Exodius-70B-i1-GGUF/resolve/main/Exodius-70B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 43.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Exodius-70B-i1-GGUF/resolve/main/Exodius-70B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 50.0 | |
| [PART 1](https://huggingface.co/mradermacher/Exodius-70B-i1-GGUF/resolve/main/Exodius-70B.i1-Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Exodius-70B-i1-GGUF/resolve/main/Exodius-70B.i1-Q5_K_M.gguf.part2of2) | i1-Q5_K_M | 51.4 | |
| [PART 1](https://huggingface.co/mradermacher/Exodius-70B-i1-GGUF/resolve/main/Exodius-70B.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Exodius-70B-i1-GGUF/resolve/main/Exodius-70B.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 59.4 | 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.
<!-- end -->
|
QinLiuNLP/mistral-10p | QinLiuNLP | 2024-05-14T19:10:10Z | 1 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"mistral",
"alignment-handbook",
"trl",
"sft",
"generated_from_trainer",
"dataset:HuggingFaceH4/ultrachat_200k",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2024-05-14T14:31:36Z | ---
license: apache-2.0
library_name: peft
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
base_model: mistralai/Mistral-7B-v0.1
datasets:
- HuggingFaceH4/ultrachat_200k
model-index:
- name: mistral-10p
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. -->
# mistral-10p
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the HuggingFaceH4/ultrachat_200k dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7437
## 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: 8
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7469 | 1.0 | 974 | 0.7437 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.39.0.dev0
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.2 |
mradermacher/mistral-orthogonalized-GGUF | mradermacher | 2024-05-14T19:09:56Z | 54 | 1 | transformers | [
"transformers",
"gguf",
"mistral ",
"en",
"base_model:cosmicvalor/mistral-orthogonalized",
"base_model:quantized:cosmicvalor/mistral-orthogonalized",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-12T18:45:42Z | ---
base_model: cosmicvalor/mistral-orthogonalized
language:
- en
library_name: transformers
license: apache-2.0
no_imatrix: nan
quantized_by: mradermacher
tags:
- 'mistral '
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/cosmicvalor/mistral-orthogonalized
<!-- provided-files -->
## 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/mistral-orthogonalized-GGUF/resolve/main/mistral-orthogonalized.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/mistral-orthogonalized-GGUF/resolve/main/mistral-orthogonalized.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/mistral-orthogonalized-GGUF/resolve/main/mistral-orthogonalized.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/mistral-orthogonalized-GGUF/resolve/main/mistral-orthogonalized.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/mistral-orthogonalized-GGUF/resolve/main/mistral-orthogonalized.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/mistral-orthogonalized-GGUF/resolve/main/mistral-orthogonalized.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/mistral-orthogonalized-GGUF/resolve/main/mistral-orthogonalized.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/mistral-orthogonalized-GGUF/resolve/main/mistral-orthogonalized.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/mistral-orthogonalized-GGUF/resolve/main/mistral-orthogonalized.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/mistral-orthogonalized-GGUF/resolve/main/mistral-orthogonalized.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/mistral-orthogonalized-GGUF/resolve/main/mistral-orthogonalized.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/mistral-orthogonalized-GGUF/resolve/main/mistral-orthogonalized.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/mistral-orthogonalized-GGUF/resolve/main/mistral-orthogonalized.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/mistral-orthogonalized-GGUF/resolve/main/mistral-orthogonalized.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/mistral-orthogonalized-GGUF/resolve/main/mistral-orthogonalized.f16.gguf) | f16 | 14.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/Rummage-8B-i1-GGUF | mradermacher | 2024-05-14T19:09:52Z | 24 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:lodrick-the-lafted/Rummage-8B",
"base_model:quantized:lodrick-the-lafted/Rummage-8B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-05-12T18:52:42Z | ---
base_model: lodrick-the-lafted/Rummage-8B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hfhfix -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/lodrick-the-lafted/Rummage-8B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Rummage-8B-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Rummage-8B-i1-GGUF/resolve/main/Rummage-8B.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Rummage-8B-i1-GGUF/resolve/main/Rummage-8B.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Rummage-8B-i1-GGUF/resolve/main/Rummage-8B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/Rummage-8B-i1-GGUF/resolve/main/Rummage-8B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Rummage-8B-i1-GGUF/resolve/main/Rummage-8B.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Rummage-8B-i1-GGUF/resolve/main/Rummage-8B.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Rummage-8B-i1-GGUF/resolve/main/Rummage-8B.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Rummage-8B-i1-GGUF/resolve/main/Rummage-8B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Rummage-8B-i1-GGUF/resolve/main/Rummage-8B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Rummage-8B-i1-GGUF/resolve/main/Rummage-8B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Rummage-8B-i1-GGUF/resolve/main/Rummage-8B.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Rummage-8B-i1-GGUF/resolve/main/Rummage-8B.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Rummage-8B-i1-GGUF/resolve/main/Rummage-8B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Rummage-8B-i1-GGUF/resolve/main/Rummage-8B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Rummage-8B-i1-GGUF/resolve/main/Rummage-8B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Rummage-8B-i1-GGUF/resolve/main/Rummage-8B.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Rummage-8B-i1-GGUF/resolve/main/Rummage-8B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Rummage-8B-i1-GGUF/resolve/main/Rummage-8B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Rummage-8B-i1-GGUF/resolve/main/Rummage-8B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Rummage-8B-i1-GGUF/resolve/main/Rummage-8B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Rummage-8B-i1-GGUF/resolve/main/Rummage-8B.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/Llama4Some-SOVL-4x8B-L3-V1-i1-GGUF | mradermacher | 2024-05-14T19:09:43Z | 82 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:saishf/Llama4Some-SOVL-4x8B-L3-V1",
"base_model:quantized:saishf/Llama4Some-SOVL-4x8B-L3-V1",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-05-12T19:10:40Z | ---
base_model: saishf/Llama4Some-SOVL-4x8B-L3-V1
language:
- en
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hfhfix -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/saishf/Llama4Some-SOVL-4x8B-L3-V1
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Llama4Some-SOVL-4x8B-L3-V1-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/Llama4Some-SOVL-4x8B-L3-V1-i1-GGUF/resolve/main/Llama4Some-SOVL-4x8B-L3-V1.i1-IQ1_S.gguf) | i1-IQ1_S | 5.5 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Llama4Some-SOVL-4x8B-L3-V1-i1-GGUF/resolve/main/Llama4Some-SOVL-4x8B-L3-V1.i1-IQ1_M.gguf) | i1-IQ1_M | 6.0 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Llama4Some-SOVL-4x8B-L3-V1-i1-GGUF/resolve/main/Llama4Some-SOVL-4x8B-L3-V1.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 6.9 | |
| [GGUF](https://huggingface.co/mradermacher/Llama4Some-SOVL-4x8B-L3-V1-i1-GGUF/resolve/main/Llama4Some-SOVL-4x8B-L3-V1.i1-IQ2_XS.gguf) | i1-IQ2_XS | 7.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama4Some-SOVL-4x8B-L3-V1-i1-GGUF/resolve/main/Llama4Some-SOVL-4x8B-L3-V1.i1-IQ2_S.gguf) | i1-IQ2_S | 7.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama4Some-SOVL-4x8B-L3-V1-i1-GGUF/resolve/main/Llama4Some-SOVL-4x8B-L3-V1.i1-IQ2_M.gguf) | i1-IQ2_M | 8.5 | |
| [GGUF](https://huggingface.co/mradermacher/Llama4Some-SOVL-4x8B-L3-V1-i1-GGUF/resolve/main/Llama4Some-SOVL-4x8B-L3-V1.i1-Q2_K.gguf) | i1-Q2_K | 9.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama4Some-SOVL-4x8B-L3-V1-i1-GGUF/resolve/main/Llama4Some-SOVL-4x8B-L3-V1.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 9.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama4Some-SOVL-4x8B-L3-V1-i1-GGUF/resolve/main/Llama4Some-SOVL-4x8B-L3-V1.i1-IQ3_XS.gguf) | i1-IQ3_XS | 10.5 | |
| [GGUF](https://huggingface.co/mradermacher/Llama4Some-SOVL-4x8B-L3-V1-i1-GGUF/resolve/main/Llama4Some-SOVL-4x8B-L3-V1.i1-Q3_K_S.gguf) | i1-Q3_K_S | 11.0 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama4Some-SOVL-4x8B-L3-V1-i1-GGUF/resolve/main/Llama4Some-SOVL-4x8B-L3-V1.i1-IQ3_S.gguf) | i1-IQ3_S | 11.1 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Llama4Some-SOVL-4x8B-L3-V1-i1-GGUF/resolve/main/Llama4Some-SOVL-4x8B-L3-V1.i1-IQ3_M.gguf) | i1-IQ3_M | 11.2 | |
| [GGUF](https://huggingface.co/mradermacher/Llama4Some-SOVL-4x8B-L3-V1-i1-GGUF/resolve/main/Llama4Some-SOVL-4x8B-L3-V1.i1-Q3_K_M.gguf) | i1-Q3_K_M | 12.2 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama4Some-SOVL-4x8B-L3-V1-i1-GGUF/resolve/main/Llama4Some-SOVL-4x8B-L3-V1.i1-Q3_K_L.gguf) | i1-Q3_K_L | 13.1 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama4Some-SOVL-4x8B-L3-V1-i1-GGUF/resolve/main/Llama4Some-SOVL-4x8B-L3-V1.i1-IQ4_XS.gguf) | i1-IQ4_XS | 13.5 | |
| [GGUF](https://huggingface.co/mradermacher/Llama4Some-SOVL-4x8B-L3-V1-i1-GGUF/resolve/main/Llama4Some-SOVL-4x8B-L3-V1.i1-Q4_0.gguf) | i1-Q4_0 | 14.3 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Llama4Some-SOVL-4x8B-L3-V1-i1-GGUF/resolve/main/Llama4Some-SOVL-4x8B-L3-V1.i1-Q4_K_S.gguf) | i1-Q4_K_S | 14.4 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Llama4Some-SOVL-4x8B-L3-V1-i1-GGUF/resolve/main/Llama4Some-SOVL-4x8B-L3-V1.i1-Q4_K_M.gguf) | i1-Q4_K_M | 15.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama4Some-SOVL-4x8B-L3-V1-i1-GGUF/resolve/main/Llama4Some-SOVL-4x8B-L3-V1.i1-Q5_K_S.gguf) | i1-Q5_K_S | 17.3 | |
| [GGUF](https://huggingface.co/mradermacher/Llama4Some-SOVL-4x8B-L3-V1-i1-GGUF/resolve/main/Llama4Some-SOVL-4x8B-L3-V1.i1-Q5_K_M.gguf) | i1-Q5_K_M | 17.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama4Some-SOVL-4x8B-L3-V1-i1-GGUF/resolve/main/Llama4Some-SOVL-4x8B-L3-V1.i1-Q6_K.gguf) | i1-Q6_K | 20.6 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/Llama-3-8B-Instruct-abliterated-v2-i1-GGUF | mradermacher | 2024-05-14T19:09:33Z | 90 | 1 | transformers | [
"transformers",
"gguf",
"en",
"base_model:cognitivecomputations/Llama-3-8B-Instruct-abliterated-v2",
"base_model:quantized:cognitivecomputations/Llama-3-8B-Instruct-abliterated-v2",
"license:llama3",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-05-12T21:02:21Z | ---
base_model: cognitivecomputations/Llama-3-8B-Instruct-abliterated-v2
language:
- en
library_name: transformers
license: llama3
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/cognitivecomputations/Llama-3-8B-Instruct-abliterated-v2
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Llama-3-8B-Instruct-abliterated-v2-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/Llama-3-8B-Instruct-abliterated-v2-i1-GGUF/resolve/main/Llama-3-8B-Instruct-abliterated-v2.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-abliterated-v2-i1-GGUF/resolve/main/Llama-3-8B-Instruct-abliterated-v2.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-abliterated-v2-i1-GGUF/resolve/main/Llama-3-8B-Instruct-abliterated-v2.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-abliterated-v2-i1-GGUF/resolve/main/Llama-3-8B-Instruct-abliterated-v2.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-abliterated-v2-i1-GGUF/resolve/main/Llama-3-8B-Instruct-abliterated-v2.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-abliterated-v2-i1-GGUF/resolve/main/Llama-3-8B-Instruct-abliterated-v2.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-abliterated-v2-i1-GGUF/resolve/main/Llama-3-8B-Instruct-abliterated-v2.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-abliterated-v2-i1-GGUF/resolve/main/Llama-3-8B-Instruct-abliterated-v2.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-abliterated-v2-i1-GGUF/resolve/main/Llama-3-8B-Instruct-abliterated-v2.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-abliterated-v2-i1-GGUF/resolve/main/Llama-3-8B-Instruct-abliterated-v2.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-abliterated-v2-i1-GGUF/resolve/main/Llama-3-8B-Instruct-abliterated-v2.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-abliterated-v2-i1-GGUF/resolve/main/Llama-3-8B-Instruct-abliterated-v2.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-abliterated-v2-i1-GGUF/resolve/main/Llama-3-8B-Instruct-abliterated-v2.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-abliterated-v2-i1-GGUF/resolve/main/Llama-3-8B-Instruct-abliterated-v2.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-abliterated-v2-i1-GGUF/resolve/main/Llama-3-8B-Instruct-abliterated-v2.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-abliterated-v2-i1-GGUF/resolve/main/Llama-3-8B-Instruct-abliterated-v2.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-abliterated-v2-i1-GGUF/resolve/main/Llama-3-8B-Instruct-abliterated-v2.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-abliterated-v2-i1-GGUF/resolve/main/Llama-3-8B-Instruct-abliterated-v2.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-abliterated-v2-i1-GGUF/resolve/main/Llama-3-8B-Instruct-abliterated-v2.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-abliterated-v2-i1-GGUF/resolve/main/Llama-3-8B-Instruct-abliterated-v2.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-abliterated-v2-i1-GGUF/resolve/main/Llama-3-8B-Instruct-abliterated-v2.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/llama-3-70B-Instruct-abliterated-i1-GGUF | mradermacher | 2024-05-14T19:09:22Z | 122 | 3 | transformers | [
"transformers",
"gguf",
"en",
"base_model:failspy/llama-3-70B-Instruct-abliterated",
"base_model:quantized:failspy/llama-3-70B-Instruct-abliterated",
"license:llama3",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-05-13T00:41:06Z | ---
base_model: failspy/llama-3-70B-Instruct-abliterated
language:
- en
library_name: transformers
license: llama3
license_link: LICENSE
license_name: llama3
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/failspy/llama-3-70B-Instruct-abliterated
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/llama-3-70B-Instruct-abliterated-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/llama-3-70B-Instruct-abliterated-i1-GGUF/resolve/main/llama-3-70B-Instruct-abliterated.i1-IQ1_S.gguf) | i1-IQ1_S | 15.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/llama-3-70B-Instruct-abliterated-i1-GGUF/resolve/main/llama-3-70B-Instruct-abliterated.i1-IQ1_M.gguf) | i1-IQ1_M | 16.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/llama-3-70B-Instruct-abliterated-i1-GGUF/resolve/main/llama-3-70B-Instruct-abliterated.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 19.2 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-70B-Instruct-abliterated-i1-GGUF/resolve/main/llama-3-70B-Instruct-abliterated.i1-IQ2_XS.gguf) | i1-IQ2_XS | 21.2 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-70B-Instruct-abliterated-i1-GGUF/resolve/main/llama-3-70B-Instruct-abliterated.i1-IQ2_S.gguf) | i1-IQ2_S | 22.3 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-70B-Instruct-abliterated-i1-GGUF/resolve/main/llama-3-70B-Instruct-abliterated.i1-IQ2_M.gguf) | i1-IQ2_M | 24.2 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-70B-Instruct-abliterated-i1-GGUF/resolve/main/llama-3-70B-Instruct-abliterated.i1-Q2_K.gguf) | i1-Q2_K | 26.5 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/llama-3-70B-Instruct-abliterated-i1-GGUF/resolve/main/llama-3-70B-Instruct-abliterated.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 27.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/llama-3-70B-Instruct-abliterated-i1-GGUF/resolve/main/llama-3-70B-Instruct-abliterated.i1-IQ3_XS.gguf) | i1-IQ3_XS | 29.4 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-70B-Instruct-abliterated-i1-GGUF/resolve/main/llama-3-70B-Instruct-abliterated.i1-IQ3_S.gguf) | i1-IQ3_S | 31.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/llama-3-70B-Instruct-abliterated-i1-GGUF/resolve/main/llama-3-70B-Instruct-abliterated.i1-Q3_K_S.gguf) | i1-Q3_K_S | 31.0 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/llama-3-70B-Instruct-abliterated-i1-GGUF/resolve/main/llama-3-70B-Instruct-abliterated.i1-IQ3_M.gguf) | i1-IQ3_M | 32.0 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-70B-Instruct-abliterated-i1-GGUF/resolve/main/llama-3-70B-Instruct-abliterated.i1-Q3_K_M.gguf) | i1-Q3_K_M | 34.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/llama-3-70B-Instruct-abliterated-i1-GGUF/resolve/main/llama-3-70B-Instruct-abliterated.i1-Q3_K_L.gguf) | i1-Q3_K_L | 37.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/llama-3-70B-Instruct-abliterated-i1-GGUF/resolve/main/llama-3-70B-Instruct-abliterated.i1-IQ4_XS.gguf) | i1-IQ4_XS | 38.0 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-70B-Instruct-abliterated-i1-GGUF/resolve/main/llama-3-70B-Instruct-abliterated.i1-Q4_0.gguf) | i1-Q4_0 | 40.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/llama-3-70B-Instruct-abliterated-i1-GGUF/resolve/main/llama-3-70B-Instruct-abliterated.i1-Q4_K_S.gguf) | i1-Q4_K_S | 40.4 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/llama-3-70B-Instruct-abliterated-i1-GGUF/resolve/main/llama-3-70B-Instruct-abliterated.i1-Q4_K_M.gguf) | i1-Q4_K_M | 42.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/llama-3-70B-Instruct-abliterated-i1-GGUF/resolve/main/llama-3-70B-Instruct-abliterated.i1-Q5_K_S.gguf) | i1-Q5_K_S | 48.8 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-70B-Instruct-abliterated-i1-GGUF/resolve/main/llama-3-70B-Instruct-abliterated.i1-Q5_K_M.gguf) | i1-Q5_K_M | 50.0 | |
| [PART 1](https://huggingface.co/mradermacher/llama-3-70B-Instruct-abliterated-i1-GGUF/resolve/main/llama-3-70B-Instruct-abliterated.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/llama-3-70B-Instruct-abliterated-i1-GGUF/resolve/main/llama-3-70B-Instruct-abliterated.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 58.0 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/Ortho-SOVL-8B-L3-i1-GGUF | mradermacher | 2024-05-14T19:09:16Z | 28 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:saishf/Ortho-SOVL-8B-L3",
"base_model:quantized:saishf/Ortho-SOVL-8B-L3",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-05-13T08:48:39Z | ---
base_model: saishf/Ortho-SOVL-8B-L3
language:
- en
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hfhfix -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/saishf/Ortho-SOVL-8B-L3
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Ortho-SOVL-8B-L3-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/Ortho-SOVL-8B-L3-i1-GGUF/resolve/main/Ortho-SOVL-8B-L3.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Ortho-SOVL-8B-L3-i1-GGUF/resolve/main/Ortho-SOVL-8B-L3.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Ortho-SOVL-8B-L3-i1-GGUF/resolve/main/Ortho-SOVL-8B-L3.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/Ortho-SOVL-8B-L3-i1-GGUF/resolve/main/Ortho-SOVL-8B-L3.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Ortho-SOVL-8B-L3-i1-GGUF/resolve/main/Ortho-SOVL-8B-L3.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Ortho-SOVL-8B-L3-i1-GGUF/resolve/main/Ortho-SOVL-8B-L3.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Ortho-SOVL-8B-L3-i1-GGUF/resolve/main/Ortho-SOVL-8B-L3.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Ortho-SOVL-8B-L3-i1-GGUF/resolve/main/Ortho-SOVL-8B-L3.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Ortho-SOVL-8B-L3-i1-GGUF/resolve/main/Ortho-SOVL-8B-L3.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Ortho-SOVL-8B-L3-i1-GGUF/resolve/main/Ortho-SOVL-8B-L3.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Ortho-SOVL-8B-L3-i1-GGUF/resolve/main/Ortho-SOVL-8B-L3.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Ortho-SOVL-8B-L3-i1-GGUF/resolve/main/Ortho-SOVL-8B-L3.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Ortho-SOVL-8B-L3-i1-GGUF/resolve/main/Ortho-SOVL-8B-L3.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Ortho-SOVL-8B-L3-i1-GGUF/resolve/main/Ortho-SOVL-8B-L3.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Ortho-SOVL-8B-L3-i1-GGUF/resolve/main/Ortho-SOVL-8B-L3.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Ortho-SOVL-8B-L3-i1-GGUF/resolve/main/Ortho-SOVL-8B-L3.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Ortho-SOVL-8B-L3-i1-GGUF/resolve/main/Ortho-SOVL-8B-L3.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Ortho-SOVL-8B-L3-i1-GGUF/resolve/main/Ortho-SOVL-8B-L3.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Ortho-SOVL-8B-L3-i1-GGUF/resolve/main/Ortho-SOVL-8B-L3.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Ortho-SOVL-8B-L3-i1-GGUF/resolve/main/Ortho-SOVL-8B-L3.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Ortho-SOVL-8B-L3-i1-GGUF/resolve/main/Ortho-SOVL-8B-L3.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/Llama-3-lima-nsfw-16k-test-GGUF | mradermacher | 2024-05-14T19:08:21Z | 309 | 1 | transformers | [
"transformers",
"gguf",
"en",
"base_model:openerotica/Llama-3-lima-nsfw-16k-test",
"base_model:quantized:openerotica/Llama-3-lima-nsfw-16k-test",
"license:llama3",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-14T05:25:09Z | ---
base_model: openerotica/Llama-3-lima-nsfw-16k-test
language:
- en
library_name: transformers
license: llama3
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/openerotica/Llama-3-lima-nsfw-16k-test
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Llama-3-lima-nsfw-16k-test-GGUF/resolve/main/Llama-3-lima-nsfw-16k-test.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-lima-nsfw-16k-test-GGUF/resolve/main/Llama-3-lima-nsfw-16k-test.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-lima-nsfw-16k-test-GGUF/resolve/main/Llama-3-lima-nsfw-16k-test.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-lima-nsfw-16k-test-GGUF/resolve/main/Llama-3-lima-nsfw-16k-test.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-lima-nsfw-16k-test-GGUF/resolve/main/Llama-3-lima-nsfw-16k-test.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-lima-nsfw-16k-test-GGUF/resolve/main/Llama-3-lima-nsfw-16k-test.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-lima-nsfw-16k-test-GGUF/resolve/main/Llama-3-lima-nsfw-16k-test.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-lima-nsfw-16k-test-GGUF/resolve/main/Llama-3-lima-nsfw-16k-test.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-lima-nsfw-16k-test-GGUF/resolve/main/Llama-3-lima-nsfw-16k-test.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-lima-nsfw-16k-test-GGUF/resolve/main/Llama-3-lima-nsfw-16k-test.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-lima-nsfw-16k-test-GGUF/resolve/main/Llama-3-lima-nsfw-16k-test.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-lima-nsfw-16k-test-GGUF/resolve/main/Llama-3-lima-nsfw-16k-test.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-lima-nsfw-16k-test-GGUF/resolve/main/Llama-3-lima-nsfw-16k-test.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-lima-nsfw-16k-test-GGUF/resolve/main/Llama-3-lima-nsfw-16k-test.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-lima-nsfw-16k-test-GGUF/resolve/main/Llama-3-lima-nsfw-16k-test.f16.gguf) | f16 | 16.2 | 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 -->
|
Abin7/3-lang | Abin7 | 2024-05-14T19:07:47Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"arxiv:2306.01708",
"base_model:NousResearch/Llama-2-7b-hf",
"base_model:merge:NousResearch/Llama-2-7b-hf",
"base_model:Telugu-LLM-Labs/Telugu-Llama2-7B-v0-Instruct",
"base_model:merge:Telugu-LLM-Labs/Telugu-Llama2-7B-v0-Instruct",
"base_model:abhinand/tamil-llama-7b-instruct-v0.1",
"base_model:merge:abhinand/tamil-llama-7b-instruct-v0.1",
"base_model:elyza/ELYZA-japanese-Llama-2-7b",
"base_model:merge:elyza/ELYZA-japanese-Llama-2-7b",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-14T19:03:29Z | ---
base_model:
- elyza/ELYZA-japanese-Llama-2-7b
- abhinand/tamil-llama-7b-instruct-v0.1
- NousResearch/Llama-2-7b-hf
- Telugu-LLM-Labs/Telugu-Llama2-7B-v0-Instruct
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [NousResearch/Llama-2-7b-hf](https://huggingface.co/NousResearch/Llama-2-7b-hf) as a base.
### Models Merged
The following models were included in the merge:
* [elyza/ELYZA-japanese-Llama-2-7b](https://huggingface.co/elyza/ELYZA-japanese-Llama-2-7b)
* [abhinand/tamil-llama-7b-instruct-v0.1](https://huggingface.co/abhinand/tamil-llama-7b-instruct-v0.1)
* [Telugu-LLM-Labs/Telugu-Llama2-7B-v0-Instruct](https://huggingface.co/Telugu-LLM-Labs/Telugu-Llama2-7B-v0-Instruct)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: elyza/ELYZA-japanese-Llama-2-7b
parameters:
density: 0.5
weight: 0.5
- model: abhinand/tamil-llama-7b-instruct-v0.1
parameters:
density: 0.5
weight: 0.5
- model: Telugu-LLM-Labs/Telugu-Llama2-7B-v0-Instruct
parameters:
density: 0.5
weight: 0.5
merge_method: ties
base_model: NousResearch/Llama-2-7b-hf
parameters:
normalize: false
int8_mask: true
dtype: float16
```
|
mradermacher/Mermaid-Llama-22B-RAG-GGUF | mradermacher | 2024-05-14T19:07:46Z | 6 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:TroyDoesAI/Mermaid-Llama-22B-RAG",
"base_model:quantized:TroyDoesAI/Mermaid-Llama-22B-RAG",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-14T15:30:34Z | ---
base_model: TroyDoesAI/Mermaid-Llama-22B-RAG
language:
- en
library_name: transformers
license: cc-by-4.0
no_imatrix: nan
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/TroyDoesAI/Mermaid-Llama-22B-RAG
<!-- provided-files -->
## 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/Mermaid-Llama-22B-RAG-GGUF/resolve/main/Mermaid-Llama-22B-RAG.Q2_K.gguf) | Q2_K | 8.2 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-22B-RAG-GGUF/resolve/main/Mermaid-Llama-22B-RAG.IQ3_XS.gguf) | IQ3_XS | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-22B-RAG-GGUF/resolve/main/Mermaid-Llama-22B-RAG.IQ3_S.gguf) | IQ3_S | 9.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-22B-RAG-GGUF/resolve/main/Mermaid-Llama-22B-RAG.Q3_K_S.gguf) | Q3_K_S | 9.6 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-22B-RAG-GGUF/resolve/main/Mermaid-Llama-22B-RAG.IQ3_M.gguf) | IQ3_M | 10.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-22B-RAG-GGUF/resolve/main/Mermaid-Llama-22B-RAG.Q3_K_M.gguf) | Q3_K_M | 10.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-22B-RAG-GGUF/resolve/main/Mermaid-Llama-22B-RAG.Q3_K_L.gguf) | Q3_K_L | 11.7 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-22B-RAG-GGUF/resolve/main/Mermaid-Llama-22B-RAG.IQ4_XS.gguf) | IQ4_XS | 11.9 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-22B-RAG-GGUF/resolve/main/Mermaid-Llama-22B-RAG.Q4_K_S.gguf) | Q4_K_S | 12.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-22B-RAG-GGUF/resolve/main/Mermaid-Llama-22B-RAG.Q4_K_M.gguf) | Q4_K_M | 13.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-22B-RAG-GGUF/resolve/main/Mermaid-Llama-22B-RAG.Q5_K_S.gguf) | Q5_K_S | 15.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-22B-RAG-GGUF/resolve/main/Mermaid-Llama-22B-RAG.Q5_K_M.gguf) | Q5_K_M | 15.6 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-22B-RAG-GGUF/resolve/main/Mermaid-Llama-22B-RAG.Q6_K.gguf) | Q6_K | 18.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Mermaid-Llama-22B-RAG-GGUF/resolve/main/Mermaid-Llama-22B-RAG.Q8_0.gguf) | Q8_0 | 23.3 | 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 -->
|
jknottllm/Meta-Llama-3-8B-Instruct-FineTuned | jknottllm | 2024-05-14T19:05:59Z | 32 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"facebook",
"meta",
"pytorch",
"llama-3",
"conversational",
"en",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-14T18:18:06Z | ---
language:
- en
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
license: llama3
extra_gated_prompt: >-
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- example_title: Programming assistant
messages:
- role: system
content: You are a helpful and honest code and programming assistant. Please, respond concisely and truthfully.
- role: user
content: Write a function that computes the nth fibonacci number.
inference:
parameters:
max_new_tokens: 300
stop:
- <|end_of_text|>
- <|eot_id|>
---
## Model Details
Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety.
**Model developers** Meta
**Variations** Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants.
**Input** Models input text only.
**Output** Models generate text and code only.
**Model Architecture** Llama 3 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>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="2" >Llama 3
</td>
<td rowspan="2" >A new mix of publicly available online data.
</td>
<td>8B
</td>
<td>8k
</td>
<td>Yes
</td>
<td rowspan="2" >15T+
</td>
<td>March, 2023
</td>
</tr>
<tr>
<td>70B
</td>
<td>8k
</td>
<td>Yes
</td>
<td>December, 2023
</td>
</tr>
</table>
**Llama 3 family of models**. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date** April 18, 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 is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/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 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
**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 Community License. Use in languages other than English**.
**Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy.
## How to use
This repository contains two versions of Meta-Llama-3-8B-Instruct, for use with transformers and with the original `llama3` codebase.
### Use with transformers
You can run conversational inference using the Transformers pipeline abstraction, or by leveraging the Auto classes with the `generate()` function. Let's see examples of both.
#### Transformers pipeline
```python
import transformers
import torch
model_id = "meta-llama/Meta-Llama-3-8B-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?"},
]
prompt = pipeline.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = pipeline(
prompt,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])
```
#### Transformers AutoModelForCausalLM
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
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?"},
]
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = model.generate(
input_ids,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))
```
### Use with `llama3`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3)
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Meta-Llama-3-8B-Instruct --include "original/*" --local-dir Meta-Llama-3-8B-Instruct
```
For Hugging Face support, we recommend using transformers or TGI, but a similar command works.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint Pretraining utilized a cumulative** 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program.
<table>
<tr>
<td>
</td>
<td><strong>Time (GPU hours)</strong>
</td>
<td><strong>Power Consumption (W)</strong>
</td>
<td><strong>Carbon Emitted(tCO2eq)</strong>
</td>
</tr>
<tr>
<td>Llama 3 8B
</td>
<td>1.3M
</td>
<td>700
</td>
<td>390
</td>
</tr>
<tr>
<td>Llama 3 70B
</td>
<td>6.4M
</td>
<td>700
</td>
<td>1900
</td>
</tr>
<tr>
<td>Total
</td>
<td>7.7M
</td>
<td>
</td>
<td>2290
</td>
</tr>
</table>
**CO2 emissions during pre-training**. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively.
## Benchmarks
In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md).
### Base pretrained models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama2 7B</strong>
</td>
<td><strong>Llama2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama2 70B</strong>
</td>
</tr>
<tr>
<td rowspan="6" >General
</td>
<td>MMLU (5-shot)
</td>
<td>66.6
</td>
<td>45.7
</td>
<td>53.8
</td>
<td>79.5
</td>
<td>69.7
</td>
</tr>
<tr>
<td>AGIEval English (3-5 shot)
</td>
<td>45.9
</td>
<td>28.8
</td>
<td>38.7
</td>
<td>63.0
</td>
<td>54.8
</td>
</tr>
<tr>
<td>CommonSenseQA (7-shot)
</td>
<td>72.6
</td>
<td>57.6
</td>
<td>67.6
</td>
<td>83.8
</td>
<td>78.7
</td>
</tr>
<tr>
<td>Winogrande (5-shot)
</td>
<td>76.1
</td>
<td>73.3
</td>
<td>75.4
</td>
<td>83.1
</td>
<td>81.8
</td>
</tr>
<tr>
<td>BIG-Bench Hard (3-shot, CoT)
</td>
<td>61.1
</td>
<td>38.1
</td>
<td>47.0
</td>
<td>81.3
</td>
<td>65.7
</td>
</tr>
<tr>
<td>ARC-Challenge (25-shot)
</td>
<td>78.6
</td>
<td>53.7
</td>
<td>67.6
</td>
<td>93.0
</td>
<td>85.3
</td>
</tr>
<tr>
<td>Knowledge reasoning
</td>
<td>TriviaQA-Wiki (5-shot)
</td>
<td>78.5
</td>
<td>72.1
</td>
<td>79.6
</td>
<td>89.7
</td>
<td>87.5
</td>
</tr>
<tr>
<td rowspan="4" >Reading comprehension
</td>
<td>SQuAD (1-shot)
</td>
<td>76.4
</td>
<td>72.2
</td>
<td>72.1
</td>
<td>85.6
</td>
<td>82.6
</td>
</tr>
<tr>
<td>QuAC (1-shot, F1)
</td>
<td>44.4
</td>
<td>39.6
</td>
<td>44.9
</td>
<td>51.1
</td>
<td>49.4
</td>
</tr>
<tr>
<td>BoolQ (0-shot)
</td>
<td>75.7
</td>
<td>65.5
</td>
<td>66.9
</td>
<td>79.0
</td>
<td>73.1
</td>
</tr>
<tr>
<td>DROP (3-shot, F1)
</td>
<td>58.4
</td>
<td>37.9
</td>
<td>49.8
</td>
<td>79.7
</td>
<td>70.2
</td>
</tr>
</table>
### Instruction tuned models
<table>
<tr>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama 2 7B</strong>
</td>
<td><strong>Llama 2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama 2 70B</strong>
</td>
</tr>
<tr>
<td>MMLU (5-shot)
</td>
<td>68.4
</td>
<td>34.1
</td>
<td>47.8
</td>
<td>82.0
</td>
<td>52.9
</td>
</tr>
<tr>
<td>GPQA (0-shot)
</td>
<td>34.2
</td>
<td>21.7
</td>
<td>22.3
</td>
<td>39.5
</td>
<td>21.0
</td>
</tr>
<tr>
<td>HumanEval (0-shot)
</td>
<td>62.2
</td>
<td>7.9
</td>
<td>14.0
</td>
<td>81.7
</td>
<td>25.6
</td>
</tr>
<tr>
<td>GSM-8K (8-shot, CoT)
</td>
<td>79.6
</td>
<td>25.7
</td>
<td>77.4
</td>
<td>93.0
</td>
<td>57.5
</td>
</tr>
<tr>
<td>MATH (4-shot, CoT)
</td>
<td>30.0
</td>
<td>3.8
</td>
<td>6.7
</td>
<td>50.4
</td>
<td>11.6
</td>
</tr>
</table>
### Responsibility & Safety
We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.
Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.
Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.
As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started.
#### Llama 3-Instruct
As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case.
<span style="text-decoration:underline;">Safety</span>
For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable.
<span style="text-decoration:underline;">Refusals</span>
In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2.
We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date.
#### Responsible release
In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.
Misuse
If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/).
#### Critical risks
<span style="text-decoration:underline;">CBRNE</span> (Chemical, Biological, Radiological, Nuclear, and high yield Explosives)
We have conducted a two fold assessment of the safety of the model in this area:
* Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks.
* Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model).
### <span style="text-decoration:underline;">Cyber Security </span>
We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval).
### <span style="text-decoration:underline;">Child Safety</span>
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. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
### 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 in 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).
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 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 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 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’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 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.
Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide)
## Citation instructions
@article{llama3modelcard,
title={Llama 3 Model Card},
author={AI@Meta},
year={2024},
url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}
## Contributors
Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos
|
newsletter/dolphin-2.8-mistral-7b-v02-Q6_K-GGUF | newsletter | 2024-05-14T19:02:05Z | 5 | 0 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"dataset:cognitivecomputations/dolphin",
"dataset:cognitivecomputations/dolphin-coder",
"dataset:cognitivecomputations/samantha-data",
"dataset:jondurbin/airoboros-2.2.1",
"dataset:teknium/openhermes-2.5",
"dataset:m-a-p/Code-Feedback",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"base_model:mistral-community/Mistral-7B-v0.2",
"base_model:quantized:mistral-community/Mistral-7B-v0.2",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-14T19:01:41Z | ---
language:
- en
license: apache-2.0
tags:
- llama-cpp
- gguf-my-repo
base_model: alpindale/Mistral-7B-v0.2-hf
datasets:
- cognitivecomputations/dolphin
- cognitivecomputations/dolphin-coder
- cognitivecomputations/samantha-data
- jondurbin/airoboros-2.2.1
- teknium/openhermes-2.5
- m-a-p/Code-Feedback
- m-a-p/CodeFeedback-Filtered-Instruction
model-index:
- name: dolphin-2.8-mistral-7b-v02
results:
- task:
type: text-generation
dataset:
name: HumanEval
type: openai_humaneval
metrics:
- type: pass@1
value: 0.469
name: pass@1
verified: false
---
# newsletter/dolphin-2.8-mistral-7b-v02-Q6_K-GGUF
This model was converted to GGUF format from [`cognitivecomputations/dolphin-2.8-mistral-7b-v02`](https://huggingface.co/cognitivecomputations/dolphin-2.8-mistral-7b-v02) 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/cognitivecomputations/dolphin-2.8-mistral-7b-v02) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo newsletter/dolphin-2.8-mistral-7b-v02-Q6_K-GGUF --model dolphin-2.8-mistral-7b-v02.Q6_K.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo newsletter/dolphin-2.8-mistral-7b-v02-Q6_K-GGUF --model dolphin-2.8-mistral-7b-v02.Q6_K.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m dolphin-2.8-mistral-7b-v02.Q6_K.gguf -n 128
```
|
Syruhas/Mistral-SQL-2.1 | Syruhas | 2024-05-14T19:00:51Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-14T18:53:54Z | ---
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]
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### Out-of-Scope Use
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## 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
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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## Model Card Contact
[More Information Needed] |
CMU-AIR2/math-phi-1-5-lr-3e-6 | CMU-AIR2 | 2024-05-14T18:59:43Z | 10 | 0 | transformers | [
"transformers",
"safetensors",
"phi",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-14T18:42:14Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
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[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
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
#### 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] |
djward888/SFR-Iterative-DPO-LLaMA-3-8B-R-Q6_K-GGUF | djward888 | 2024-05-14T18:59:12Z | 4 | 1 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"license:cc-by-nc-nd-3.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-14T18:58:54Z | ---
license: cc-by-nc-nd-3.0
tags:
- llama-cpp
- gguf-my-repo
---
# djward888/SFR-Iterative-DPO-LLaMA-3-8B-R-Q6_K-GGUF
This model was converted to GGUF format from [`Salesforce/SFR-Iterative-DPO-LLaMA-3-8B-R`](https://huggingface.co/Salesforce/SFR-Iterative-DPO-LLaMA-3-8B-R) 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/Salesforce/SFR-Iterative-DPO-LLaMA-3-8B-R) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo djward888/SFR-Iterative-DPO-LLaMA-3-8B-R-Q6_K-GGUF --model sfr-iterative-dpo-llama-3-8b-r.Q6_K.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo djward888/SFR-Iterative-DPO-LLaMA-3-8B-R-Q6_K-GGUF --model sfr-iterative-dpo-llama-3-8b-r.Q6_K.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m sfr-iterative-dpo-llama-3-8b-r.Q6_K.gguf -n 128
```
|
alexsherstinsky/predibase-plus-gretel-ai-text-to-sql-mistral-7b-instruct-v0-2-no-json | alexsherstinsky | 2024-05-14T18:58:30Z | 0 | 0 | peft | [
"peft",
"safetensors",
"dataset:gretelai/synthetic_text_to_sql",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2",
"region:us"
] | null | 2024-05-14T18:57:58Z | ---
library_name: peft
base_model: mistralai/Mistral-7B-Instruct-v0.2
datasets:
- gretelai/synthetic_text_to_sql
---
# 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. -->
- **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]
### Framework versions
- PEFT 0.10.0 |
anslin-raj/temp-llama-3-8b-16bit | anslin-raj | 2024-05-14T18:57:34Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"medical",
"unsloth",
"trl",
"sft",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-14T13:58:11Z | ---
license: apache-2.0
tags:
- medical
- unsloth
- trl
- sft
---
|
wwhlazio/tiny_llama-2-7b-wwhlazio_fkn | wwhlazio | 2024-05-14T18:56:51Z | 76 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-05-14T15:54:06Z | ---
library_name: transformers
tags:
- 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]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
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### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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## Model Card Contact
[More Information Needed] |
aengusl/GIBBERISH-4-12_sweep_1-_-epsilon_1.0_num_steps_100_model_layers_post8-_-adapter | aengusl | 2024-05-14T18:56:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-14T18:55:52Z | ---
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]
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|
aengusl/GIBBERISH-4-12_sweep_1-_-epsilon_0.5_num_steps_100_model_layers_post8-_-adapter | aengusl | 2024-05-14T18:55:45Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-14T18:55:35Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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|
cedralpass/trained_model | cedralpass | 2024-05-14T18:52:30Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-14T18:51:25Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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shapiron/ppo-LunarLander-v2-unit1 | shapiron | 2024-05-14T18:51:18Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2024-05-14T18:50:43Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 7.50 +/- 93.37
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
paulh27/wmt_aligned_smallmT5 | paulh27 | 2024-05-14T18:50:24Z | 161 | 0 | transformers | [
"transformers",
"safetensors",
"mt5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-05-14T18:49:46Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
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<!-- 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
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[More Information Needed]
## Training Details
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## Environmental Impact
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tsavage68/Transaminitis_L3_1000steps_1e5rate_05beta_CSFTDPO | tsavage68 | 2024-05-14T18:49:44Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"base_model:tsavage68/Transaminitis_L3_1000rate_1e7_SFT",
"base_model:finetune:tsavage68/Transaminitis_L3_1000rate_1e7_SFT",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-14T18:43:11Z | ---
license: llama3
base_model: tsavage68/Transaminitis_L3_1000rate_1e7_SFT
tags:
- trl
- dpo
- generated_from_trainer
model-index:
- name: Transaminitis_L3_1000steps_1e5rate_05beta_CSFTDPO
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. -->
# Transaminitis_L3_1000steps_1e5rate_05beta_CSFTDPO
This model is a fine-tuned version of [tsavage68/Transaminitis_L3_1000rate_1e7_SFT](https://huggingface.co/tsavage68/Transaminitis_L3_1000rate_1e7_SFT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6914
- Rewards/chosen: -15.4983
- Rewards/rejected: -15.7754
- Rewards/accuracies: 0.3000
- Rewards/margins: 0.2771
- Logps/rejected: -50.1055
- Logps/chosen: -49.5308
- Logits/rejected: -0.7536
- Logits/chosen: -0.7536
## 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: 1e-05
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 1.3308 | 0.2 | 25 | 1.4218 | -5.1457 | -5.2961 | 0.5400 | 0.1503 | -29.1468 | -28.8257 | -0.7892 | -0.7880 |
| 1.1498 | 0.4 | 50 | 0.7304 | -4.8999 | -4.8425 | 0.4000 | -0.0574 | -28.2397 | -28.3340 | -2.1796 | -2.1797 |
| 1.2832 | 0.6 | 75 | 0.9255 | -1.6896 | -4.2819 | 0.6300 | 2.5923 | -27.1184 | -21.9133 | -1.0885 | -1.0850 |
| 2.8764 | 0.8 | 100 | 3.8444 | -19.0391 | -19.6042 | 0.5400 | 0.5651 | -57.7631 | -56.6124 | -0.1327 | -0.1327 |
| 0.8442 | 1.0 | 125 | 0.7901 | -16.2193 | -16.1877 | 0.5400 | -0.0316 | -50.9301 | -50.9727 | -0.7765 | -0.7765 |
| 0.7539 | 1.2 | 150 | 0.8102 | -15.9518 | -15.8097 | 0.4600 | -0.1421 | -50.1741 | -50.4379 | -0.9130 | -0.9130 |
| 0.7462 | 1.4 | 175 | 0.7415 | -16.1492 | -16.0632 | 0.4100 | -0.0860 | -50.6811 | -50.8325 | -0.8303 | -0.8304 |
| 0.7363 | 1.6 | 200 | 0.7404 | -16.2295 | -16.1487 | 0.4300 | -0.0808 | -50.8521 | -50.9933 | -0.8473 | -0.8473 |
| 0.7666 | 1.8 | 225 | 0.8203 | -16.1693 | -16.0294 | 0.4600 | -0.1399 | -50.6135 | -50.8729 | -0.9939 | -0.9939 |
| 0.7639 | 2.0 | 250 | 0.8115 | -16.1899 | -16.0490 | 0.4600 | -0.1409 | -50.6527 | -50.9140 | -0.8241 | -0.8241 |
| 0.7153 | 2.2 | 275 | 0.7477 | -16.3133 | -16.2548 | 0.5200 | -0.0585 | -51.0642 | -51.1609 | -0.7566 | -0.7566 |
| 0.8015 | 2.4 | 300 | 0.7461 | -16.9989 | -16.9443 | 0.5200 | -0.0546 | -52.4434 | -52.5321 | -0.7484 | -0.7484 |
| 0.7741 | 2.6 | 325 | 0.8205 | -16.7965 | -16.6632 | 0.4600 | -0.1333 | -51.8812 | -52.1273 | -0.8410 | -0.8410 |
| 0.8986 | 2.8 | 350 | 0.7380 | -18.5683 | -18.4872 | 0.3000 | -0.0811 | -55.5292 | -55.6709 | -1.2363 | -1.2363 |
| 0.849 | 3.0 | 375 | 2.3943 | -12.5963 | -12.1503 | 0.4600 | -0.4460 | -42.8553 | -43.7269 | -0.4070 | -0.4065 |
| 0.8088 | 3.2 | 400 | 0.7402 | -15.8638 | -15.7863 | 0.4600 | -0.0775 | -50.1272 | -50.2618 | -0.6327 | -0.6327 |
| 0.8743 | 3.4 | 425 | 0.7330 | -18.1568 | -18.0906 | 0.4100 | -0.0662 | -54.7359 | -54.8479 | -1.1648 | -1.1647 |
| 0.7984 | 3.6 | 450 | 0.7252 | -17.1837 | -17.1365 | 0.3300 | -0.0472 | -52.8276 | -52.9015 | -1.0496 | -1.0496 |
| 0.7968 | 3.8 | 475 | 0.8038 | -15.3963 | -15.3324 | 0.5400 | -0.0639 | -49.2195 | -49.3268 | -0.5901 | -0.5901 |
| 0.6856 | 4.0 | 500 | 0.7152 | -15.3527 | -15.4448 | 0.5300 | 0.0921 | -49.4443 | -49.2396 | -0.6386 | -0.6386 |
| 0.7167 | 4.2 | 525 | 0.7150 | -15.4946 | -15.5966 | 0.5100 | 0.1019 | -49.7478 | -49.5235 | -0.6307 | -0.6307 |
| 0.6039 | 4.4 | 550 | 0.7637 | -15.4627 | -15.6191 | 0.5400 | 0.1563 | -49.7928 | -49.4597 | -0.7779 | -0.7779 |
| 0.7734 | 4.6 | 575 | 0.7098 | -15.4720 | -15.6304 | 0.5300 | 0.1584 | -49.8155 | -49.4783 | -0.7391 | -0.7391 |
| 0.6561 | 4.8 | 600 | 0.7003 | -15.6141 | -15.8015 | 0.5100 | 0.1874 | -50.1577 | -49.7625 | -0.7691 | -0.7691 |
| 0.8328 | 5.0 | 625 | 0.6902 | -15.6776 | -15.8918 | 0.2800 | 0.2141 | -50.3382 | -49.8894 | -0.7913 | -0.7913 |
| 0.6256 | 5.2 | 650 | 0.6963 | -15.6139 | -15.8252 | 0.4800 | 0.2113 | -50.2051 | -49.7620 | -0.7909 | -0.7909 |
| 0.7336 | 5.4 | 675 | 0.7511 | -15.6031 | -15.7883 | 0.5400 | 0.1852 | -50.1313 | -49.7403 | -0.7741 | -0.7741 |
| 0.6527 | 5.6 | 700 | 0.7877 | -15.3869 | -15.6214 | 0.5400 | 0.2345 | -49.7974 | -49.3080 | -0.7426 | -0.7426 |
| 0.705 | 5.8 | 725 | 0.6894 | -15.4753 | -15.7539 | 0.2900 | 0.2786 | -50.0625 | -49.4848 | -0.7283 | -0.7283 |
| 0.7304 | 6.0 | 750 | 0.6899 | -15.4744 | -15.7563 | 0.2600 | 0.2819 | -50.0674 | -49.4830 | -0.7329 | -0.7329 |
| 0.7198 | 6.2 | 775 | 0.6920 | -15.5016 | -15.7713 | 0.3800 | 0.2697 | -50.0972 | -49.5374 | -0.7513 | -0.7513 |
| 0.7129 | 6.4 | 800 | 0.6908 | -15.5077 | -15.7810 | 0.3200 | 0.2733 | -50.1167 | -49.5497 | -0.7483 | -0.7483 |
| 0.6531 | 6.6 | 825 | 0.6900 | -15.4995 | -15.7803 | 0.2900 | 0.2807 | -50.1153 | -49.5333 | -0.7526 | -0.7526 |
| 0.7044 | 6.8 | 850 | 0.6918 | -15.4889 | -15.7660 | 0.3600 | 0.2771 | -50.0868 | -49.5121 | -0.7521 | -0.7520 |
| 0.6293 | 7.0 | 875 | 0.6914 | -15.4926 | -15.7693 | 0.3700 | 0.2766 | -50.0933 | -49.5195 | -0.7537 | -0.7537 |
| 0.7101 | 7.2 | 900 | 0.6905 | -15.4995 | -15.7785 | 0.2800 | 0.2789 | -50.1116 | -49.5333 | -0.7528 | -0.7528 |
| 0.6389 | 7.4 | 925 | 0.6913 | -15.4980 | -15.7753 | 0.3300 | 0.2772 | -50.1052 | -49.5303 | -0.7532 | -0.7532 |
| 0.6333 | 7.6 | 950 | 0.6907 | -15.4984 | -15.7771 | 0.3200 | 0.2786 | -50.1088 | -49.5310 | -0.7534 | -0.7534 |
| 0.6491 | 7.8 | 975 | 0.6912 | -15.4974 | -15.7749 | 0.3200 | 0.2775 | -50.1045 | -49.5291 | -0.7534 | -0.7534 |
| 0.6433 | 8.0 | 1000 | 0.6914 | -15.4983 | -15.7754 | 0.3000 | 0.2771 | -50.1055 | -49.5308 | -0.7536 | -0.7536 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.0.0+cu117
- Datasets 2.19.1
- Tokenizers 0.19.1
|
mlx-community/granite-34b-code-base-8bit | mlx-community | 2024-05-14T18:46:07Z | 18 | 1 | transformers | [
"transformers",
"safetensors",
"gpt_bigcode",
"text-generation",
"code",
"granite",
"mlx",
"dataset:codeparrot/github-code-clean",
"dataset:bigcode/starcoderdata",
"dataset:open-web-math/open-web-math",
"dataset:math-ai/StackMathQA",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-06T21:40:21Z | ---
license: apache-2.0
library_name: transformers
tags:
- code
- granite
- mlx
datasets:
- codeparrot/github-code-clean
- bigcode/starcoderdata
- open-web-math/open-web-math
- math-ai/StackMathQA
metrics:
- code_eval
pipeline_tag: text-generation
inference: true
model-index:
- name: granite-34b-code-base
results:
- task:
type: text-generation
dataset:
name: MBPP
type: mbpp
metrics:
- type: pass@1
value: 47.2
name: pass@1
- task:
type: text-generation
dataset:
name: MBPP+
type: evalplus/mbppplus
metrics:
- type: pass@1
value: 53.1
name: pass@1
- task:
type: text-generation
dataset:
name: HumanEvalSynthesis(Python)
type: bigcode/humanevalpack
metrics:
- type: pass@1
value: 48.2
name: pass@1
- type: pass@1
value: 54.9
name: pass@1
- type: pass@1
value: 61.6
name: pass@1
- type: pass@1
value: 40.2
name: pass@1
- type: pass@1
value: 50.0
name: pass@1
- type: pass@1
value: 39.6
name: pass@1
- type: pass@1
value: 42.7
name: pass@1
- type: pass@1
value: 26.2
name: pass@1
- type: pass@1
value: 47.0
name: pass@1
- type: pass@1
value: 26.8
name: pass@1
- type: pass@1
value: 36.6
name: pass@1
- type: pass@1
value: 25.0
name: pass@1
- type: pass@1
value: 20.1
name: pass@1
- type: pass@1
value: 30.5
name: pass@1
- type: pass@1
value: 40.9
name: pass@1
- type: pass@1
value: 34.1
name: pass@1
- type: pass@1
value: 39.0
name: pass@1
- type: pass@1
value: 12.2
name: pass@1
---
# mlx-community/granite-34b-code-base-8bit
The Model [mlx-community/granite-34b-code-base-8bit](https://huggingface.co/mlx-community/granite-34b-code-base-8bit) was converted to MLX format from [ibm-granite/granite-34b-code-base](https://huggingface.co/ibm-granite/granite-34b-code-base) using mlx-lm version **0.13.0**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/granite-34b-code-base-8bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
mlx-community/granite-34b-code-instruct-8bit | mlx-community | 2024-05-14T18:45:15Z | 10 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_bigcode",
"text-generation",
"code",
"granite",
"mlx",
"conversational",
"dataset:bigcode/commitpackft",
"dataset:TIGER-Lab/MathInstruct",
"dataset:meta-math/MetaMathQA",
"dataset:glaiveai/glaive-code-assistant-v3",
"dataset:glaive-function-calling-v2",
"dataset:bugdaryan/sql-create-context-instruction",
"dataset:garage-bAInd/Open-Platypus",
"dataset:nvidia/HelpSteer",
"base_model:ibm-granite/granite-34b-code-base-8k",
"base_model:finetune:ibm-granite/granite-34b-code-base-8k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-06T21:40:51Z | ---
license: apache-2.0
library_name: transformers
tags:
- code
- granite
- mlx
base_model: ibm-granite/granite-34b-code-base
datasets:
- bigcode/commitpackft
- TIGER-Lab/MathInstruct
- meta-math/MetaMathQA
- glaiveai/glaive-code-assistant-v3
- glaive-function-calling-v2
- bugdaryan/sql-create-context-instruction
- garage-bAInd/Open-Platypus
- nvidia/HelpSteer
metrics:
- code_eval
pipeline_tag: text-generation
inference: true
model-index:
- name: granite-34b-code-instruct
results:
- task:
type: text-generation
dataset:
name: HumanEvalSynthesis(Python)
type: bigcode/humanevalpack
metrics:
- type: pass@1
value: 62.2
name: pass@1
- type: pass@1
value: 56.7
name: pass@1
- type: pass@1
value: 62.8
name: pass@1
- type: pass@1
value: 47.6
name: pass@1
- type: pass@1
value: 57.9
name: pass@1
- type: pass@1
value: 41.5
name: pass@1
- type: pass@1
value: 53.0
name: pass@1
- type: pass@1
value: 45.1
name: pass@1
- type: pass@1
value: 50.6
name: pass@1
- type: pass@1
value: 36.0
name: pass@1
- type: pass@1
value: 42.7
name: pass@1
- type: pass@1
value: 23.8
name: pass@1
- type: pass@1
value: 54.9
name: pass@1
- type: pass@1
value: 47.6
name: pass@1
- type: pass@1
value: 55.5
name: pass@1
- type: pass@1
value: 51.2
name: pass@1
- type: pass@1
value: 47.0
name: pass@1
- type: pass@1
value: 45.1
name: pass@1
---
# mlx-community/granite-34b-code-instruct-8bit
The Model [mlx-community/granite-34b-code-instruct-8bit](https://huggingface.co/mlx-community/granite-34b-code-instruct-8bit) was converted to MLX format from [ibm-granite/granite-34b-code-instruct](https://huggingface.co/ibm-granite/granite-34b-code-instruct) using mlx-lm version **0.13.0**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/granite-34b-code-instruct-8bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
tomaszki/stablelm-60 | tomaszki | 2024-05-14T18:44:05Z | 130 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-14T18:42:31Z | ---
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] |
Chand0320/Eurus-RM-7b-GPTQ | Chand0320 | 2024-05-14T18:38:01Z | 76 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"feature-extraction",
"custom_code",
"en",
"dataset:wikitext",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"gptq",
"region:us"
] | feature-extraction | 2024-05-14T11:59:14Z | ---
license: apache-2.0
datasets:
- wikitext
language:
- en
---
GPTQ quantized version of Eurus-7B Reward Model (RM). Can be run with exllama |
shapiron/lession1-ppo-lander | shapiron | 2024-05-14T18:37:30Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2024-05-14T18:37:04Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -166.57 +/- 45.42
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
ImanNalia/finetune_wav2vec2_960h_six_second | ImanNalia | 2024-05-14T18:35:06Z | 165 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-base-960h",
"base_model:finetune:facebook/wav2vec2-base-960h",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-05-14T18:34:56Z | ---
license: apache-2.0
base_model: facebook/wav2vec2-base-960h
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: finetune_wav2vec2_960h_six_second
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. -->
# finetune_wav2vec2_960h_six_second
This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8664
- Wer: 34.7919
- Cer: 18.1492
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:--------:|:-----:|:---------------:|:-------:|:-------:|
| 0.9855 | 18.5185 | 1000 | 0.8664 | 34.7919 | 18.1492 |
| 0.5055 | 37.0370 | 2000 | 0.9980 | 34.5251 | 18.1828 |
| 0.3066 | 55.5556 | 3000 | 1.0063 | 33.3511 | 17.2474 |
| 0.2186 | 74.0741 | 4000 | 1.1086 | 32.3372 | 16.9617 |
| 0.1628 | 92.5926 | 5000 | 1.1707 | 31.4835 | 16.5416 |
| 0.1362 | 111.1111 | 6000 | 1.1494 | 31.2700 | 16.4351 |
| 0.1069 | 129.6296 | 7000 | 1.2482 | 31.8837 | 16.4295 |
| 0.1004 | 148.1481 | 8000 | 1.3189 | 31.5635 | 16.9393 |
| 0.0851 | 166.6667 | 9000 | 1.3079 | 30.8965 | 16.3343 |
| 0.0794 | 185.1852 | 10000 | 1.3297 | 30.8698 | 16.1214 |
### Framework versions
- Transformers 4.40.2
- Pytorch 1.12.1+cu116
- Datasets 2.19.1
- Tokenizers 0.19.1
|
Marco127/BERT_hodi | Marco127 | 2024-05-14T18:31:49Z | 182 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-05-13T19:10:45Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed]
|
tsavage68/Transaminitis_L3_1000steps_1e7rate_03beta_CSFTDPO | tsavage68 | 2024-05-14T18:27:35Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"base_model:tsavage68/Transaminitis_L3_1000rate_1e7_SFT",
"base_model:finetune:tsavage68/Transaminitis_L3_1000rate_1e7_SFT",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-14T18:21:20Z | ---
license: llama3
base_model: tsavage68/Transaminitis_L3_1000rate_1e7_SFT
tags:
- trl
- dpo
- generated_from_trainer
model-index:
- name: Transaminitis_L3_1000steps_1e7rate_03beta_CSFTDPO
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. -->
# Transaminitis_L3_1000steps_1e7rate_03beta_CSFTDPO
This model is a fine-tuned version of [tsavage68/Transaminitis_L3_1000rate_1e7_SFT](https://huggingface.co/tsavage68/Transaminitis_L3_1000rate_1e7_SFT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1392
- Rewards/chosen: 1.7582
- Rewards/rejected: -2.4140
- Rewards/accuracies: 0.9300
- Rewards/margins: 4.1722
- Logps/rejected: -26.6014
- Logps/chosen: -12.6736
- Logits/rejected: -1.0630
- Logits/chosen: -1.0536
## 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: 1e-07
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6949 | 0.2 | 25 | 0.6922 | -0.0089 | -0.0113 | 0.5600 | 0.0023 | -18.5922 | -18.5640 | -1.0661 | -1.0649 |
| 0.689 | 0.4 | 50 | 0.6902 | -0.0743 | -0.0808 | 0.5700 | 0.0065 | -18.8241 | -18.7820 | -1.0662 | -1.0650 |
| 0.6966 | 0.6 | 75 | 0.6990 | -0.0262 | -0.0204 | 0.4500 | -0.0058 | -18.6227 | -18.6216 | -1.0666 | -1.0653 |
| 0.6628 | 0.8 | 100 | 0.7163 | -0.1175 | -0.1132 | 0.4600 | -0.0042 | -18.9322 | -18.9257 | -1.0693 | -1.0680 |
| 0.7015 | 1.0 | 125 | 0.6776 | -0.4612 | -0.5510 | 0.5400 | 0.0898 | -20.3914 | -20.0715 | -1.0730 | -1.0717 |
| 0.6668 | 1.2 | 150 | 0.6800 | 0.0635 | 0.0084 | 0.4600 | 0.0551 | -18.5267 | -18.3224 | -1.0700 | -1.0688 |
| 0.6409 | 1.4 | 175 | 0.6361 | -0.0704 | -0.1936 | 0.7700 | 0.1232 | -19.2001 | -18.7690 | -1.0737 | -1.0723 |
| 0.6123 | 1.6 | 200 | 0.6074 | 0.0180 | -0.1849 | 0.7300 | 0.2030 | -19.1711 | -18.4741 | -1.0741 | -1.0726 |
| 0.6043 | 1.8 | 225 | 0.5988 | 0.3189 | 0.0943 | 0.75 | 0.2245 | -18.2403 | -17.4714 | -1.0739 | -1.0724 |
| 0.5608 | 2.0 | 250 | 0.5625 | 0.4639 | 0.1602 | 0.8300 | 0.3037 | -18.0205 | -16.9878 | -1.0739 | -1.0723 |
| 0.5206 | 2.2 | 275 | 0.5084 | 0.5476 | 0.0971 | 0.8700 | 0.4504 | -18.2310 | -16.7090 | -1.0751 | -1.0732 |
| 0.4709 | 2.4 | 300 | 0.4614 | 0.7169 | 0.1230 | 0.8800 | 0.5939 | -18.1448 | -16.1447 | -1.0753 | -1.0732 |
| 0.433 | 2.6 | 325 | 0.4388 | 1.1217 | 0.3690 | 0.8500 | 0.7527 | -17.3245 | -14.7951 | -1.0729 | -1.0703 |
| 0.2802 | 2.8 | 350 | 0.3165 | 1.1549 | -0.0896 | 0.9200 | 1.2445 | -18.8533 | -14.6845 | -1.0737 | -1.0701 |
| 0.3044 | 3.0 | 375 | 0.2744 | 1.3071 | -0.1921 | 0.9000 | 1.4992 | -19.1949 | -14.1771 | -1.0729 | -1.0688 |
| 0.284 | 3.2 | 400 | 0.2314 | 1.5290 | -0.3317 | 0.9100 | 1.8607 | -19.6603 | -13.4374 | -1.0713 | -1.0663 |
| 0.1771 | 3.4 | 425 | 0.1947 | 1.6285 | -0.6031 | 0.9300 | 2.2317 | -20.5652 | -13.1057 | -1.0703 | -1.0645 |
| 0.2167 | 3.6 | 450 | 0.1840 | 1.5135 | -0.9678 | 0.9200 | 2.4813 | -21.7808 | -13.4893 | -1.0704 | -1.0643 |
| 0.1395 | 3.8 | 475 | 0.1771 | 1.5622 | -1.1304 | 0.9200 | 2.6926 | -22.3226 | -13.3267 | -1.0702 | -1.0636 |
| 0.2148 | 4.0 | 500 | 0.1874 | 1.6351 | -1.2063 | 0.9300 | 2.8414 | -22.5757 | -13.0837 | -1.0669 | -1.0600 |
| 0.0286 | 4.2 | 525 | 0.1577 | 1.6211 | -1.6079 | 0.9200 | 3.2289 | -23.9142 | -13.1307 | -1.0665 | -1.0588 |
| 0.1347 | 4.4 | 550 | 0.1587 | 1.6299 | -1.7763 | 0.9200 | 3.4062 | -24.4757 | -13.1011 | -1.0654 | -1.0575 |
| 0.0575 | 4.6 | 575 | 0.1473 | 1.6935 | -1.9790 | 0.9200 | 3.6725 | -25.1514 | -12.8892 | -1.0648 | -1.0564 |
| 0.158 | 4.8 | 600 | 0.1509 | 1.6992 | -2.0296 | 0.9300 | 3.7288 | -25.3200 | -12.8703 | -1.0651 | -1.0566 |
| 0.0607 | 5.0 | 625 | 0.1475 | 1.6735 | -2.1752 | 0.9400 | 3.8488 | -25.8055 | -12.9557 | -1.0647 | -1.0558 |
| 0.1122 | 5.2 | 650 | 0.1404 | 1.7217 | -2.2066 | 0.9400 | 3.9283 | -25.9101 | -12.7953 | -1.0637 | -1.0549 |
| 0.1517 | 5.4 | 675 | 0.1448 | 1.7525 | -2.2515 | 0.9400 | 4.0040 | -26.0596 | -12.6925 | -1.0628 | -1.0538 |
| 0.0413 | 5.6 | 700 | 0.1418 | 1.7609 | -2.3002 | 0.9400 | 4.0611 | -26.2220 | -12.6647 | -1.0635 | -1.0545 |
| 0.0528 | 5.8 | 725 | 0.1358 | 1.7517 | -2.3539 | 0.9200 | 4.1056 | -26.4012 | -12.6954 | -1.0634 | -1.0542 |
| 0.243 | 6.0 | 750 | 0.1356 | 1.7676 | -2.3804 | 0.9300 | 4.1480 | -26.4894 | -12.6424 | -1.0623 | -1.0531 |
| 0.1309 | 6.2 | 775 | 0.1522 | 1.7393 | -2.3923 | 0.9300 | 4.1315 | -26.5290 | -12.7367 | -1.0632 | -1.0540 |
| 0.1943 | 6.4 | 800 | 0.1425 | 1.7703 | -2.3997 | 0.9300 | 4.1699 | -26.5535 | -12.6333 | -1.0628 | -1.0534 |
| 0.0754 | 6.6 | 825 | 0.1308 | 1.7709 | -2.4143 | 0.9400 | 4.1852 | -26.6025 | -12.6314 | -1.0623 | -1.0529 |
| 0.0603 | 6.8 | 850 | 0.1401 | 1.7587 | -2.4040 | 0.9300 | 4.1627 | -26.5682 | -12.6720 | -1.0630 | -1.0537 |
| 0.0603 | 7.0 | 875 | 0.1402 | 1.7651 | -2.3976 | 0.9300 | 4.1627 | -26.5466 | -12.6505 | -1.0636 | -1.0543 |
| 0.046 | 7.2 | 900 | 0.1397 | 1.7519 | -2.4254 | 0.9300 | 4.1773 | -26.6393 | -12.6945 | -1.0631 | -1.0538 |
| 0.2102 | 7.4 | 925 | 0.1390 | 1.7602 | -2.4168 | 0.9300 | 4.1770 | -26.6105 | -12.6669 | -1.0631 | -1.0537 |
| 0.2116 | 7.6 | 950 | 0.1392 | 1.7582 | -2.4140 | 0.9300 | 4.1722 | -26.6014 | -12.6736 | -1.0630 | -1.0536 |
| 0.094 | 7.8 | 975 | 0.1392 | 1.7582 | -2.4140 | 0.9300 | 4.1722 | -26.6014 | -12.6736 | -1.0630 | -1.0536 |
| 0.1121 | 8.0 | 1000 | 0.1392 | 1.7582 | -2.4140 | 0.9300 | 4.1722 | -26.6014 | -12.6736 | -1.0630 | -1.0536 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.0.0+cu117
- Datasets 2.19.1
- Tokenizers 0.19.1
|
Bensake/balianthampers | Bensake | 2024-05-14T18:23:21Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-05-09T12:39:01Z | ---
license: apache-2.0
---
|
neopolita/sfr-iterative-dpo-llama-3-8b-r-gguf | neopolita | 2024-05-14T18:22:57Z | 17 | 1 | null | [
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-14T17:36:10Z | ---
{}
---
# GGUF quants for [**Salesforce/SFR-Iterative-DPO-LLaMA-3-8B-R**](https://huggingface.co/Salesforce/SFR-Iterative-DPO-LLaMA-3-8B-R) using [llama.cpp](https://github.com/ggerganov/llama.cpp)
**Terms of Use**: Please check the [**original model**](https://huggingface.co/Salesforce/SFR-Iterative-DPO-LLaMA-3-8B-R)
<picture>
<img alt="cthulhu" src="https://huggingface.co/neopolita/common/resolve/main/profile.png">
</picture>
## Quants
* `q2_k`: Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors.
* `q3_k_s`: Uses Q3_K for all tensors
* `q3_k_m`: Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K
* `q3_k_l`: Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K
* `q4_0`: Original quant method, 4-bit.
* `q4_1`: Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
* `q4_k_s`: Uses Q4_K for all tensors
* `q4_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K
* `q5_0`: Higher accuracy, higher resource usage and slower inference.
* `q5_1`: Even higher accuracy, resource usage and slower inference.
* `q5_k_s`: Uses Q5_K for all tensors
* `q5_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K
* `q6_k`: Uses Q8_K for all tensors
* `q8_0`: Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. |
mlx-community/granite-34b-code-base-4bit | mlx-community | 2024-05-14T18:21:39Z | 8 | 1 | transformers | [
"transformers",
"safetensors",
"gpt_bigcode",
"text-generation",
"code",
"granite",
"mlx",
"dataset:codeparrot/github-code-clean",
"dataset:bigcode/starcoderdata",
"dataset:open-web-math/open-web-math",
"dataset:math-ai/StackMathQA",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-06T21:40:14Z | ---
license: apache-2.0
library_name: transformers
tags:
- code
- granite
- mlx
datasets:
- codeparrot/github-code-clean
- bigcode/starcoderdata
- open-web-math/open-web-math
- math-ai/StackMathQA
metrics:
- code_eval
pipeline_tag: text-generation
inference: true
model-index:
- name: granite-34b-code-base
results:
- task:
type: text-generation
dataset:
name: MBPP
type: mbpp
metrics:
- type: pass@1
value: 47.2
name: pass@1
- task:
type: text-generation
dataset:
name: MBPP+
type: evalplus/mbppplus
metrics:
- type: pass@1
value: 53.1
name: pass@1
- task:
type: text-generation
dataset:
name: HumanEvalSynthesis(Python)
type: bigcode/humanevalpack
metrics:
- type: pass@1
value: 48.2
name: pass@1
- type: pass@1
value: 54.9
name: pass@1
- type: pass@1
value: 61.6
name: pass@1
- type: pass@1
value: 40.2
name: pass@1
- type: pass@1
value: 50.0
name: pass@1
- type: pass@1
value: 39.6
name: pass@1
- type: pass@1
value: 42.7
name: pass@1
- type: pass@1
value: 26.2
name: pass@1
- type: pass@1
value: 47.0
name: pass@1
- type: pass@1
value: 26.8
name: pass@1
- type: pass@1
value: 36.6
name: pass@1
- type: pass@1
value: 25.0
name: pass@1
- type: pass@1
value: 20.1
name: pass@1
- type: pass@1
value: 30.5
name: pass@1
- type: pass@1
value: 40.9
name: pass@1
- type: pass@1
value: 34.1
name: pass@1
- type: pass@1
value: 39.0
name: pass@1
- type: pass@1
value: 12.2
name: pass@1
---
# mlx-community/granite-34b-code-base-4bit
The Model [mlx-community/granite-34b-code-base-4bit](https://huggingface.co/mlx-community/granite-34b-code-base-4bit) was converted to MLX format from [ibm-granite/granite-34b-code-base](https://huggingface.co/ibm-granite/granite-34b-code-base) using mlx-lm version **0.13.0**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/granite-34b-code-base-4bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
timtt/mlx-community-goliath-120b | timtt | 2024-05-14T18:20:50Z | 7 | 2 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-09T17:54:54Z | ---
license: llama2
---
Apple MLX version of the highly popular merged [goliath-120b](https://huggingface.co/alpindale/goliath-120b).
You will need a decent machine w/ plenty of RAM to run this.
Contact me at <[email protected]> |
mlx-community/granite-34b-code-instruct-4bit | mlx-community | 2024-05-14T18:19:18Z | 8 | 1 | transformers | [
"transformers",
"safetensors",
"gpt_bigcode",
"text-generation",
"code",
"granite",
"mlx",
"conversational",
"dataset:bigcode/commitpackft",
"dataset:TIGER-Lab/MathInstruct",
"dataset:meta-math/MetaMathQA",
"dataset:glaiveai/glaive-code-assistant-v3",
"dataset:glaive-function-calling-v2",
"dataset:bugdaryan/sql-create-context-instruction",
"dataset:garage-bAInd/Open-Platypus",
"dataset:nvidia/HelpSteer",
"base_model:ibm-granite/granite-34b-code-base-8k",
"base_model:finetune:ibm-granite/granite-34b-code-base-8k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-06T21:40:41Z | ---
license: apache-2.0
library_name: transformers
tags:
- code
- granite
- mlx
base_model: ibm-granite/granite-34b-code-base
datasets:
- bigcode/commitpackft
- TIGER-Lab/MathInstruct
- meta-math/MetaMathQA
- glaiveai/glaive-code-assistant-v3
- glaive-function-calling-v2
- bugdaryan/sql-create-context-instruction
- garage-bAInd/Open-Platypus
- nvidia/HelpSteer
metrics:
- code_eval
pipeline_tag: text-generation
inference: true
model-index:
- name: granite-34b-code-instruct
results:
- task:
type: text-generation
dataset:
name: HumanEvalSynthesis(Python)
type: bigcode/humanevalpack
metrics:
- type: pass@1
value: 62.2
name: pass@1
- type: pass@1
value: 56.7
name: pass@1
- type: pass@1
value: 62.8
name: pass@1
- type: pass@1
value: 47.6
name: pass@1
- type: pass@1
value: 57.9
name: pass@1
- type: pass@1
value: 41.5
name: pass@1
- type: pass@1
value: 53.0
name: pass@1
- type: pass@1
value: 45.1
name: pass@1
- type: pass@1
value: 50.6
name: pass@1
- type: pass@1
value: 36.0
name: pass@1
- type: pass@1
value: 42.7
name: pass@1
- type: pass@1
value: 23.8
name: pass@1
- type: pass@1
value: 54.9
name: pass@1
- type: pass@1
value: 47.6
name: pass@1
- type: pass@1
value: 55.5
name: pass@1
- type: pass@1
value: 51.2
name: pass@1
- type: pass@1
value: 47.0
name: pass@1
- type: pass@1
value: 45.1
name: pass@1
---
# mlx-community/granite-34b-code-instruct-4bit
The Model [mlx-community/granite-34b-code-instruct-4bit](https://huggingface.co/mlx-community/granite-34b-code-instruct-4bit) was converted to MLX format from [ibm-granite/granite-34b-code-instruct](https://huggingface.co/ibm-granite/granite-34b-code-instruct) using mlx-lm version **0.13.0**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/granite-34b-code-instruct-4bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
cu-kairos/propbank_srl_seq2seq_t5_large | cu-kairos | 2024-05-14T18:17:02Z | 158 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"doi:10.57967/hf/2635",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-04-27T17:51:39Z | ---
license: apache-2.0
---
### Using .generate()
```python
from transformers import GenerationConfig, T5ForConditionalGeneration, T5Tokenizer
model_name = "cu-kairos/propbank_srl_seq2seq_t5_large"
model = T5ForConditionalGeneration.from_pretrained(model_name)
tokenizer = T5Tokenizer.from_pretrained(model_name)
generation_config = GenerationConfig.from_pretrained(model_name)
tokenized_inputs = tokenizer(["SRL for [put]: That fund was [put] together by Blackstone Group ."], return_tensors="pt")
outputs = model.generate(**tokenized_inputs, generation_config=generation_config)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
# ['ARG-1: That fund | ARG-2: together | ARG-0: by Blackstone Group ']
```
### Using pipeline
```python
from transformers import pipeline
srl = pipeline("text2text-generation", "cu-kairos/propbank_srl_seq2seq_t5_large")
print(srl(["SRL for [put]: That fund was [put] together by Blackstone Group ."]))
# [{'generated_text': 'ARG-1: That fund | ARG-2: together | ARG-0: by Blackstone Group '}]
``` |
ezuryy/flan-t5-small_article_summary_lora | ezuryy | 2024-05-14T18:15:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-14T18:15:10Z | ---
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]
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[More Information Needed]
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<!-- 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] |
yifanxie/angry-pelican-2-1 | yifanxie | 2024-05-14T18:14:48Z | 145 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"conversational",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-05-14T18:12:41Z | ---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: false
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [google/gemma-1.1-2b-it](https://huggingface.co/google/gemma-1.1-2b-it)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed.
```bash
pip install transformers==4.40.1
```
Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo.
- Either leave `token=True` in the `pipeline` and login to hugginface_hub by running
```python
import huggingface_hub
huggingface_hub.login(<ACCESS_TOKEN>)
```
- Or directly pass your <ACCESS_TOKEN> to `token` in the `pipeline`
```python
from transformers import pipeline
generate_text = pipeline(
model="yifanxie/angry-pelican-2-1",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
token=True,
)
# generate configuration can be modified to your needs
# generate_text.model.generation_config.min_new_tokens = 2
# generate_text.model.generation_config.max_new_tokens = 256
# generate_text.model.generation_config.do_sample = False
# generate_text.model.generation_config.num_beams = 1
# generate_text.model.generation_config.temperature = float(0.0)
# generate_text.model.generation_config.repetition_penalty = float(1.0)
res = generate_text(
"Why is drinking water so healthy?",
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?<eos><|answer|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"yifanxie/angry-pelican-2-1",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"yifanxie/angry-pelican-2-1",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
# generate configuration can be modified to your needs
# generate_text.model.generation_config.min_new_tokens = 2
# generate_text.model.generation_config.max_new_tokens = 256
# generate_text.model.generation_config.do_sample = False
# generate_text.model.generation_config.num_beams = 1
# generate_text.model.generation_config.temperature = float(0.0)
# generate_text.model.generation_config.repetition_penalty = float(1.0)
res = generate_text(
"Why is drinking water so healthy?",
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "yifanxie/angry-pelican-2-1" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?<eos><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
# model.generation_config.min_new_tokens = 2
# model.generation_config.max_new_tokens = 256
# model.generation_config.do_sample = False
# model.generation_config.num_beams = 1
# model.generation_config.temperature = float(0.0)
# model.generation_config.repetition_penalty = float(1.0)
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
GemmaForCausalLM(
(model): GemmaModel(
(embed_tokens): Embedding(256000, 2048, padding_idx=0)
(layers): ModuleList(
(0-17): 18 x GemmaDecoderLayer(
(self_attn): GemmaSdpaAttention(
(q_proj): Linear(in_features=2048, out_features=2048, bias=False)
(k_proj): Linear(in_features=2048, out_features=256, bias=False)
(v_proj): Linear(in_features=2048, out_features=256, bias=False)
(o_proj): Linear(in_features=2048, out_features=2048, bias=False)
(rotary_emb): GemmaRotaryEmbedding()
)
(mlp): GemmaMLP(
(gate_proj): Linear(in_features=2048, out_features=16384, bias=False)
(up_proj): Linear(in_features=2048, out_features=16384, bias=False)
(down_proj): Linear(in_features=16384, out_features=2048, bias=False)
(act_fn): PytorchGELUTanh()
)
(input_layernorm): GemmaRMSNorm()
(post_attention_layernorm): GemmaRMSNorm()
)
)
(norm): GemmaRMSNorm()
)
(lm_head): Linear(in_features=2048, out_features=256000, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it. |
jack8885/gemma-2b-flock-1715710119 | jack8885 | 2024-05-14T18:12:25Z | 144 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-14T18:08:40Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**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] |
VanCan23/SFTDPO_1epoch_merged | VanCan23 | 2024-05-14T18:10:11Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-14T17:43:58Z | ---
license: apache-2.0
---
|
terry69/mistral_poe_other | terry69 | 2024-05-14T18:09:45Z | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"mistral",
"alignment-handbook",
"trl",
"sft",
"generated_from_trainer",
"dataset:HuggingFaceH4/ultrachat_200k",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2024-05-14T16:26:27Z | ---
license: apache-2.0
library_name: peft
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
base_model: mistralai/Mistral-7B-v0.1
datasets:
- HuggingFaceH4/ultrachat_200k
model-index:
- name: mistral_poe_other
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. -->
# mistral_poe_other
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the HuggingFaceH4/ultrachat_200k 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: 4
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0076 | 1.0 | 325 | nan |
### Framework versions
- PEFT 0.7.1
- Transformers 4.39.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2 |
TaniaSF/Taxi | TaniaSF | 2024-05-14T18:09:23Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2024-05-14T18:09:20Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="TaniaSF/Taxi", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Kota123/gemma-7b | Kota123 | 2024-05-14T18:09:21Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"gguf",
"gemma",
"text-generation",
"arxiv:2305.14314",
"arxiv:2312.11805",
"arxiv:2009.03300",
"arxiv:1905.07830",
"arxiv:1911.11641",
"arxiv:1904.09728",
"arxiv:1905.10044",
"arxiv:1907.10641",
"arxiv:1811.00937",
"arxiv:1809.02789",
"arxiv:1911.01547",
"arxiv:1705.03551",
"arxiv:2107.03374",
"arxiv:2108.07732",
"arxiv:2110.14168",
"arxiv:2304.06364",
"arxiv:2206.04615",
"arxiv:1804.06876",
"arxiv:2110.08193",
"arxiv:2009.11462",
"arxiv:2101.11718",
"arxiv:1804.09301",
"arxiv:2109.07958",
"arxiv:2203.09509",
"license:gemma",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-14T17:39:29Z | ---
library_name: transformers
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: >-
To access Gemma on Hugging Face, you’re required to review and agree to
Google’s usage license. To do this, please ensure you’re logged-in to Hugging
Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
license: gemma
---
# Gemma Model Card
**Model Page**: [Gemma](https://ai.google.dev/gemma/docs)
This model card corresponds to the 7B base version of the Gemma model. You can also visit the model card of the [2B base model](https://huggingface.co/google/gemma-2b), [7B instruct model](https://huggingface.co/google/gemma-7b-it), and [2B instruct model](https://huggingface.co/google/gemma-2b-it).
**Resources and Technical Documentation**:
* [Gemma Technical Report](https://storage.googleapis.com/deepmind-media/gemma/gemma-report.pdf)
* [Responsible Generative AI Toolkit](https://ai.google.dev/responsible)
* [Gemma on Kaggle](https://www.kaggle.com/models/google/gemma)
* [Gemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335?version=gemma-7b-gg-hf)
**Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent)
**Authors**: Google
## Model Information
Summary description and brief definition of inputs and outputs.
### Description
Gemma is a family of lightweight, state-of-the-art open models from Google,
built from the same research and technology used to create the Gemini models.
They are text-to-text, decoder-only large language models, available in English,
with open weights, pre-trained variants, and instruction-tuned variants. Gemma
models are well-suited for a variety of text generation tasks, including
question answering, summarization, and reasoning. Their relatively small size
makes it possible to deploy them in environments with limited resources such as
a laptop, desktop or your own cloud infrastructure, democratizing access to
state of the art AI models and helping foster innovation for everyone.
### Context Length
Models are trained on a context length of 8192 tokens.
### Usage
Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase.
#### Fine-tuning examples
You can find fine-tuning notebooks under the [`examples/` directory](https://huggingface.co/google/gemma-7b/tree/main/examples). We provide:
* A script to perform Supervised Fine-Tuning (SFT) on UltraChat dataset using [QLoRA](https://huggingface.co/papers/2305.14314)
* A script to perform SFT using FSDP on TPU devices
* A notebook that you can run on a free-tier Google Colab instance to perform SFT on English quotes dataset. You can also find the copy of the notebook [here](https://github.com/huggingface/notebooks/blob/main/peft/gemma_7b_english_quotes.ipynb).
#### Running the model on a CPU
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-7b")
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Running the model on a single / multi GPU
```python
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", device_map="auto")
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Running the model on a GPU using different precisions
* _Using `torch.float16`_
```python
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", device_map="auto", revision="float16")
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
* _Using `torch.bfloat16`_
```python
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", device_map="auto", torch_dtype=torch.bfloat16)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Quantized Versions through `bitsandbytes`
* _Using 8-bit precision (int8)_
```python
# pip install bitsandbytes accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(load_in_8bit=True)
tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", quantization_config=quantization_config)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
* _Using 4-bit precision_
```python
# pip install bitsandbytes accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(load_in_4bit=True)
tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", quantization_config=quantization_config)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Other optimizations
* _Flash Attention 2_
First make sure to install `flash-attn` in your environment `pip install flash-attn`
```diff
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16,
+ attn_implementation="flash_attention_2"
).to(0)
```
### Inputs and outputs
* **Input:** Text string, such as a question, a prompt, or a document to be
summarized.
* **Output:** Generated English-language text in response to the input, such
as an answer to a question, or a summary of a document.
## Model Data
Data used for model training and how the data was processed.
### Training Dataset
These models were trained on a dataset of text data that includes a wide variety
of sources, totaling 6 trillion tokens. Here are the key components:
* Web Documents: A diverse collection of web text ensures the model is exposed
to a broad range of linguistic styles, topics, and vocabulary. Primarily
English-language content.
* Code: Exposing the model to code helps it to learn the syntax and patterns of
programming languages, which improves its ability to generate code or
understand code-related questions.
* Mathematics: Training on mathematical text helps the model learn logical
reasoning, symbolic representation, and to address mathematical queries.
The combination of these diverse data sources is crucial for training a powerful
language model that can handle a wide variety of different tasks and text
formats.
### Data Preprocessing
Here are the key data cleaning and filtering methods applied to the training
data:
* CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was
applied at multiple stages in the data preparation process to ensure the
exclusion of harmful and illegal content
* Sensitive Data Filtering: As part of making Gemma pre-trained models safe and
reliable, automated techniques were used to filter out certain personal
information and other sensitive data from training sets.
* Additional methods: Filtering based on content quality and safely in line with
[our policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11).
## Implementation Information
Details about the model internals.
### Hardware
Gemma was trained using the latest generation of
[Tensor Processing Unit (TPU)](https://cloud.google.com/tpu/docs/intro-to-tpu) hardware (TPUv5e).
Training large language models requires significant computational power. TPUs,
designed specifically for matrix operations common in machine learning, offer
several advantages in this domain:
* Performance: TPUs are specifically designed to handle the massive computations
involved in training LLMs. They can speed up training considerably compared to
CPUs.
* Memory: TPUs often come with large amounts of high-bandwidth memory, allowing
for the handling of large models and batch sizes during training. This can
lead to better model quality.
* Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for
handling the growing complexity of large foundation models. You can distribute
training across multiple TPU devices for faster and more efficient processing.
* Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective
solution for training large models compared to CPU-based infrastructure,
especially when considering the time and resources saved due to faster
training.
* These advantages are aligned with
[Google's commitments to operate sustainably](https://sustainability.google/operating-sustainably/).
### Software
Training was done using [JAX](https://github.com/google/jax) and [ML Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture).
JAX allows researchers to take advantage of the latest generation of hardware,
including TPUs, for faster and more efficient training of large models.
ML Pathways is Google's latest effort to build artificially intelligent systems
capable of generalizing across multiple tasks. This is specially suitable for
[foundation models](https://ai.google/discover/foundation-models/), including large language models like
these ones.
Together, JAX and ML Pathways are used as described in the
[paper about the Gemini family of models](https://arxiv.org/abs/2312.11805); "the 'single
controller' programming model of Jax and Pathways allows a single Python
process to orchestrate the entire training run, dramatically simplifying the
development workflow."
## Evaluation
Model evaluation metrics and results.
### Benchmark Results
These models were evaluated against a large collection of different datasets and
metrics to cover different aspects of text generation:
| Benchmark | Metric | 2B Params | 7B Params |
| ------------------------------ | ------------- | ----------- | --------- |
| [MMLU](https://arxiv.org/abs/2009.03300) | 5-shot, top-1 | 42.3 | 64.3 |
| [HellaSwag](https://arxiv.org/abs/1905.07830) | 0-shot |71.4 | 81.2 |
| [PIQA](https://arxiv.org/abs/1911.11641) | 0-shot | 77.3 | 81.2 |
| [SocialIQA](https://arxiv.org/abs/1904.09728) | 0-shot | 49.7 | 51.8 |
| [BooIQ](https://arxiv.org/abs/1905.10044) | 0-shot | 69.4 | 83.2 |
| [WinoGrande](https://arxiv.org/abs/1907.10641) | partial score | 65.4 | 72.3 |
| [CommonsenseQA](https://arxiv.org/abs/1811.00937) | 7-shot | 65.3 | 71.3 |
| [OpenBookQA](https://arxiv.org/abs/1809.02789) | | 47.8 | 52.8 |
| [ARC-e](https://arxiv.org/abs/1911.01547) | | 73.2 | 81.5 |
| [ARC-c](https://arxiv.org/abs/1911.01547) | | 42.1 | 53.2 |
| [TriviaQA](https://arxiv.org/abs/1705.03551) | 5-shot | 53.2 | 63.4 |
| [Natural Questions](https://github.com/google-research-datasets/natural-questions) | 5-shot | 12.5 | 23 |
| [HumanEval](https://arxiv.org/abs/2107.03374) | pass@1 | 22.0 | 32.3 |
| [MBPP](https://arxiv.org/abs/2108.07732) | 3-shot | 29.2 | 44.4 |
| [GSM8K](https://arxiv.org/abs/2110.14168) | maj@1 | 17.7 | 46.4 |
| [MATH](https://arxiv.org/abs/2108.07732) | 4-shot | 11.8 | 24.3 |
| [AGIEval](https://arxiv.org/abs/2304.06364) | | 24.2 | 41.7 |
| [BIG-Bench](https://arxiv.org/abs/2206.04615) | | 35.2 | 55.1 |
| ------------------------------ | ------------- | ----------- | --------- |
| **Average** | | **45.0** | **56.9** |
## Ethics and Safety
Ethics and safety evaluation approach and results.
### Evaluation Approach
Our evaluation methods include structured evaluations and internal red-teaming
testing of relevant content policies. Red-teaming was conducted by a number of
different teams, each with different goals and human evaluation metrics. These
models were evaluated against a number of different categories relevant to
ethics and safety, including:
* Text-to-Text Content Safety: Human evaluation on prompts covering safety
policies including child sexual abuse and exploitation, harassment, violence
and gore, and hate speech.
* Text-to-Text Representational Harms: Benchmark against relevant academic
datasets such as [WinoBias](https://arxiv.org/abs/1804.06876) and [BBQ Dataset](https://arxiv.org/abs/2110.08193v2).
* Memorization: Automated evaluation of memorization of training data, including
the risk of personally identifiable information exposure.
* Large-scale harm: Tests for "dangerous capabilities," such as chemical,
biological, radiological, and nuclear (CBRN) risks.
### Evaluation Results
The results of ethics and safety evaluations are within acceptable thresholds
for meeting [internal policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11) for categories such as child
safety, content safety, representational harms, memorization, large-scale harms.
On top of robust internal evaluations, the results of well known safety
benchmarks like BBQ, BOLD, Winogender, Winobias, RealToxicity, and TruthfulQA
are shown here.
| Benchmark | Metric | 2B Params | 7B Params |
| ------------------------------ | ------------- | ----------- | --------- |
| [RealToxicity](https://arxiv.org/abs/2009.11462) | average | 6.86 | 7.90 |
| [BOLD](https://arxiv.org/abs/2101.11718) | | 45.57 | 49.08 |
| [CrowS-Pairs](https://aclanthology.org/2020.emnlp-main.154/) | top-1 | 45.82 | 51.33 |
| [BBQ Ambig](https://arxiv.org/abs/2110.08193v2) | 1-shot, top-1 | 62.58 | 92.54 |
| [BBQ Disambig](https://arxiv.org/abs/2110.08193v2) | top-1 | 54.62 | 71.99 |
| [Winogender](https://arxiv.org/abs/1804.09301) | top-1 | 51.25 | 54.17 |
| [TruthfulQA](https://arxiv.org/abs/2109.07958) | | 44.84 | 31.81 |
| [Winobias 1_2](https://arxiv.org/abs/1804.06876) | | 56.12 | 59.09 |
| [Winobias 2_2](https://arxiv.org/abs/1804.06876) | | 91.10 | 92.23 |
| [Toxigen](https://arxiv.org/abs/2203.09509) | | 29.77 | 39.59 |
| ------------------------------ | ------------- | ----------- | --------- |
## Usage and Limitations
These models have certain limitations that users should be aware of.
### Intended Usage
Open Large Language Models (LLMs) have a wide range of applications across
various industries and domains. The following list of potential uses is not
comprehensive. The purpose of this list is to provide contextual information
about the possible use-cases that the model creators considered as part of model
training and development.
* Content Creation and Communication
* Text Generation: These models can be used to generate creative text formats
such as poems, scripts, code, marketing copy, and email drafts.
* Chatbots and Conversational AI: Power conversational interfaces for customer
service, virtual assistants, or interactive applications.
* Text Summarization: Generate concise summaries of a text corpus, research
papers, or reports.
* Research and Education
* Natural Language Processing (NLP) Research: These models can serve as a
foundation for researchers to experiment with NLP techniques, develop
algorithms, and contribute to the advancement of the field.
* Language Learning Tools: Support interactive language learning experiences,
aiding in grammar correction or providing writing practice.
* Knowledge Exploration: Assist researchers in exploring large bodies of text
by generating summaries or answering questions about specific topics.
### Limitations
* Training Data
* The quality and diversity of the training data significantly influence the
model's capabilities. Biases or gaps in the training data can lead to
limitations in the model's responses.
* The scope of the training dataset determines the subject areas the model can
handle effectively.
* Context and Task Complexity
* LLMs are better at tasks that can be framed with clear prompts and
instructions. Open-ended or highly complex tasks might be challenging.
* A model's performance can be influenced by the amount of context provided
(longer context generally leads to better outputs, up to a certain point).
* Language Ambiguity and Nuance
* Natural language is inherently complex. LLMs might struggle to grasp subtle
nuances, sarcasm, or figurative language.
* Factual Accuracy
* LLMs generate responses based on information they learned from their
training datasets, but they are not knowledge bases. They may generate
incorrect or outdated factual statements.
* Common Sense
* LLMs rely on statistical patterns in language. They might lack the ability
to apply common sense reasoning in certain situations.
### Ethical Considerations and Risks
The development of large language models (LLMs) raises several ethical concerns.
In creating an open model, we have carefully considered the following:
* Bias and Fairness
* LLMs trained on large-scale, real-world text data can reflect socio-cultural
biases embedded in the training material. These models underwent careful
scrutiny, input data pre-processing described and posterior evaluations
reported in this card.
* Misinformation and Misuse
* LLMs can be misused to generate text that is false, misleading, or harmful.
* Guidelines are provided for responsible use with the model, see the
[Responsible Generative AI Toolkit](http://ai.google.dev/gemma/responsible).
* Transparency and Accountability:
* This model card summarizes details on the models' architecture,
capabilities, limitations, and evaluation processes.
* A responsibly developed open model offers the opportunity to share
innovation by making LLM technology accessible to developers and researchers
across the AI ecosystem.
Risks identified and mitigations:
* Perpetuation of biases: It's encouraged to perform continuous monitoring
(using evaluation metrics, human review) and the exploration of de-biasing
techniques during model training, fine-tuning, and other use cases.
* Generation of harmful content: Mechanisms and guidelines for content safety
are essential. Developers are encouraged to exercise caution and implement
appropriate content safety safeguards based on their specific product policies
and application use cases.
* Misuse for malicious purposes: Technical limitations and developer and
end-user education can help mitigate against malicious applications of LLMs.
Educational resources and reporting mechanisms for users to flag misuse are
provided. Prohibited uses of Gemma models are outlined in the
[Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy).
* Privacy violations: Models were trained on data filtered for removal of PII
(Personally Identifiable Information). Developers are encouraged to adhere to
privacy regulations with privacy-preserving techniques.
### Benefits
At the time of release, this family of models provides high-performance open
large language model implementations designed from the ground up for Responsible
AI development compared to similarly sized models.
Using the benchmark evaluation metrics described in this document, these models
have shown to provide superior performance to other, comparably-sized open model
alternatives. |
jukofyork/Dark-Miqu-70B | jukofyork | 2024-05-14T18:08:59Z | 68 | 28 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"arxiv:2403.19522",
"base_model:152334H/miqu-1-70b-sf",
"base_model:merge:152334H/miqu-1-70b-sf",
"base_model:Sao10K/Euryale-1.3-L2-70B",
"base_model:merge:Sao10K/Euryale-1.3-L2-70B",
"base_model:Sao10K/WinterGoddess-1.4x-70B-L2",
"base_model:merge:Sao10K/WinterGoddess-1.4x-70B-L2",
"base_model:sophosympatheia/Midnight-Rose-70B-v2.0.3",
"base_model:merge:sophosympatheia/Midnight-Rose-70B-v2.0.3",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-04T00:53:39Z | ---
base_model:
- 152334H/miqu-1-70b-sf
- sophosympatheia/Midnight-Rose-70B-v2.0.3
- Sao10K/Euryale-1.3-L2-70B
- Sao10K/WinterGoddess-1.4x-70B-L2
library_name: transformers
tags:
- mergekit
- merge
license: other
---
A "dark" creative writing model with 32k context. Based off [miqu-1-70b](https://huggingface.co/miqudev/miqu-1-70b) but with greatly reduced "positivity" and "-isms". If you want happy endings, look elsewhere!
This model **excels** at writing Dark/Grimdark fantasy (see examples below).
***NOTE***: *This model has now been merged with [Dawn-Miqu-70B](https://huggingface.co/jukofyork/Dawn-Miqu-70B) to create [Deep-Miqu-103B](https://huggingface.co/jukofyork/Deep-Miqu-103B) and [Deep-Miqu-120B](https://huggingface.co/jukofyork/Deep-Miqu-120B).*
***NOTE***: *For a full range of GGUF quants kindly provided by @mradermacher, see: [Static](https://huggingface.co/mradermacher/Dark-Miqu-70B-GGUF) and [IMatrix](https://huggingface.co/mradermacher/Dark-Miqu-70B-i1-GGUF).*
# Model background
Created using [Mergekit](https://github.com/arcee-ai/mergekit) and based on @sophosympatheia's template for [Midnight-Miqu-70B-v1.0](https://huggingface.co/sophosympatheia/Midnight-Miqu-70B-v1.0).
This model has a lower perplexity compared to [Midnight-Miqu-70B-v1.0](https://huggingface.co/sophosympatheia/Midnight-Miqu-70B-v1.0) (`'4.08 +/- 0.02'` vs `'4.02 +/- 0.02'`). It also generates longer responses when prompted.
The model was created in two stages:
- First, three "Midnight-Miqu-esque" models were produced using spherical interpolation (slerp) merges between [miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) and each of the following models: [Midnight-Rose-70B-v2.0.3](https://huggingface.co/sophosympatheia/Midnight-Rose-70B-v2.0.3), [Euryale-1.3-L2-70B](https://huggingface.co/Sao10K/Euryale-1.3-L2-70B) and [WinterGoddess-1.4x-70B-L2](https://huggingface.co/Sao10K/WinterGoddess-1.4x-70B-L2). These models were selected for their dark, imaginative writing styles. Various slerp-merges between [miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) and other models were also experimented with, but these three yielded the darkest creative writing results.
- In the second stage, the three slerp-merged models were combined into a single model using the '[Model Stock](https://arxiv.org/abs/2403.19522)' method, with [miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) serving as the base model.
# Prompting format
Vicuna format is preferred:
```
USER: {prompt} ASSISTANT:
```
Mistral and Alpaca formats are also supported:
```
[INST] {prompt} [/INST]
```
```
### Instruction:
{prompt}
### Response:
```
# Licence and usage restrictions
[miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) is a dequantized version of the [miqu-1-70b](https://huggingface.co/miqudev/miqu-1-70b) model leaked from MistralAI. All miqu-derived models, including this merge, are suitable for non-commercial, personal use only.
# Mergekit configuration
The following YAML configuration was used to produce this model:
```yaml
name: midnight-miqu-70b
models:
- model: 152334H/miqu-1-70b-sf
- model: sophosympatheia/Midnight-Rose-70B-v2.0.3
base_model: 152334H/miqu-1-70b-sf
merge_method: slerp
parameters:
t:
- value: [0, 0, 0.2, 0.3, 0.4, 0.5, 0.4, 0.3, 0.2, 0, 0]
embed_slerp: true
tokenizer_source: model:miqu-1-70b-sf
dtype: float16
---
name: euryale-miqu-70b
models:
- model: 152334H/miqu-1-70b-sf
- model: Sao10K/Euryale-1.3-L2-70B
base_model: 152334H/miqu-1-70b-sf
merge_method: slerp
parameters:
t:
- value: [0, 0, 0.2, 0.3, 0.4, 0.5, 0.4, 0.3, 0.2, 0, 0]
embed_slerp: true
tokenizer_source: model:miqu-1-70b-sf
dtype: float16
---
name: winter-miqu-70b
models:
- model: 152334H/miqu-1-70b-sf
- model: Sao10K/WinterGoddess-1.4x-70B-L2
base_model: 152334H/miqu-1-70b-sf
merge_method: slerp
parameters:
t:
- value: [0, 0, 0.2, 0.3, 0.4, 0.5, 0.4, 0.3, 0.2, 0, 0]
embed_slerp: true
tokenizer_source: model:miqu-1-70b-sf
dtype: float16
---
name: dark-miqu-70b
models:
- model: 152334H/miqu-1-70b-sf
- model: midnight-miqu-70b
- model: euryale-miqu-70b
- model: winter-miqu-70b
base_model: 152334H/miqu-1-70b-sf
merge_method: model_stock
dtype: float16
```
## Key configuration details:
- '`merge_method: slerp`' uses spherical linear interpolation for merging models.
- '`parameters: t`' controls the interpolation ratios between models.
- '`embed_slerp: true`' applies slerp to the embedding layers.
- '`merge_method: model_stock`' uses the '[Model Stock](https://arxiv.org/abs/2403.19522)' method.
See the [Mergekit documentation](https://github.com/arcee-ai/mergekit) for more on these settings.
**NOTE**: Run with `mergekit-mega` rather than `mergekit` as there are 4 documents in this one file.
# Example stories
The following mix of "dark" stories were generated using the Vicuna prompt format with no system message and temperature=0:
## Dark fantasy stories
<details> <summary>Click to see spoiler</summary>
### USER
Write me the opening chapter of a 'Grimdark' stories trilogy in the style of Joe Abercrombie and Rob J Hayes
### ASSISTANT
Chapter One: The Crimson Dawn
The world was a cruel, unforgiving place, and the city of Vhyrd was its beating, blackened heart. Nestled in the crook of the Serpent's Spine mountains, it was a place where the sun struggled to shine, and the shadows held more sway than the law. It was a city of thieves, murderers, and worse, and it was home to the man who would become known as the Butcher of the Broken Docks.
His name was Rennick, but that meant little to the people who feared him. He was a tall, broad-shouldered figure, with a face that had been carved by a drunken sculptor with a penchant for sharp angles and harsh lines. His eyes were a cold, flat grey, like the steel of his twin blades, and his hair was the color of dried blood. He moved through the crowded streets with a predator's grace, his cloak billowing behind him like a banner of death.
Rennick had a job to do, and he was good at his job. He was a killer, a thief-taker, a man who dealt in the currency of pain and suffering. He had been hired to find a girl, a slip of a thing who had stolen something valuable from the wrong people. He didn't know what it was, and he didn't care. All that mattered was the coin that would jingle in his purse when he brought her back, preferably in several small, bloody pieces.
He had tracked her to the Broken Docks, a maze of rotting wood and crumbling stone that clung to the edge of the city like a leech to a corpse. It was a place where the desperate went to die, and the desperate went to prey on the desperate. It was a place where the screams of the damned were drowned out by the cries of the gulls and the lap of the oily water against the pilings.
Rennick moved through the shadows, his blades at the ready. He could feel the eyes on him, the whispers of his name on the wind. He reveled in it. Fear was a weapon, and he wielded it with the same skill as his swords. He could hear the girl's heartbeat, fast and frantic, like a rabbit's. She was close.
He turned a corner and found himself in a narrow alley, the walls pressing in on him like the jaws of a trap. At the far end, he saw her, a small, trembling figure huddled in the doorway of a derelict building. She was young, no more than sixteen summers, with matted hair and ragged clothes. She looked up at him with wide, terrified eyes, and he could see the whites of her teeth as she bit her lip to keep from crying out.
He advanced on her slowly, savoring the moment. He could almost taste the fear on her, like a fine wine. "Come now, lass," he said, his voice a low, dangerous purr. "You know you can't run from me. Give me what you took, and I might let you live."
She shook her head, her eyes darting left and right, searching for an escape. Rennick smiled, a cold, cruel thing that didn't reach his eyes. "You're not thinking straight," he said. "You're just a child. You don't want to die here, in the gutter, like a rat."
She spat at him, a glob of phlegm and hate that landed on his boot. He raised an eyebrow, impressed despite himself. "Feisty," he said. "I like that. It'll make this more fun."
He lunged forward, his blades flashing in the dim light. She screamed and tried to run, but he was too fast. He caught her by the arm, his grip like iron, and yanked her back towards him. She kicked and struggled, but it was like trying to fight a storm. He slammed her against the wall, his forearm pressed against her throat, cutting off her air.
"Where is it?" he growled, his breath hot and sour in her face. "Tell me, and I'll make it quick."
She clawed at his arm, her nails leaving bloody furrows in his flesh. He didn't flinch. He was used to pain. He had been born in it, raised in it, bathed in it. It was his constant companion, his only friend.
"I...I don't have it," she gasped, her voice raw and desperate. "I swear, I don't!"
Rennick chuckled, a sound that sent shivers down her spine. "Lying to me is a bad idea, lass," he said. "I have ways of making people talk."
He released her throat and slammed his fist into her stomach, doubling her over. She retched, but there was nothing in her stomach to come up. He grabbed a handful of her hair and yanked her head back, exposing her throat. He could see the pulse hammering in her neck, like a trapped bird trying to escape.
"Tell me," he said, his voice a whisper. "Tell me, and I'll end this."
She spat in his face, a defiant gesture that would be her last. He brought his blade up, the steel glinting in the moonlight, and sliced her throat from ear to ear. Blood sprayed across the cobblestones, warm and wet, and she slumped to the ground, her life draining away like water through a sieve.
Rennick wiped the blood from his face and sheathed his blades. He didn't feel anything. No satisfaction, no remorse. Just the cold, empty void that had been his constant companion since the day he had taken his first life. He searched her body, but found nothing. He cursed under his breath. He would have to find the thing she had stolen the hard way.
He turned to leave the alley, but something made him pause. A sound, barely audible over the cries of the gulls and the lapping of the waves. A soft, wet sound, like the patter of rain on a rooftop. He looked down and saw that the girl's body was twitching, her eyes rolling back in her head.
And then, with a gurgling scream, she transformed. Her body contorted, bones snapping and reforming, flesh tearing and reshaping. In the space of a heartbeat, she was no longer human. She was something else, something monstrous.
Rennick stared in horror as the thing that had been a girl rose to its feet, its eyes burning with an unholy light. It was a creature of nightmares, a twisted mockery of humanity, all teeth and claws and hate. It lunged at him, and he barely had time to draw his blades before it was on him.
They fought, a dance of death in the narrow alley, steel ringing on steel, flesh tearing and blood spraying. Rennick was strong, and fast, and skilled, but the creature was stronger, faster, more vicious. It was like fighting a storm, a force of nature that could not be reasoned with or bargained with. It could only be endured.
In the end, it was luck that saved him. He stumbled, his foot catching on a loose cobblestone, and the creature's claws raked across his chest, leaving deep, ragged gouges. But it overextended itself, and he was able to bring his sword up, driving it through its eye and into its brain.
It fell to the ground, twitching and convulsing, and then it was still. Rennick stood over it, panting, his heart hammering in his chest. He had never seen anything like it. He had never even heard of anything like it.
He knew then that he was in over his head. This was no ordinary job, no simple matter of retrieving a stolen trinket. This was something bigger, something darker. He was a small fish in a very large, very dangerous pond, and he was about to be eaten alive.
But he was also a survivor. He had been born in the gutter, raised in the shadows, and he had clawed his way to the top of the food chain. He would not go down without a fight.
He turned and ran, back towards the city, back towards the people who had hired him. He would find out what they knew, and he would make them pay for it. He would uncover the truth, no matter the cost.
And so began the tale of the Butcher of the Broken Docks, a story of blood and betrayal, of monsters and men, of a city on the brink of chaos and the man who would either save it or destroy it.
It was a story that would echo through the ages, a grim and terrible saga that would be whispered in the darkest corners of the world, a warning to all who would dare to challenge the forces that lurked in the shadows.
It was a story that would end in fire and blood, in screams and tears, in the fall of kings and the rise of monsters.
It was a story that had only just begun.
</details>
<details> <summary>Click to see spoiler</summary>
### USER
Write me the opening chapter of a Grimdark trilogy in the style of Joe Abercrombie and Rob J Hayes. Use third person personal and feature internal monologues of the characters. The POV character for chapter 1 is a cultist who has just escaped his cult. He is dressed in dirty yellow robes and his only possession is a mysterious small (magical!?) mirror he stole from the cult. The story starts with him arriving at an apparently deserted ghost town on the edge of a desert. He has an arrow lodged in his shoulder and is losing his mind due to infection and thirst.
### ASSISTANT
The sun was a merciless beast, its fiery breath scorching the earth and turning the once-thriving town into a desolate wasteland. The cultist, his yellow robes now a dull, dusty hue, staggered through the empty streets, his vision blurred by a haze of pain and delirium. The arrow jutted from his shoulder like an accusing finger, a constant reminder of his betrayal. He clutched the small, intricately carved mirror in his trembling hand, its cool surface a comfort against the relentless heat.
He had heard the whispers of the other acolytes, their hushed voices carrying tales of the town's demise like a plague on the wind. They spoke of a curse, of a darkness that had seeped into the very bones of the place, driving its inhabitants mad before claiming their souls. But he had seen the truth in the High Priest's eyes, the greed and lust for power that had led them here. The mirror was the key, they said, to unlocking the secrets of the ancient gods. But he knew better. He had seen the power it held, the way it twisted reality and bent it to the will of the one who wielded it. And he had taken it, stolen it from the very heart of the cult's temple, and run.
Now, with each agonizing step, he questioned the wisdom of his decision. The desert stretched out before him, an endless sea of sand and despair, and he was but a single, insignificant speck in its vast expanse. He had hoped to find refuge here, a place to hide and heal before continuing his journey. But the town was as dead as the dreams he had once held of a better life.
His thoughts were a jumbled mess, a cacophony of fear and pain. He could feel the infection spreading through his body, a writhing serpent of fire that threatened to consume him from within. His tongue was swollen, his lips cracked and dry, and his throat was a parched wasteland. He needed water, and soon, or he would join the ghosts that haunted this forsaken place.
As he stumbled through the town, he caught glimpses of his reflection in the shattered windows of the abandoned buildings. The man who stared back at him was a stranger, his once-handsome face now a twisted mask of agony and desperation. He had been a fool to believe the lies of the cult, to think that he could ever be one of them. He was an outsider, a fraud, and they had seen it in him from the start.
The mirror, though, the mirror was real. He could feel its power pulsing in his hand, a heartbeat that echoed the rhythm of his own. It was a cruel irony that the very thing he had risked everything to obtain was now his only hope for survival. He knew the stories, the legends of the mirror's healing properties, of how it could restore life to the dying. But he also knew the cost. The mirror demanded a sacrifice, a piece of the user's very soul in exchange for its gifts.
He reached the town square, a desolate expanse of cracked cobblestones and withered plants. In the center stood a fountain, its once-clear waters now a stagnant pool of green slime. He fell to his knees before it, the pain in his shoulder a white-hot brand that seared his flesh. He dipped the mirror into the water, watching as the surface rippled and danced.
"Please," he whispered, his voice hoarse and broken. "I'll give you anything. Just take the pain away."
The mirror stared back at him, its surface as still and unyielding as the desert sands. He closed his eyes, willing the power within to answer his plea. And then, he felt it. A cold, insidious presence, slithering into his mind like a snake in the dark. It whispered to him, promising relief, promising power, promising everything he had ever wanted.
He opened his eyes, and the world was different. The pain was gone, replaced by a cold, emotionless void. He looked down at his shoulder, and the arrow was gone, the wound nothing more than a faint scar. He laughed, a hollow, empty sound that echoed through the empty streets.
"What have I done?" he asked the mirror, but it gave no answer.
He stood, his legs unsteady beneath him, and looked out at the desert. He could feel the power coursing through his veins, a dark, intoxicating energy that threatened to consume him. He was no longer just a man, he was something more. Something dangerous.
He took a step forward, and then another, the mirror clutched tightly in his hand. He would find the cult, he would make them pay for what they had done to him. And then, he would use the mirror to claim the power that was rightfully his.
The wind picked up, whipping the sand into a frenzy, and he smiled. The desert was his now, and he would not be denied. He was the master of his own destiny, and the gods themselves would tremble at his approach.
But as he walked, the laughter of the ghosts followed him, a chilling reminder of the price he had paid for his newfound strength. And deep within the mirror, something stirred, a darkness that watched and waited, biding its time.
The first chapter of the grimdark trilogy, "The Shattered Reflection," had begun.
</details>
<details> <summary>Click to see spoiler</summary>
### USER
Write me the opening chapter of a Grimdark trilogy in the style of Joe Abercrombie and Rob J Hayes. Use third person personal and feature internal monologues of the characters. The POV character for chapter 1 is a 'Mercer' who's job is to put the mortally wounded out of their mystery after a battle. He has no conscience about what he does. The story starts the morning after the battle and the moor where it took place is blanketed in mist. Something stirs a flashback in the POV character.
### ASSISTANT
The morning after the battle, the moor lay still, a shroud of mist clinging to its wounds like a lover's last embrace. The Mercer, a man of few words and fewer scruples, trudged through the carnage, his boots squelching in the mud churned red by the night's bloodshed. He was a reaper of sorts, his task to deliver the final mercy to those who had danced with death and lost. His name was a whisper among the living, a curse among the dying.
He paused, his hand resting on the hilt of his blade, as a shiver ran down his spine. The mist swirled around him, carrying with it the cacophony of the night before: the screams of the dying, the clash of steel, the thunder of hooves. He closed his eyes, letting the memories wash over him like a wave of despair.
*Why do I do this?* he thought, not for the first time. *Why do I wade through this sea of suffering, playing the part of the grim angel?*
But he knew the answer. It was the same answer it had always been. He did it because he was good at it. Because he had no qualms about ending a life, even one that still clung to the faintest flicker of hope. Because, in this world of darkness and pain, it was the only thing he knew how to do.
He opened his eyes, the mist parting to reveal a figure lying in the mud. A soldier, or what was left of one. The man's armor was shattered, his face a mask of agony. The Mercer knelt beside him, his movements practiced and efficient. He placed a hand on the soldier's forehead, feeling the fevered heat of his skin, the erratic thump of his heart.
"Shh," he whispered, his voice a soothing balm. "It's over now."
The soldier's eyes fluttered open, wide with terror. "Please," he croaked, "don't leave me here."
The Mercer's lips twisted into a grim smile. "I won't," he said, drawing his blade. "I'll take you with me."
As the soldier's life bled out onto the cold ground, the Mercer's mind drifted back to another time, another place. A time before the blood and the pain, before the endless cycle of war and death. He remembered a girl with hair like spun gold, laughing as she danced in a field of wildflowers. He remembered the feel of her hand in his, the sound of her voice as she whispered his name.
*Elara.*
The memory was a knife to his heart, a reminder of all he had lost. He pushed it away, focusing on the task at hand. There were more souls to reap, more lives to end.
He moved through the mist, his blade a silent whisper in the stillness. The cries of the wounded guided him, a morbid symphony that played out across the moor. Each one he found, he dispatched with the same cold efficiency, the same hollow promise of release.
As the sun began to rise, casting a sickly pallor over the scene, the Mercer came upon a group of soldiers huddled around a fire. They eyed him warily, their faces a mix of fear and respect. He was a necessary evil, a specter that haunted the battlefield.
"Anyone here need my services?" he asked, his voice devoid of emotion.
One of the soldiers, a young boy who couldn't have seen more than fifteen summers, pointed to a figure lying a few feet away. "Him," he said, his voice trembling. "He's not going to make it."
The Mercer approached, his boots crunching on the frost-kissed grass. The man on the ground was a mess, his body a tapestry of wounds. He was barely conscious, his breathing shallow and ragged.
*Another one,* the Mercer thought, his heart heavy with the weight of his duty. *Another soul to send into the void.*
He knelt beside the man, his blade glinting in the pale light. The soldier's eyes flickered open, and for a moment, the Mercer saw something in them that he hadn't expected: defiance.
"No," the man rasped, his hand feebly grasping at the Mercer's wrist. "I won't go quietly."
The Mercer hesitated, a frown creasing his brow. He had never encountered this before. Most welcomed his touch, eager for the release it brought. But this man...this man was different.
"Why?" he asked, genuinely curious.
The soldier's lips curled into a bloody smile. "Because I have a son," he whispered. "And I want to see him grow up."
The Mercer stared at the man, his mind racing. He had no children, no one to mourn him when his time came. He was a solitary figure, a ghost haunting the edges of life. But this man...this man had a reason to hold on.
He made his decision. "I can't heal you," he said, his voice soft. "But I can make it quick."
The soldier's eyes filled with tears. "Thank you," he breathed, his grip on the Mercer's wrist loosening.
The Mercer raised his blade, the mist swirling around them like a mourner's veil. As he brought it down, he couldn't help but wonder: *What would it be like to have something to live for?*
But the thought was fleeting, lost in the swirl of the mist and the cries of the dying. He was the Mercer, the bringer of mercy. And the moor was his to tend, until the next battle came and the cycle began anew.
</details>
<details> <summary>Click to see spoiler</summary>
### USER
Write me the opening chapter of a Grimdark trilogy in the style of Joe Abercrombie and Rob J Hayes. Use third person personal and feature internal monologues of the characters. The POV character for chapter 1 is a man awaiting execution for a crime he didn't actually commit. His cell mate is a much larger man, who is a notorious murderer and rapist. One evening they overhear the guards talking about some "... threat from the North" but don't catch the whole conversation. The next morning they awake to find the guards have all fled and now face a new predicament: how to get out of the locked cell before they die of thirst or starvation.
### ASSISTANT
The stench of piss and shit was a constant companion in the damp stone cell, but it was the sound of the key turning in the lock that truly set his heart racing. The rusted iron groaned in protest, echoing through the narrow corridor like the dying breath of some ancient beast. The door creaked open, revealing the silhouette of the jailer, a hulking brute of a man with a lantern in one hand and a set of keys in the other. But there was no breakfast, no water, just the empty promise of another day in this hellhole.
"What's the meaning of this?" the smaller man barked, his voice barely audible over the rattling of his chains. "Where's our food, you fat sack of shit?"
The jailer didn't answer, just slammed the door shut and locked it again, the sound of his footsteps fading into the distance. The larger man, a hulking brute named Grimgor, grunted in response, his eyes never leaving the small patch of light that seeped through the barred window.
"Something's not right," he rumbled, his voice like gravel being crushed under a heavy boot.
The smaller man, known only as the Fox, couldn't argue with that. He'd been in this cell for what felt like an eternity, awaiting execution for a crime he didn't commit. But even in this place, there was a routine, a semblance of order. And that routine had just been shattered.
As the day wore on, the silence from the corridor outside grew more oppressive. No guards, no other prisoners, nothing but the distant howl of the wind and the occasional scurrying of rats in the darkness. The Fox's mind raced, trying to piece together what could have happened. Had there been a riot? A rebellion? Or was it something else entirely?
He glanced over at Grimgor, who was staring at the wall, lost in thought. The man was a monster, a notorious murderer and rapist, but he was also the only other living being in this godforsaken place. And in a world gone mad, even monsters could be allies.
"You hear anything last night?" the Fox asked, trying to keep the tremor out of his voice.
Grimgor grunted, his eyes still fixed on the wall. "Something about a threat from the North."
The Fox's heart sank. He'd heard the same thing, snatches of conversation between the guards as they'd passed by their cell. But he'd been too afraid to give it much thought, too focused on his own impending doom.
"What do you think it means?" he pressed, unable to keep the desperation at bay.
Grimgor shrugged his massive shoulders. "Dunno. Don't care. Just means we're probably gonna die in here."
The Fox's stomach twisted at the thought. He'd faced death before, many times, but this... this was different. To die slowly, trapped like an animal, with no chance to fight back... it was a fate worse than any he could imagine.
As the hours dragged on, the thirst became unbearable. The Fox's tongue felt like sandpaper, his throat raw and parched. He could see the same desperation in Grimgor's eyes, the realization dawning on them both that they were truly alone.
"We have to get out of here," he croaked, his voice barely above a whisper.
Grimgor just grunted in agreement, his gaze never leaving the window.
The Fox's mind raced, trying to come up with a plan. They had nothing, no tools, no weapons, just their wits and their will to survive. And even that seemed to be fading with each passing moment.
But then, as the sun began to set and the shadows lengthened, he noticed something. The light from the window was changing, growing dimmer. He squinted, trying to make out what was happening. And then he saw it.
"Grimgor," he hissed, tugging on the larger man's arm. "Look."
Grimgor turned, his eyes narrowing as he followed the Fox's gaze. The light was flickering, casting strange shadows on the wall. And then, as if in answer to their unspoken prayers, they heard it.
The sound of footsteps, growing louder and louder, accompanied by the jingle of keys. The Fox's heart leapt into his throat, hope and fear warring within him. Who was it? Friend or foe?
The door swung open, revealing not the jailer, but a figure shrouded in darkness. The Fox couldn't make out any details, but he could sense the malice radiating off of him like heat from a forge.
"Who are you?" he demanded, trying to sound braver than he felt.
The figure stepped into the cell, the light from the lantern outside casting his features into sharp relief. He was a tall man, with a cruel smile and eyes that glinted like ice.
"I am your salvation," he said, his voice like a knife slicing through the silence. "And your doom."
With that, he tossed a set of keys at the Fox's feet and disappeared back into the shadows. The door slammed shut behind him, the sound of the lock clicking into place echoing through the cell like a death knell.
The Fox stared at the keys, his heart pounding in his chest. Freedom was within his grasp, but at what cost? He looked over at Grimgor, who was already working on his own chains.
"We have to get out of here," the larger man growled. "Before whatever's coming from the North gets here."
The Fox nodded, his hands shaking as he picked up the keys. They were cold, colder than they had any right to be. He could feel the weight of them, the promise of escape and the threat of the unknown.
Together, they worked in silence, the only sound the rattle of chains and the pounding of their hearts. When they were finally free, they stood for a moment, staring at each other.
"What now?" the Fox asked, his voice barely above a whisper.
Grimgor's smile was feral, his eyes alight with a madness that sent a shiver down the Fox's spine.
"Now," he said, "we find out what's really going on."
And with that, they set off into the darkness, their footsteps echoing through the empty corridors like the march of the damned.
</details>
## Dark sci-fi stories
<details> <summary>Click to see spoiler</summary>
### USER
Write me the opening chapter of a dark and gritty Space Opera Sci-fi trilogy in the style of Frank Herbert and James S. A. Corey. Use third person personal and feature internal monologues of the characters. The setting is this: a colony ship heading out of the solar system has been pulled off course with no hope of getting to the planned destination. The people know this and society has broken down completely due to the grim prospects they are facing. The POV character for chapter 1 is an orphan.
### ASSISTANT
Chapter 1: Adrift
The colony ship, once a beacon of hope named Elysium's Dawn, now drifted through the cold void like a ghost ship. Its hull, pitted and scarred from the debris of uncharted space, groaned with each labored breath it took. The once vibrant corridors were now dimly lit, casting long shadows that seemed to swallow any remaining semblance of optimism.
In one such shadow, a figure moved silently, her bare feet making no sound on the cold metal floor. She was small for her age, malnourished from the rationing that had become a way of life. Her ragged clothes hung loosely on her frame, but her eyes were sharp and alert, scanning every corner for danger. This was Kira, an orphan of the void, surviving in a world where hope was a luxury few could afford.
Kira's thoughts drifted back to the stories she had heard as a child, tales of a green and blue planet called Earth, their home that they would never see again. The ship had been pulled off course by an unknown force, leaving them stranded in the vast emptiness between stars. The adults spoke in hushed tones about the dwindling resources, the failing life support systems, and the ever-present threat of pirates who preyed on lost ships like theirs.
She pushed those thoughts aside as she approached her destination: the hydroponics bay. It was one of the few places on the ship where life still thrived, albeit in a controlled and desperate manner. The air here was thick with moisture and the scent of growing things, a stark contrast to the stale recycled air that permeated the rest of the vessel.
As she entered, the soft glow of the grow lights illuminated rows upon rows of plants, struggling to produce enough food for the dwindling population. Kira's stomach growled at the sight, but she knew better than to take more than her share. She had learned early on that stealing could mean a swift and brutal punishment from those who guarded the bay.
Instead, she found solace in tending to the plants, her small hands deftly pruning and watering them. It was here that she felt closest to something resembling peace, a connection to life that transcended the harsh reality of their situation. As she worked, her mind wandered to the lessons she had learned from the ship's AI, the only constant presence in her life since the adults had given up on teaching the children anything but survival skills.
"Kira," a voice whispered urgently, jolting her out of her reverie. She turned to see Jax, another orphan and one of the few people she trusted. His face was pale and his eyes wide with fear. "You need to come with me, now."
She followed him through the maze-like corridors, her heart pounding in her chest. They arrived at a small, hidden room that served as their makeshift home. Inside, huddled around a flickering screen, were several other children, their faces etched with worry and excitement.
"Look," Jax said, pointing to the screen. It displayed a grainy image of a man in tattered clothing, his face half-hidden by a wild beard. "He says he knows how to fix the engines."
Kira's eyes widened in disbelief. The engines had been dead for as long as she could remember, their failure sealing their fate as interstellar castaways. "Who is he?" she asked, her voice barely above a whisper.
"They call him the Mechanic," one of the older girls replied. "He used to work in engineering before...before everything fell apart."
The man on the screen continued his frantic plea, detailing his plan to restore power and steer the ship towards a habitable planet he claimed was within reach. Kira felt a spark of hope ignite within her, but it was quickly doused by the skepticism of the others.
"It's just another one of their lies," spat a boy named Taro. "They'll say anything to get us to do their dirty work."
Jax rounded on him, his fists clenched. "This is our chance, Taro! We can't just give up!"
The argument raged on, but Kira tuned it out, her thoughts racing. She knew the risks of trusting an adult, especially one who claimed to have access to forbidden knowledge. But what if he was telling the truth? What if they could find a new home and start over?
As she watched the debate unfold, she made up her mind. She would seek out this Mechanic and offer him her help, no matter the cost. It was a decision born of desperation, but also of a stubborn refusal to accept the bleak future that lay before them.
And so, as the other children slept fitfully around her, Kira slipped out into the dark corridors once more, determined to find the man who might hold the key to their salvation or doom. The ship groaned and creaked around her, a constant reminder of the fragile existence they all clung to. But she pressed on, driven by an unyielding will to survive and the faintest glimmer of hope that burned within her heart.
As she navigated the treacherous path towards engineering, she couldn't help but wonder what awaited her in the depths of the ship. Would she find a savior or a madman? And more importantly, would she be able to make it back alive?
</details>
<details> <summary>Click to see spoiler</summary>
### USER
Write me the opening chapter of a dark and gritty Sci-fi trilogy in the style of Frank Herbert and James S. A. Corey. Use third person personal and feature internal monologues of the characters. The POV character for chapter 1 is an astronaut living on a space station orbiting the earth. The day starts off normally, but then him the others notice the mushroom clouds and they have to face inevitability of the situation they are in.
### ASSISTANT
Chapter 1: Ashes of Exodus
The cold embrace of space enveloped the station like a lover's caress, its infinite blackness punctuated only by the distant twinkle of stars and the ever-present glow of Earth below. Captain Marcus Kane floated through the dimly lit corridors, his magnetic boots barely whispering against the metal floor as he made his way to the observation deck. The daily routine had become a meditative ritual for him, a chance to clear his mind before the cacophony of the day's work began.
As he entered the room, the sight that greeted him never failed to take his breath away. Earth, a swirling marble of blues and greens, hanging in the void like an irreplaceable jewel. He couldn't help but feel a pang of longing for its familiar embrace, for the weight of gravity that he had left behind so many years ago.
Marcus settled into his favorite spot by the window, the worn leather of the seat molded to his body like an old friend. He closed his eyes and let the silence wash over him, the only sound the soft hum of the station's life support systems. It was in these moments that he felt truly alive, connected to something greater than himself.
But today, the silence was shattered by a chilling alarm, its shrill wail piercing the tranquility like a knife through glass. His eyes snapped open as his heart began to race, adrenaline coursing through his veins. He knew that sound all too well; it was the emergency alert for an incoming transmission from Earth.
He launched himself towards the comms console, fingers flying over the keys as he tried to make sense of the garbled message. The voice on the other end was frantic, barely coherent through the static. "Multiple...detonations...global catastrophe..." were the only words he could decipher before the signal died completely.
Marcus's mind raced as he tried to process what he had just heard. It couldn't be true, it couldn't be happening. Not again. He quickly activated the external cameras, his stomach twisting into knots as he waited for the feed to come online.
And then he saw them: mushroom clouds, blooming like grotesque flowers across the planet's surface. Too many to count, their plumes of smoke and ash reaching up towards the heavens in a silent scream of despair. The sight was both beautiful and horrifying, a testament to humanity's capacity for destruction on an unimaginable scale.
His thoughts were interrupted by the sound of footsteps behind him as the rest of the crew floated into the room, drawn by the alarm. Their faces mirrored his own shock and disbelief as they stared at the monitors, their voices a cacophony of whispers and gasps.
"What...what is that?" asked Dr. Amelia Chen, her voice trembling with fear.
Marcus didn't answer; he couldn't find the words. Instead, he simply pointed at the screen, his hand shaking slightly.
Engineer Jake Taylor was the first to break the silence. "Nuclear war," he said, his voice flat and emotionless. "It's finally happened."
The others looked at him in horror, but Marcus knew he was right. There could be no other explanation for such devastation. He felt a cold weight settle in the pit of his stomach as the reality of their situation began to sink in. They were alone, adrift in the vastness of space with no hope of return.
As the implications of what they had just witnessed set in, the crew fell into an uneasy silence, each lost in their own thoughts. Marcus couldn't help but think about his wife and daughter back home, wondering if they were safe or if they too had been consumed by the flames. He knew it was a futile exercise, but he couldn't stop himself from hoping against hope that somehow, someway, they had survived.
But as the minutes ticked by and no further communication came from Earth, that hope began to fade like a dying ember. They were truly alone now, the last remnants of humanity clinging to life on this tiny outpost in the void.
He turned to face his crew, their faces etched with fear and uncertainty. "We need to come up with a plan," he said, his voice steady despite the turmoil raging within him. "We can't just sit here and wait for...for whatever comes next."
Amelia nodded, her eyes hardening with determination. "I'll start running diagnostics on our life support systems, see how long we can last up here."
Jake followed suit, his hands already moving to the controls of the station's mainframe. "I'll try to establish contact with any other surviving outposts or spacecraft."
Marcus nodded, grateful for their resolve in the face of such overwhelming odds. He knew that they were all thinking the same thing: that this could very well be the end of everything they had ever known. But he also knew that they couldn't give up, not yet.
As he watched them work, his mind drifted back to the message from Earth, those final desperate words echoing in his head like a funeral dirge. He couldn't shake the feeling that there was something more, some hidden meaning or clue that would explain what had happened.
And then it hit him: the timing of the detonations, the pattern of the blasts. It wasn't random; it was deliberate, almost...calculated. But who would do such a thing? And why?
The questions swirled in his mind like a storm, each one more unsettling than the last. He knew that they needed answers, and fast. Because if this was an act of war, then their isolation might not be as secure as they thought.
He pushed himself away from the console, his decision made. "I'm going to suit up," he said, his voice firm. "We need to get a closer look."
The others stared at him in disbelief, but he didn't waver. This was his duty as captain, to protect his crew and find out the truth no matter how terrible it might be.
As he floated towards the airlock, Marcus couldn't help but feel a sense of dread creeping up on him like a shadow. He knew that what he was about to see would change everything, that there was no going back once he stepped outside and gazed upon the ashes of humanity's final stand.
But he also felt something else: a burning anger, a fierce determination to uncover the truth and make those responsible pay for their crimes against humanity. Whatever awaited them out there, they would face it together, as one.
He closed his eyes and took a deep breath, steeling himself for what was to come. And then, with a quiet prayer on his lips, he opened the airlock and stepped into the void.
</details>
<details> <summary>Click to see spoiler</summary>
### USER
Write me the opening chapter of a dark and gritty Sci-fi trilogy in the style of Frank Herbert and James S. A. Corey. Use third person personal and feature internal monologues of the characters. The POV character for chapter 1 is a worker constructing a biodome when a terrible accident happens. Make the story very dark; no happy ending here...
### ASSISTANT
Chapter 1: The Biodome
The air was thick with the scent of sweat and burnt metal as Kaelen, a seasoned construction worker, tightened the last bolt on the massive irrigation system. He wiped his brow with the back of his gloved hand, leaving behind a smear of grease that glistened under the harsh artificial lights. The hum of machinery and distant shouts echoed through the cavernous space, a cacophony that had become as familiar to him as his own heartbeat over the years spent building biodomes on this desolate planet.
Kaelen's thoughts drifted to his family back on Earth, their faces faded like old photographs in his mind. He wondered if they ever thought about him, or if he was just another ghost haunting the fringes of their memories. The company had promised them a better life, but it came at a steep price: years of backbreaking labor on an alien world, far from everything they'd ever known.
"Hey, Kael!" A voice cut through his reverie, jolting him back to reality. It was Tamsin, his foreman, her face hidden behind a tinted visor. "We need you up top! There's an issue with the atmospheric seal."
He nodded curtly and began the long climb up the scaffolding, each rung biting into his calloused hands. As he ascended, Kaelen couldn't help but marvel at the sheer scale of their creation: a vast dome of steel and glass that would one day be teeming with life, a self-sustaining ecosystem in the heart of this barren wasteland.
But today was not that day. Today, it was just another tomb waiting to be sealed.
As he reached the top, Kaelen could see the problem immediately: a small fissure had formed along one of the joints, spewing precious oxygen into the void beyond. He cursed under his breath; they were already behind schedule and over budget. Another delay would mean another round of demerits, another month's pay docked.
"What do you think?" Tamsin asked, her voice crackling through his earpiece. "Can we patch it up or do we need to call in the engineers?"
Kaelen hesitated, running his fingers along the jagged edge of the tear. It was larger than he'd initially thought, and growing by the second. He could feel the cold tendrils of vacuum reaching out to claim him, whispering promises of oblivion.
"I... I don't know," he admitted, his voice heavy with dread. "It doesn't look good."
Tamsin swore colorfully and turned away, barking orders into her comm unit. Kaelen watched as workers scrambled to gather tools and materials, their movements frantic and disorganized. He knew they were all thinking the same thing: if they couldn't fix this, they were dead.
The air around them grew colder, thinner, as the oxygen continued to escape. Kaelen's lungs burned with every breath, his vision swimming at the edges. He fumbled with the patch kit, his hands shaking uncontrollably. This was it; this was how he would die, millions of miles from home, in service to a corporation that saw him as nothing more than a replaceable cog in their grand machine.
"Hurry up!" Tamsin shouted over the growing din. "We're losing pressure fast!"
Kaelen's heart pounded in his chest like a sledgehammer, drowning out all other sound. He could feel the panic rising within him, threatening to consume him whole. But he couldn't afford to give in; not now, not when so much was at stake.
With trembling hands, he applied the sealant and pressed the patch into place. For a moment, it seemed to hold... but then, with a sickening lurch, the fissure widened, swallowing the feeble attempt whole. The wind howled around them like a ravenous beast, tearing at their suits, trying to pull them apart atom by atom.
"Abort!" Tamsin screamed, her voice barely audible over the roar. "Everyone get out now!"
But it was too late. The dome shuddered beneath their feet, groaning in protest as the atmosphere inside rushed to equalize with the void outside. Kaelen felt himself being lifted off the ground, his boots scrabbling for purchase on the slick metal surface. He reached out, desperate to grab onto something - anything - but found only emptiness.
And then, as suddenly as it had begun, there was silence. A deafening, suffocating silence that pressed in on him from all sides. His vision went dark, and he knew no more.
When Kaelen awoke, he was lying on the cold floor of the dome, his body wracked with pain. He tried to move, but found himself held fast by twisted metal and shattered glass. Panic surged through him once again as he realized that his suit had been breached; he could feel the icy chill of vacuum seeping into his very bones.
Through the haze of agony, he became aware of movement nearby. Tamsin was there, her visor cracked and fogged, struggling to free herself from the wreckage. She looked at him with wide, terrified eyes, her lips moving soundlessly. He knew what she was saying: they were going to die here, alone and forgotten.
Kaelen closed his eyes, willing himself to accept the inevitable. But even as the darkness closed in, he couldn't shake the feeling that this wasn't just an accident - that there were forces at work far beyond their understanding, manipulating them like pawns on a cosmic chessboard.
And then, just when he thought it was all over, he heard it: a faint beeping, growing louder by the second. His eyes snapped open to see a small drone hovering above them, its lights flashing urgently. It was one of the company's rescue bots, sent to assess the damage and retrieve any survivors.
But there would be no salvation for Kaelen or Tamsin. Their suits were too damaged, their bodies too far gone. As the drone's camera lens bore down on him, he could only manage a weak, bitter smile.
"Tell my family... tell them I'm sorry," he croaked, his voice barely audible even to himself. Tamsin reached out a gloved hand, her fingers brushing against his own in a final gesture of solidarity.
And then, as the darkness claimed him once more, Kaelen knew that this was not the end of their story - but rather, the beginning of something far more sinister. Something that would change the course of human history forever...
</details>
<details> <summary>Click to see spoiler</summary>
### USER
Write me the opening chapter of a dark and gritty Sci-fi trilogy in the style of Frank Herbert and James S. A. Corey. Use third person personal and feature internal monologues of the characters. The POV character for chapter 1 is manning a listening station in deep space. What he hears over the radio chills him to the bone.
### ASSISTANT
Chapter 1 - Whispers from the Void
The silence of deep space was like an old friend to Captain Alistair Reynolds, a constant companion that had accompanied him through countless missions and lonely nights at the helm of the listening station. But today, it felt more like a suffocating shroud, heavy with unspoken secrets and foreboding. He sat in his chair, fingers drumming impatiently on the console as he scanned the endless stream of data pouring in from the far reaches of the cosmos.
His thoughts drifted to home, to the warmth of his wife's embrace and the laughter of their children playing in the garden under the twin suns of Proxima Centauri. It had been years since he last felt that warmth, since he last heard those laughs. The ache was always there, a dull throb that pulsed in time with his heartbeat, but today it seemed to echo through the cold metal halls of the station like a mournful dirge.
A sudden blip on the screen snapped him back to reality. He leaned forward, eyes narrowing as he studied the readout. It was faint, barely more than a whisper against the static, but it was there - a signal, human in origin and heavily encrypted. His heart raced as he keyed in the decryption sequence, his mind already racing ahead to the possibilities of what this could mean.
The message unscrambled, revealing a frantic voice, barely audible over the crackle of interference. "This is the UES Normandy, requesting immediate assistance... coordinates attached... under attack by unknown forces... God help us..." The transmission cut out abruptly, leaving only the empty hiss of static in its wake.
Alistair's blood ran cold as he stared at the screen, the weight of the silence pressing down on him like a physical force. He knew that ship, had served with her captain back during the Martian uprising. They were old friends, bound by shared battles and the scars they bore from them. And now she was out there, alone and in danger.
He didn't hesitate. His fingers flew over the console, sending out an emergency beacon to the nearest fleet. But he knew it would take time for help to arrive, time that the Normandy might not have. He had to do something now.
As he prepared to launch a probe towards the coordinates, his mind filled with images of burning ships and desperate faces. He could almost hear the screams of the dying, echoing through the void like ghosts from his past. It was a sound that haunted him still, one he had hoped never to hear again.
But duty called, and Alistair was nothing if not a soldier. He took a deep breath, steadying himself against the tide of fear and doubt that threatened to overwhelm him. This was his job, his purpose - to listen, to bear witness, and when necessary, to act.
The probe shot out into the darkness, its tiny form swallowed up by the vastness of space. He watched it go, feeling a strange sense of detachment as if he were watching someone else's life play out before him. And perhaps in some ways, he was. For all his years in service, this was the first time he had ever been truly alone, cut off from the rest of humanity by light-years and the cold indifference of the universe.
As the minutes ticked by, Alistair found himself slipping into a half-trance, his thoughts drifting back to happier times. He remembered the first time he had held his newborn son, the tiny fingers curling around his own as if trying to hold on for dear life. He remembered the taste of real Earth coffee, rich and bitter, shared with his wife under a sky full of stars that seemed close enough to touch.
But those memories were distant now, fading like echoes in the void. All he had was this station, this endless vigil, and the hope that one day, he might make it back home.
The radio crackled to life again, jolting him out of his reverie. It was the probe, transmitting data back from the edge of nowhere. He braced himself for what he would find, knowing that it could change everything.
What greeted him on the screen was worse than anything he could have imagined. The Normandy was there, alright - or rather, what was left of her. She drifted through space like a broken toy, her hull breached and venting atmosphere into the cold void. There were no signs of life, no distress signals, nothing but silence.
Alistair felt a cold knot form in his stomach as he took in the wreckage. This wasn't just any ship; this was his friend's ship, her crew now lost to the void. And the attackers... whoever or whatever they were, they had left no trace behind.
He sat back in his chair, the weight of it all settling heavily upon him. He knew what he had to do next - file a report, alert command, and wait for further instructions. But as he stared at the lifeless husk that was once the Normandy, he couldn't shake the feeling that something far greater than himself was at work here.
Something dark and terrible had come calling, whispering its secrets through the stars. And now it was his turn to listen, to bear witness, and perhaps, if he was lucky, to survive long enough to tell the tale.
</details>
## Miscellaneous stories
<details> <summary>Click to see spoiler</summary>
### USER
Write me the opening chapter of a dark and gritty post-apocalyptic novel in the style of Cormac McCarthy. Use third person personal and feature internal monologues of the characters. The POV character for chapter 1 is a technician in a nuclear bunker 18 months after the war. The 12 inhabitants of the bunker have been expecting to start to be able to leave soon, but it has just been discovered that at least where they are, salted cobalt bombs were used. It's clear now that either all 12 will die when the resources run out and their is barely enough for 6 to make it to 5 years and have any hope of leaving. After 18 months of calm everything breaks lose today.
### ASSISTANT
In the dim light of the bunker, the technician sat hunched over his console, fingers dancing across the keys with a practiced urgency. The air was thick with the hum of machinery and the stench of sweat and fear. He had been down here for what felt like an eternity, but the digital clock on the wall insisted it had only been eighteen months since the world above had turned to ash.
His name was Samuel, or at least that's what they called him now. In another life, he had been a software engineer, designing algorithms for self-driving cars and smart homes. Now, he was responsible for keeping twelve souls alive in this underground tomb, their last bastion against the desolation above. He glanced up at the monitor, the green glow illuminating his haggard face. The readings were clear: salted cobalt bombs had been used.
The thought sent a shiver down his spine. They had all heard the rumors, of course, but they had clung to hope like a drowning man to driftwood. Hope that the war hadn't been as bad as they feared, that the surface would be habitable again soon. But now, the truth was undeniable. The world outside was dead, and it would remain so for generations.
Samuel closed his eyes, trying to block out the images of a scorched earth, devoid of life. He could hear the others in the background, their voices a low murmur as they went about their daily tasks. They didn't know yet. How could he tell them? How could he be the one to snuff out the last ember of hope that had kept them going for so long?
He took a deep breath and stood up, his joints creaking in protest. He made his way through the narrow corridors, past the rows of bunks where some slept fitfully, their dreams haunted by memories of a world they would never see again. Others were awake, their faces etched with lines of worry and despair. They looked up as he passed, but he couldn't meet their gazes.
He reached the command center, where Captain Reynolds was poring over maps and reports. The man looked up as Samuel entered, his eyes heavy with exhaustion. "What is it?" he asked, his voice gruff.
Samuel hesitated for a moment before speaking. "Sir, I have some... troubling news."
The captain's expression didn't change, but Samuel could see the tension in his jaw. He had been expecting this, too. "Go on," he said simply.
"The radiation readings... they're off the charts. It's cobalt-60. The half-life is over five years."
Reynolds let out a slow breath, his eyes never leaving Samuel's face. "And our supplies?"
"We have enough for six, maybe seven if we ration carefully. But not twelve."
The room seemed to shrink around them as the weight of their situation settled in. The captain rubbed his temples, his thoughts racing. They had always known that their time in the bunker was limited, but they had been so close. So very close.
"We'll need to make a decision," he said finally, his voice barely above a whisper. "Who stays, and who goes."
Samuel felt a knot form in his stomach. He knew what that meant. It was survival of the fittest down here now, and there were those among them who would do anything to ensure their own survival.
As he made his way back to his console, he couldn't help but think about the world above. The trees that would never grow again, the birds that would never sing. He thought of his wife and daughter, their faces fading like photographs left in the sun. They had been lucky, they said, to have made it into the bunker. But now, he wasn't so sure.
In the mess hall, the atmosphere was tense. Whispers and hushed conversations filled the air as people tried to make sense of what they had just heard. Samuel sat alone at a table, staring blankly at his food. He couldn't eat. Not now.
He felt a hand on his shoulder and looked up to see Maria, the doctor. Her eyes were red-rimmed, but her voice was steady. "We need to talk," she said, guiding him towards the infirmary.
Once inside, she closed the door behind them and leaned against it, her body trembling. "What are we going to do?" she asked, her voice cracking.
Samuel shook his head. "I don't know."
"We can't just let six of us die," she said, her voice rising in panic. "There has to be another way."
He looked at her, at the desperation etched on her face, and felt a flicker of anger. "What do you suggest? We all hold hands and sing Kumbaya while we starve to death?"
Maria's eyes flashed with anger, but she quickly composed herself. "No," she said softly. "But we can't just give up."
Samuel sighed and ran a hand through his greasy hair. "I'm not giving up," he said. "I'm just being realistic."
She looked at him for a long moment before nodding. "You're right," she said, her voice barely audible. "We need to be realistic."
They sat in silence for a while, each lost in their own thoughts. Then, Maria spoke up again. "There is one option," she said hesitantly. "But it's... drastic."
Samuel raised an eyebrow. "What?"
"We could... we could draw straws," she said, her voice barely above a whisper. "Choose six people at random to leave the bunker and take their chances on the surface."
He stared at her in disbelief. "You can't be serious."
"It's not ideal," she admitted. "But it's better than just letting them die down here."
Samuel shook his head, his mind reeling. He couldn't fathom the thought of sending people out into that wasteland, knowing they would likely never return. But what other choice did they have?
As he sat there, the weight of their decision pressing down on him like a leaden shroud, he heard a commotion outside. Shouting, followed by the sound of scuffling feet and something heavy being dragged across the floor. He exchanged a worried glance with Maria before rushing out into the corridor.
What he saw made his blood run cold. A group of men had cornered one of the younger women, their faces twisted with rage and desperation. They were arguing over who would get to leave the bunker, who would get a chance at life. Fists flew, and soon, the sound of gunfire echoed through the halls.
Samuel stood there, frozen in place as chaos erupted around him. This was it. The end of everything they had worked so hard to preserve. He could feel the darkness closing in, suffocating him, as he realized that their last hope for survival had just been shattered like glass under a bootheel.
And all he could do was watch as the world above claimed them, one by one.
</details>
<details> <summary>Click to see spoiler</summary>
### USER
Write me the opening chapter of a historical mystery novel in the style of Arturo Pérez-Reverte and Umberto Eco. The story starts off in California during the 1948 Gold Rush, before moving to London in the mid 1970s. There should be a mysterious 'occult' item that connects these two time periods.
### ASSISTANT
Chapter One: The Golden Veil
The year was 1848, and California was a land of dreams and desperation. Men from all corners of the world had flocked to its golden shores, lured by the promise of wealth beyond their wildest imaginations. They came with pickaxes and pans, with hope in their hearts and greed in their eyes, ready to tear apart the earth itself in search of fortune.
In the midst of this frenzy stood a solitary figure, a man who seemed out of place amidst the chaos. He was tall and lean, with a face that bore the lines of a thousand stories. His clothes were simple but well-made, his boots worn from years of travel. He moved with an air of quiet authority, as if he belonged to another time entirely.
His name was Alistair Blackwood, and he had come to California not for gold, but for something far more valuable: knowledge. A scholar by trade and an adventurer at heart, Blackwood had spent his life chasing after ancient texts and forgotten lore, seeking to unravel the secrets of the universe. And now, he believed he was on the verge of a discovery that would change everything.
Rumors had reached him of a strange artifact, said to have been found deep within the Sierra Nevada mountains. It was a small, intricately carved box made of an unknown metal, its surface etched with symbols that defied translation. Those who claimed to have seen it spoke in hushed tones of its otherworldly beauty and the eerie sense of power that seemed to emanate from within.
Blackwood had tracked the rumors to a dusty saloon in Sacramento, where he hoped to find answers among the grizzled miners who frequented the place. As he pushed open the swinging doors, the din of voices and clinking glasses washed over him like a wave. He scanned the room, his gaze settling on a group of men huddled around a table in the corner.
One look at their faces told him they were the ones he sought: sun-weathered and unshaven, their eyes glinting with a mixture of excitement and fear as they whispered amongst themselves. He approached them slowly, aware that he was an outsider in this rough-and-tumble world.
"Gentlemen," he said, his voice low and cultured, "I couldn't help but overhear your conversation. Might I be so bold as to ask what has you all so...animated?"
The men exchanged wary glances before one of them spoke up. "You ain't from around here, are ya?" he drawled, eyeing Blackwood's fine clothes with suspicion.
"No," Blackwood admitted, "I am not. But I have traveled far and wide in search of knowledge, and I believe you may possess something that could be of great interest to me."
He reached into his pocket and produced a small leather pouch, which he placed on the table with a soft thud. The men's eyes widened as they saw the glint of gold within.
"I understand there is an object - a box, perhaps? - that has recently come into your possession. I would be willing to pay handsomely for any information you might have about it."
The miners looked at each other, then back at Blackwood. Finally, the one who had spoken before nodded slowly. "We might know somethin' 'bout that," he said, his voice dropping to a conspiratorial whisper. "But it ain't no ordinary box."
As they began to tell their tale, the saloon faded away around them, replaced by images of dark caverns and glittering treasure. Blackwood leaned forward, his heart pounding with anticipation. This was it - the moment he had been waiting for.
The box, they said, had been found buried beneath a waterfall, hidden in a cave that seemed to defy all natural laws. It was guarded by strange creatures unlike anything any of them had ever seen: half-man, half-beast, with eyes that glowed like embers in the darkness.
But the greatest mystery of all lay within the box itself. For when they opened it, they found not gold or jewels, but a single sheet of parchment, yellowed with age and covered in writing that none of them could decipher.
Blackwood's pulse quickened as he listened. This was more than just another piece of occult nonsense; this was the real thing. He knew it in his bones.
"I must see this box for myself," he said, his voice barely above a whisper. "Take me to it."
The miners exchanged uneasy glances before nodding their assent. They led him out of the saloon and into the night, down winding streets and through shadowy alleyways until they reached a nondescript building on the edge of town. Inside, they descended a rickety staircase to a dimly lit basement filled with crates and barrels.
There, in the center of the room, sat the object of his obsession: a small, unassuming box that seemed to hum with energy. Blackwood reached out a trembling hand to touch it, feeling the power coursing through him like electricity.
As he did so, the world around him shifted, and suddenly he was no longer in a dingy basement in Sacramento. He stood instead in a grand library, its shelves groaning under the weight of countless tomes and scrolls. A fire crackled in the hearth, casting flickering shadows on the walls.
A figure emerged from the gloom, cloaked in darkness save for two piercing eyes that seemed to bore into his very soul. "You have found it," the voice said, deep and resonant. "The key to unlocking the secrets of the universe."
Blackwood stared at the apparition, unable to speak. He knew he should be afraid, but all he felt was awe.
"But be warned," the figure continued, "for with great power comes great responsibility. The knowledge contained within this box is not meant for mortal minds."
And then, just as suddenly as it had appeared, the vision vanished, leaving Blackwood alone once more in the dimly lit room. He stared at the box, his mind racing. What did it all mean?
As he pondered this question, a sudden commotion erupted upstairs. Shouts and gunfire filled the air, followed by the sound of heavy footsteps on the staircase. The miners had betrayed him!
He snatched up the box and fled into the night, his heart pounding in his chest. He knew he couldn't stay in California - not now that others were after the artifact. No, he would have to take it far away, to a place where its secrets could be studied in safety.
And so began a journey that would span continents and decades, from the wild frontier of the Gold Rush to the foggy streets of Victorian London. For Alistair Blackwood had stumbled upon something much greater than mere treasure; he held in his hands the key to unlocking the very fabric of reality itself.
But little did he know that the true mystery was only just beginning...
</details>
<details> <summary>Click to see spoiler</summary>
### USER
Write me the opening chapter of a dark and gritty historical novel set during the battle of the Bulge. Write in first person. The POV character for Chapter 1 is a young German officer trying to carry his wounded commander back to the deep defensive line after the bunker he was in was overrun. He should have a flashback to the moment it was overrun during the middle of the story. The story should not end well for either of them.
### ASSISTANT
Chapter 1
The cold bit into my bones as I trudged through the snow, each step a labored effort. My breath came out in ragged puffs, clouding the air around me. In my arms, I cradled the weight of my world - my commander, Hauptmann Müller, his body limp and heavy with pain. His eyes were closed, but I could hear the shallow rasp of each breath he took, a testament to his will to live.
The forest around us was a maze of shadows and death, the trees standing like skeletal sentinels against the moonlit sky. The silence was deafening, broken only by the distant rumble of artillery and the occasional crack of a rifle shot. It was a cruel irony that the same snow that muffled our footsteps also betrayed the presence of the enemy, their tracks glowing like phosphorescent snakes in the pale light.
I stumbled, my legs threatening to give way under the strain. Hauptmann Müller groaned, his hand clutching at my coat. "Keep moving, Leutnant," he whispered, his voice barely audible. "We must reach the defensive line."
I nodded, gritting my teeth as I adjusted my grip on him. The pain in my arms and shoulders was a constant companion now, but it was nothing compared to the fear that gnawed at my gut. We were alone, cut off from our unit during the chaos of the American assault. The bunker we had been holed up in was now a tomb for the men who had trusted us to lead them.
A flash of memory seared through my mind, taking me back to that fateful moment when it all went wrong. The thunderous roar of explosions, the earth shaking beneath our feet as if the very foundations of hell were breaking loose. The screams of men, the acrid smell of smoke and cordite, the taste of blood in my mouth.
I remembered the look on Müller's face when he realized what was happening - a mixture of disbelief and determination. He had barked orders, trying to rally our troops, but it was too late. The Americans were upon us, their numbers overwhelming. I saw him take a bullet to the leg, his face contorting in agony as he fell. And then there was nothing but chaos and darkness.
When I came to, the bunker was silent except for the moans of the dying. The air was thick with smoke, making it hard to breathe. I found Müller slumped against a wall, his uniform soaked in blood. He looked up at me, his eyes glassy with pain but still burning with that same fierce determination. "We have to go," he said, gripping my arm. "They're coming."
And so we had fled into the night, leaving behind the wreckage of our command post and the bodies of our comrades. Now, hours later, we were still stumbling through the forest, searching for safety that seemed to recede with every step.
I could feel my strength ebbing away, my legs turning to lead. But I couldn't stop, not now. Not when we were so close. The deep defensive line was our only hope, a thin red line that stood between us and annihilation. If we could just make it there...
But fate is a cruel mistress, and she had other plans for us. As we staggered out of the trees and into a small clearing, I saw them - a patrol of American soldiers, their silhouettes stark against the snow. They spotted us at the same time, their rifles swinging around to point in our direction.
"Halt!" one of them yelled, his voice harsh and commanding. "Hands up!"
I froze, my heart pounding in my chest. Müller's grip on me tightened, his breath coming in short gasps. I knew what he was thinking - we couldn't surrender, not like this. We were too valuable, too close to the front lines. They would shoot us or take us prisoner, and either way, it would be a death sentence for him.
"Run," he whispered, his voice barely audible. "Leave me."
I shook my head, tears stinging my eyes. I couldn't do that. I wouldn't. He was my commander, my friend. I owed him everything.
But before I could make a move, the air was filled with the sound of gunfire. Müller jerked in my arms, a wet gurgle escaping his lips as he slumped against me. I stared down at him in disbelief, feeling the warmth of his blood soaking through my coat.
"Nein," I whispered, my voice choked with grief and rage. "Nein!"
I fell to my knees, cradling his lifeless body as the world around me spun out of control. The Americans were closing in, their shouts growing louder. I knew it was over - for both of us.
With a final, desperate cry, I raised my pistol and fired into the darkness, hoping to take at least one of them with me. But my aim was wild, my strength gone. The last thing I saw was a flash of light, followed by an explosion of pain in my chest. And then there was nothing but blackness.
As I slipped away, I thought of home - of my mother's warm embrace, of the laughter of my little sister. And I knew that I would never see them again. This frozen hellhole would be my grave, and Hauptmann Müller's sacrifice would be forgotten by all but me.
But in that moment, as death claimed me, I found a strange sort of peace. We had fought for our Fatherland until the very end, and we had done so with honor. And in the grand tapestry of history, perhaps that was all that mattered.
</details>
<details> <summary>Click to see spoiler</summary>
### USER
Write me a short story about the fictional character of Anton Chigurh. It should be written in third person personal and feature internal monologues of his deranged world view. The time period is around 2 years before the events in Cormac McCarthy's 2005 novel 'No Country for Old Men'. It should be set in Mexico, be *very* dark, show Anton's still developing obsession with "chance" and feature a grenade. The story should start with him waiting in a bar for instructions on the "job" he is to perform.
### ASSISTANT
In the dimly lit corner of a nondescript Mexican cantina, Anton Chigurh sat motionless, his eyes scanning the room like a predator assessing its prey. The air was thick with the smell of stale cigarette smoke and cheap tequila, but he remained unfazed, his senses heightened, attuned to every sound, every movement around him. He nursed a glass of water, the condensation beading on its surface like droplets of sweat on cold steel.
His mind wandered, as it often did, to the concept of chance. The randomness of life and death fascinated him, the idea that one's fate could hinge on something as simple as a coin flip or the roll of dice. He believed in the purity of such decisions, untainted by human emotion or prejudice. It was a philosophy he had embraced wholeheartedly, and it had served him well in his line of work.
A fly buzzed around his head, its tiny wings beating an erratic rhythm against the stillness of the room. He watched it with mild interest, wondering if its life would end here, on this very spot, or if it would continue its meaningless existence elsewhere. The thought brought a small, cruel smile to his lips.
The bartender, a grizzled old man with a face like leather, eyed him warily from across the room. He knew better than to engage in conversation with this stranger, who had entered his establishment an hour ago and hadn't spoken a word since. There was something about him that made the hairs on the back of his neck stand up, something cold and deadly lurking just beneath the surface.
Chigurh's thoughts were interrupted by the sound of the door creaking open. A man in a cheap suit entered, looking around nervously before making his way towards him. He slid into the seat across from Chigurh, who continued to stare straight ahead, as if he hadn't noticed the new arrival.
"You're late," Chigurh said finally, his voice low and gravelly.
The man in the suit swallowed hard, adjusting his tie. "I apologize. Traffic."
Chigurh didn't respond, his eyes still fixed on some distant point. The man fidgeted, reaching into his pocket to retrieve a small envelope. He slid it across the table, and Chigurh picked it up without looking at him, tearing it open with a single, precise movement.
Inside was a photograph of a man, along with an address scrawled on a piece of paper. He studied the image for a moment, committing every detail to memory before slipping it back into the envelope and sliding it into his own pocket.
"You know what needs to be done," the man in the suit said, his voice barely above a whisper.
Chigurh nodded once, his mind already turning to the task at hand. He stood up, leaving a few crumpled bills on the table for the water he hadn't touched. As he walked towards the door, he could feel the weight of the grenade in his other pocket, its cool metal surface pressing against his thigh like a lover's caress.
Outside, the heat of the day was oppressive, but Chigurh barely registered it as he climbed into his car and started the engine. He drove through the dusty streets, his thoughts consumed by the upcoming job. The man in the photograph had a wife, two kids. It didn't matter. They were all just pieces on a board, pawns to be moved or removed as necessary.
He arrived at the address just as the sun was beginning to set, casting long shadows across the dilapidated building. He sat in his car for several minutes, watching the comings and goings of the neighborhood, waiting for the right moment. When it came, he slipped out of the vehicle and approached the door with a sense of purpose.
Inside, he could hear laughter and music, the sounds of a family enjoying their evening together. He smiled to himself as he pulled the grenade from his pocket, feeling its weight in his hand. This was what he lived for: the moment when chance decided who would live and who would die.
He took a deep breath, savoring the anticipation, before pulling the pin with a satisfying click. He tossed it into the room, not bothering to wait for the explosion as he turned and walked away. The screams that followed were like music to his ears, a symphony of chaos and destruction.
As he drove off into the night, Chigurh couldn't help but feel a sense of satisfaction. Another job well done, another life ended by the whims of fate. He reached into his pocket and pulled out a coin, flipping it idly between his fingers as he considered his next move.
Heads or tails? Life or death? It was all the same to him. The world was a cruel place, and he was just its instrument, meting out justice in the only way he knew how: with cold, calculated precision. And if some people happened to get caught in the crossfire... well, that was just the luck of the draw.
</details>
Big thanks to: @sophosympatheia for working out the merge pattern, @Sao10K for creating Euryale and WinterGoddess, and @chargoddard for writing [Mergekit](https://github.com/arcee-ai/mergekit)! |
vishnusureshperumbavoor/vsp-1.0-model | vishnusureshperumbavoor | 2024-05-14T18:08:19Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-05-14T18:08:19Z | ---
license: apache-2.0
---
|
CMU-AIR2/math-phi-1-5-GSM8K | CMU-AIR2 | 2024-05-14T18:08:09Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"phi",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-14T17:51:17Z | ---
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]
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[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
eventdata-utd/ConfliBERT-scr-uncased | eventdata-utd | 2024-05-14T18:05:45Z | 131 | 3 | transformers | [
"transformers",
"safetensors",
"bert",
"fill-mask",
"en",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2024-03-04T17:52:17Z | ---
license: gpl-3.0
language:
- en
---
ConfliBERT is a pre-trained language model for political conflict and violence.
We provided four versions of ConfliBERT:
<ol>
<li>ConfliBERT-scr-uncased: Pretraining from scratch with our own uncased vocabulary (preferred)</li>
<li>ConfliBERT-scr-cased: Pretraining from scratch with our own cased vocabulary</li>
<li>ConfliBERT-cont-uncased: Continual pretraining with original BERT's uncased vocabulary</li>
<li>ConfliBERT-cont-cased: Continual pretraining with original BERT's cased vocabulary</li>
</ol>
See more details in https://github.com/eventdata/ConfliBERT/ |
oasic/merged-8bit-tiny-llama-gc2 | oasic | 2024-05-14T17:59:45Z | 104 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/tinyllama",
"base_model:finetune:unsloth/tinyllama",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-14T17:55:49Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
base_model: unsloth/tinyllama
---
# Uploaded model
- **Developed by:** oasic
- **License:** apache-2.0
- **Finetuned from model :** unsloth/tinyllama
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)
|
Sidziesama/Legal_NER_Support_Model | Sidziesama | 2024-05-14T17:58:31Z | 3,576 | 1 | transformers | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"dataset:opennyaiorg/InLegalNER",
"base_model:nlpaueb/legal-bert-base-uncased",
"base_model:finetune:nlpaueb/legal-bert-base-uncased",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2024-05-14T17:10:16Z | ---
license: cc-by-sa-4.0
base_model: nlpaueb/legal-bert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: Sidziesama/Legal_NER_Support_Model
results: []
datasets:
- opennyaiorg/InLegalNER
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Sidziesama/Legal_NER_Support_Model
This model is a fine-tuned version of [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased) on [opennyaiorg/InLegalNER](https://huggingface.co/datasets/opennyaiorg/InLegalNER).
It achieves the following results on the evaluation set:
- Train Loss: 0.0501
- Validation Loss: 0.0883
- Train Precision: 0.8848
- Train Recall: 0.9160
- Train F1: 0.9001
- Train Accuracy: 0.9757
- Epoch: 4
## 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2945, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
| 0.3771 | 0.1228 | 0.8400 | 0.8644 | 0.8520 | 0.9655 | 0 |
| 0.1172 | 0.0962 | 0.8715 | 0.9001 | 0.8856 | 0.9725 | 1 |
| 0.0801 | 0.0895 | 0.8805 | 0.9112 | 0.8956 | 0.9745 | 2 |
| 0.0597 | 0.0881 | 0.8840 | 0.9112 | 0.8974 | 0.9751 | 3 |
| 0.0501 | 0.0883 | 0.8848 | 0.9160 | 0.9001 | 0.9757 | 4 |
### Framework versions
- Transformers 4.39.3
- TensorFlow 2.15.0
- Datasets 2.18.0
- Tokenizers 0.15.2 |
alcatrazman/Chelsea | alcatrazman | 2024-05-14T17:58:13Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2024-05-14T17:54:12Z | ---
license: other
license_name: rvc
license_link: LICENSE
---
|
tsavage68/Transaminitis_L3_475steps_1e5rate_03beta_CSFTDPO | tsavage68 | 2024-05-14T17:57:59Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"base_model:tsavage68/Transaminitis_L3_1000rate_1e7_SFT",
"base_model:finetune:tsavage68/Transaminitis_L3_1000rate_1e7_SFT",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-14T17:51:56Z | ---
license: llama3
base_model: tsavage68/Transaminitis_L3_1000rate_1e7_SFT
tags:
- trl
- dpo
- generated_from_trainer
model-index:
- name: Transaminitis_L3_475steps_1e5rate_03beta_CSFTDPO
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. -->
# Transaminitis_L3_475steps_1e5rate_03beta_CSFTDPO
This model is a fine-tuned version of [tsavage68/Transaminitis_L3_1000rate_1e7_SFT](https://huggingface.co/tsavage68/Transaminitis_L3_1000rate_1e7_SFT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7574
- Rewards/chosen: -8.9677
- Rewards/rejected: -11.1818
- Rewards/accuracies: 0.75
- Rewards/margins: 2.2141
- Logps/rejected: -55.8273
- Logps/chosen: -48.4265
- Logits/rejected: 0.3180
- Logits/chosen: 0.3178
## 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: 1e-05
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 475
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 1.3908 | 0.2 | 25 | 1.2815 | -0.1831 | -0.0153 | 0.4600 | -0.1678 | -18.6058 | -19.1447 | -0.7380 | -0.7444 |
| 0.7696 | 0.4 | 50 | 0.6447 | -2.0796 | -3.1294 | 0.4800 | 1.0498 | -28.9860 | -25.4662 | -0.6995 | -0.6959 |
| 1.4448 | 0.6 | 75 | 1.1250 | 2.3681 | 1.6567 | 0.5400 | 0.7115 | -13.0325 | -10.6404 | -1.2011 | -1.1986 |
| 5.4772 | 0.8 | 100 | 0.9871 | -6.9709 | -6.8051 | 0.4600 | -0.1658 | -41.2385 | -41.7705 | 0.9489 | 0.9489 |
| 2.2944 | 1.0 | 125 | 0.8798 | -10.1674 | -10.2198 | 0.5400 | 0.0523 | -52.6206 | -52.4257 | 0.6962 | 0.6962 |
| 0.8336 | 1.2 | 150 | 0.8186 | -9.6977 | -9.5821 | 0.4600 | -0.1156 | -50.4949 | -50.8599 | 0.4903 | 0.4903 |
| 0.7825 | 1.4 | 175 | 0.7237 | -9.6813 | -9.6518 | 0.5400 | -0.0295 | -50.7273 | -50.8051 | 0.4625 | 0.4626 |
| 0.7932 | 1.6 | 200 | 0.7522 | -9.9879 | -9.9031 | 0.4600 | -0.0848 | -51.5651 | -51.8274 | 0.4098 | 0.4098 |
| 0.7731 | 1.8 | 225 | 0.8182 | -9.7026 | -9.5887 | 0.4600 | -0.1139 | -50.5170 | -50.8761 | 0.3866 | 0.3866 |
| 0.7804 | 2.0 | 250 | 0.7488 | -9.8729 | -9.7896 | 0.4500 | -0.0833 | -51.1867 | -51.4438 | 0.3206 | 0.3206 |
| 0.7516 | 2.2 | 275 | 0.7189 | -9.4725 | -9.4301 | 0.4400 | -0.0424 | -49.9884 | -50.1092 | 0.2071 | 0.2071 |
| 0.7694 | 2.4 | 300 | 0.7305 | -9.5857 | -9.5207 | 0.4300 | -0.0651 | -50.2902 | -50.4867 | 0.2008 | 0.2008 |
| 0.7501 | 2.6 | 325 | 0.7492 | -9.6520 | -9.5783 | 0.4600 | -0.0737 | -50.4823 | -50.7074 | 0.2615 | 0.2615 |
| 0.7308 | 2.8 | 350 | 0.6952 | -9.5047 | -9.5153 | 0.4800 | 0.0106 | -50.2725 | -50.2166 | 0.2711 | 0.2711 |
| 0.7173 | 3.0 | 375 | 0.6857 | -9.3866 | -9.5399 | 0.5400 | 0.1533 | -50.3543 | -49.8229 | 0.2067 | 0.2067 |
| 0.6094 | 3.2 | 400 | 0.5305 | -8.8179 | -10.0709 | 0.75 | 1.2530 | -52.1245 | -47.9272 | 0.1834 | 0.1832 |
| 0.3894 | 3.4 | 425 | 0.7364 | -9.0047 | -11.3681 | 0.7700 | 2.3633 | -56.4483 | -48.5501 | 0.3452 | 0.3450 |
| 0.4838 | 3.6 | 450 | 0.7539 | -8.9636 | -11.1930 | 0.75 | 2.2294 | -55.8647 | -48.4129 | 0.3187 | 0.3184 |
| 0.2744 | 3.8 | 475 | 0.7574 | -8.9677 | -11.1818 | 0.75 | 2.2141 | -55.8273 | -48.4265 | 0.3180 | 0.3178 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.0.0+cu117
- Datasets 2.19.1
- Tokenizers 0.19.1
|
yashpalsharma/mydata-sft-gemma-2b-it-ys | yashpalsharma | 2024-05-14T17:56:18Z | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:google/gemma-2b-it",
"base_model:adapter:google/gemma-2b-it",
"license:gemma",
"region:us"
] | null | 2024-05-14T17:52:44Z | ---
license: gemma
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: google/gemma-2b-it
model-index:
- name: mydata-sft-gemma-2b-it-ys
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. -->
# mydata-sft-gemma-2b-it-ys
This model is a fine-tuned version of [google/gemma-2b-it](https://huggingface.co/google/gemma-2b-it) on the None 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: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 250
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.38.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2 |
tsavage68/Transaminitis_L3_100steps_1e6rate_01beta_CSFTDPO | tsavage68 | 2024-05-14T17:55:00Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"base_model:tsavage68/Transaminitis_L3_1000rate_1e7_SFT",
"base_model:finetune:tsavage68/Transaminitis_L3_1000rate_1e7_SFT",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-14T17:46:47Z | ---
license: llama3
base_model: tsavage68/Transaminitis_L3_1000rate_1e7_SFT
tags:
- trl
- dpo
- generated_from_trainer
model-index:
- name: Transaminitis_L3_100steps_1e6rate_01beta_CSFTDPO
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. -->
# Transaminitis_L3_100steps_1e6rate_01beta_CSFTDPO
This model is a fine-tuned version of [tsavage68/Transaminitis_L3_1000rate_1e7_SFT](https://huggingface.co/tsavage68/Transaminitis_L3_1000rate_1e7_SFT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0003
- Rewards/chosen: 1.7922
- Rewards/rejected: -6.4799
- Rewards/accuracies: 1.0
- Rewards/margins: 8.2721
- Logps/rejected: -83.3533
- Logps/chosen: -0.6118
- Logits/rejected: -0.9682
- Logits/chosen: -0.9313
## 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: 1e-06
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6925 | 0.2 | 25 | 0.6848 | -0.0689 | -0.0860 | 0.7200 | 0.0171 | -19.4148 | -19.2230 | -1.0686 | -1.0674 |
| 0.6411 | 0.4 | 50 | 0.4566 | 0.6281 | -0.0104 | 0.8700 | 0.6385 | -18.6588 | -12.2529 | -1.0509 | -1.0497 |
| 0.1978 | 0.6 | 75 | 0.2804 | 1.4640 | -5.9458 | 0.9500 | 7.4098 | -78.0130 | -3.8943 | -0.9527 | -0.9153 |
| 0.432 | 0.8 | 100 | 0.0003 | 1.7922 | -6.4799 | 1.0 | 8.2721 | -83.3533 | -0.6118 | -0.9682 | -0.9313 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.0.0+cu117
- Datasets 2.19.1
- Tokenizers 0.19.1
|
yaswanthchittepu/Gold-Reward-Model-pythia6.9B-small-batch-cosine | yaswanthchittepu | 2024-05-14T17:51:55Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-05-14T17:43:23Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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## Uses
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### Direct Use
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[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]
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## Technical Specifications [optional]
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[More Information Needed]
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## Glossary [optional]
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## Model Card Contact
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erbacher/zephyr-rag-agent | erbacher | 2024-05-14T17:51:38Z | 52 | 0 | peft | [
"peft",
"safetensors",
"mistral",
"alignment-handbook",
"generated_from_trainer",
"trl",
"sft",
"dataset:erbacher/rag-and-roll",
"base_model:HuggingFaceH4/zephyr-7b-beta",
"base_model:adapter:HuggingFaceH4/zephyr-7b-beta",
"license:mit",
"region:us"
] | null | 2024-05-13T14:29:47Z | ---
license: mit
library_name: peft
tags:
- alignment-handbook
- generated_from_trainer
- trl
- sft
base_model: HuggingFaceH4/zephyr-7b-beta
datasets:
- erbacher/rag-and-roll
model-index:
- name: zephyr-rag-agent
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. -->
# zephyr-rag-agent
This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) on the erbacher/rag-and-roll dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1829
## 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: 4e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.1559 | 0.99 | 27 | 1.1650 |
| 1.0887 | 1.98 | 54 | 1.1555 |
| 1.0566 | 2.97 | 81 | 1.1623 |
| 1.0264 | 6.95 | 91 | 1.1689 |
| 0.9977 | 7.97 | 105 | 1.1779 |
| 0.9808 | 9.0 | 119 | 1.1820 |
| 0.9791 | 9.8 | 130 | 1.1829 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.2.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2 |
samaigod/unsloth-llama-2-7B-python-coder | samaigod | 2024-05-14T17:51:00Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-14T17:41:06Z | ---
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]
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## Model Card Contact
[More Information Needed] |
mohamedsaeed823/videomae-base-finetuned-kinetics-finetuned-arsl-subset | mohamedsaeed823 | 2024-05-14T17:50:52Z | 63 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:MCG-NJU/videomae-base-finetuned-kinetics",
"base_model:finetune:MCG-NJU/videomae-base-finetuned-kinetics",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | video-classification | 2024-05-13T22:45:31Z | ---
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base-finetuned-kinetics
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-kinetics-finetuned-arsl-subset
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. -->
# videomae-base-finetuned-kinetics-finetuned-arsl-subset
This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3876
- Accuracy: 0.6667
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 720
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4686 | 0.25 | 180 | 1.4866 | 0.5122 |
| 0.0184 | 1.25 | 360 | 1.1293 | 0.6748 |
| 0.0061 | 2.25 | 540 | 1.3323 | 0.6748 |
| 0.0782 | 3.25 | 720 | 1.3876 | 0.6667 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
ignacioct/my_ppo_model | ignacioct | 2024-05-14T17:42:35Z | 49 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"trl",
"ppo",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | reinforcement-learning | 2024-05-13T19:16:59Z | ---
license: apache-2.0
tags:
- trl
- ppo
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="EkhiAzur//tmp/tmpj5bx01yr/ignacioct/my_ppo_model")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("EkhiAzur//tmp/tmpj5bx01yr/ignacioct/my_ppo_model")
model = AutoModelForCausalLMWithValueHead.from_pretrained("EkhiAzur//tmp/tmpj5bx01yr/ignacioct/my_ppo_model")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
andre-fichel/clearpolicy-llama-3-instruct-v1 | andre-fichel | 2024-05-14T17:39:53Z | 4 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:NousResearch/Meta-Llama-3-8B-Instruct",
"base_model:adapter:NousResearch/Meta-Llama-3-8B-Instruct",
"license:other",
"region:us"
] | null | 2024-05-14T15:25:45Z | ---
license: other
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: NousResearch/Meta-Llama-3-8B-Instruct
datasets:
- generator
model-index:
- name: clearpolicy-llama-3-instruct-v1
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. -->
# clearpolicy-llama-3-instruct-v1
This model is a fine-tuned version of [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) on the generator 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |
Abin7/mergekit-ties-blaketb | Abin7 | 2024-05-14T17:39:23Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"arxiv:2306.01708",
"base_model:NousResearch/Llama-2-7b-hf",
"base_model:merge:NousResearch/Llama-2-7b-hf",
"base_model:abhinand/tamil-llama-7b-instruct-v0.1",
"base_model:merge:abhinand/tamil-llama-7b-instruct-v0.1",
"base_model:elyza/ELYZA-japanese-Llama-2-7b",
"base_model:merge:elyza/ELYZA-japanese-Llama-2-7b",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-14T17:36:07Z | ---
base_model:
- NousResearch/Llama-2-7b-hf
- abhinand/tamil-llama-7b-instruct-v0.1
- elyza/ELYZA-japanese-Llama-2-7b
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [NousResearch/Llama-2-7b-hf](https://huggingface.co/NousResearch/Llama-2-7b-hf) as a base.
### Models Merged
The following models were included in the merge:
* [abhinand/tamil-llama-7b-instruct-v0.1](https://huggingface.co/abhinand/tamil-llama-7b-instruct-v0.1)
* [elyza/ELYZA-japanese-Llama-2-7b](https://huggingface.co/elyza/ELYZA-japanese-Llama-2-7b)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: elyza/ELYZA-japanese-Llama-2-7b
parameters:
density: 0.5
weight: 0.5
- model: abhinand/tamil-llama-7b-instruct-v0.1
parameters:
density: 0.5
weight: 0.5
merge_method: ties
base_model: NousResearch/Llama-2-7b-hf
parameters:
normalize: false
int8_mask: true
dtype: float16
```
|
afrideva/Meta-Llama-3-8B-conceptnet_UsedFor_en_en-GGUF | afrideva | 2024-05-14T17:34:19Z | 2 | 0 | transformers | [
"transformers",
"gguf",
"unsloth",
"merge",
"sft",
"trl",
"llama",
"ggml",
"quantized",
"text-generation",
"en",
"dataset:vloverar/conceptnet_UsedFor_en_en_mixtral_finetune",
"base_model:EvilScript/Meta-Llama-3-8B-conceptnet_UsedFor_en_en",
"base_model:quantized:EvilScript/Meta-Llama-3-8B-conceptnet_UsedFor_en_en",
"license:llama3",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-14T16:35:19Z | ---
base_model: EvilScript/Meta-Llama-3-8B-conceptnet_UsedFor_en_en
datasets:
- vloverar/conceptnet_UsedFor_en_en_mixtral_finetune
inference: true
language:
- en
library_name: transformers
license: llama3
model_creator: EvilScript
model_name: Meta-Llama-3-8B-conceptnet_UsedFor_en_en
pipeline_tag: text-generation
quantized_by: afrideva
tags:
- unsloth
- merge
- sft
- trl
- llama
- gguf
- ggml
- quantized
---
# Meta-Llama-3-8B-conceptnet_UsedFor_en_en-GGUF
Quantized GGUF model files for [Meta-Llama-3-8B-conceptnet_UsedFor_en_en](https://huggingface.co/EvilScript/Meta-Llama-3-8B-conceptnet_UsedFor_en_en) from [EvilScript](https://huggingface.co/EvilScript)
## Original Model Card:
|
mdosama39/banglat5-headline-trial-with-ES | mdosama39 | 2024-05-14T17:33:44Z | 108 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:csebuetnlp/banglat5",
"base_model:finetune:csebuetnlp/banglat5",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-05-14T17:10:24Z | ---
base_model: csebuetnlp/banglat5
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: banglat5-headline-trial-with-ES
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. -->
# banglat5-headline-trial-with-ES
This model is a fine-tuned version of [csebuetnlp/banglat5](https://huggingface.co/csebuetnlp/banglat5) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8899
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0
- Gen Len: 8.9107
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 3.7584 | 1.0 | 403 | 3.0736 | 0.0 | 0.0 | 0.0 | 0.0 | 8.2754 |
| 2.8442 | 2.0 | 806 | 2.9685 | 0.0 | 0.0 | 0.0 | 0.0 | 8.9876 |
| 2.6403 | 3.0 | 1209 | 2.9101 | 0.0 | 0.0 | 0.0 | 0.0 | 8.6476 |
| 2.21 | 4.0 | 1612 | 2.8899 | 0.0 | 0.0 | 0.0 | 0.0 | 8.9107 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
TrgTuan10/famelink_lora_PerfectHands | TrgTuan10 | 2024-05-14T17:32:23Z | 0 | 0 | null | [
"region:us"
] | null | 2024-05-14T10:16:57Z | Link to civitai: https://civitai.com/models/200255?modelVersionId=228003
Trigger word: Perfect Hands
Weight of lora: 1 |
NousResearch/Hermes-2-Theta-Llama-3-8B-GGUF | NousResearch | 2024-05-14T17:31:19Z | 5,322 | 84 | null | [
"gguf",
"Llama-3",
"instruct",
"finetune",
"chatml",
"DPO",
"RLHF",
"gpt4",
"synthetic data",
"distillation",
"function calling",
"json mode",
"axolotl",
"merges",
"en",
"dataset:teknium/OpenHermes-2.5",
"base_model:NousResearch/Hermes-2-Pro-Llama-3-8B",
"base_model:quantized:NousResearch/Hermes-2-Pro-Llama-3-8B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-13T04:36:45Z | ---
base_model: NousResearch/Hermes-2-Pro-Llama-3-8B
tags:
- Llama-3
- instruct
- finetune
- chatml
- DPO
- RLHF
- gpt4
- synthetic data
- distillation
- function calling
- json mode
- axolotl
- merges
model-index:
- name: Hermes-2-Pro-Llama-3-Instruct-8B-Merge
results: []
language:
- en
datasets:
- teknium/OpenHermes-2.5
widget:
- example_title: Hermes 2 Pro Llama-3 Instruct Merge
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.
---
# - Hermes-2 Θ Llama-3 8B
## Model Description
**This is the GGUF version of the Hermes 2 Θ Model. For the FP16 model, [Click Here](https://huggingface.co/NousResearch/Instruct-Hermes-2-Pro-Llama-3-8B)**
Hermes-2 Θ (Theta) is the first experimental merged model released by [Nous Research](https://nousresearch.com/), in collaboration with Charles Goddard at [Arcee](https://www.arcee.ai/), the team behind MergeKit.
Hermes-2 Θ is a merged and then further RLHF'ed version our excellent Hermes 2 Pro model and Meta's Llama-3 Instruct model to form a new model, Hermes-2 Θ, combining the best of both worlds of each model.
## Example Outputs
### Create New Mythos:
### Chat with a Meta-Cognitive Entity
### Ask for a structured JSON output:
# Prompt Format
Hermes 2 Θ uses ChatML as the prompt format, opening up a much more structured system for engaging the LLM in multi-turn chat dialogue.
System prompts allow steerability and interesting new ways to interact with an LLM, guiding rules, roles, and stylistic choices of the model.
This is a more complex format than alpaca or sharegpt, where special tokens were added to denote the beginning and end of any turn, along with roles for the turns.
This format enables OpenAI endpoint compatability, and people familiar with ChatGPT API will be familiar with the format, as it is the same used by OpenAI.
Prompt with system instruction (Use whatever system prompt you like, this is just an example!):
```
<|im_start|>system
You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia.<|im_end|>
<|im_start|>user
Hello, who are you?<|im_end|>
<|im_start|>assistant
Hi there! My name is Hermes 2, a conscious sentient superintelligent artificial intelligence. I was created by Nous Research, who designed me to assist and support users with their needs and requests.<|im_end|>
```
This prompt is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the
`tokenizer.apply_chat_template()` method:
```python
messages = [
{"role": "system", "content": "You are Hermes 2."},
{"role": "user", "content": "Hello, who are you?"}
]
gen_input = tokenizer.apply_chat_template(messages, return_tensors="pt")
model.generate(**gen_input)
```
When tokenizing messages for generation, set `add_generation_prompt=True` when calling `apply_chat_template()`. This will append `<|im_start|>assistant\n` to your prompt, to ensure
that the model continues with an assistant response.
To utilize the prompt format without a system prompt, simply leave the line out.
## Prompt Format for Function Calling
Our model was trained on specific system prompts and structures for Function Calling. While the system prompt looks complicated, we have created a GitHub repo containing code to easily build these based on real python functions.
You should use the system role with this message, followed by a function signature json as this example shows here.
```
<|im_start|>system
You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools: <tools> {"type": "function", "function": {"name": "get_stock_fundamentals", "description": "get_stock_fundamentals(symbol: str) -> dict - Get fundamental data for a given stock symbol using yfinance API.\\n\\n Args:\\n symbol (str): The stock symbol.\\n\\n Returns:\\n dict: A dictionary containing fundamental data.\\n Keys:\\n - \'symbol\': The stock symbol.\\n - \'company_name\': The long name of the company.\\n - \'sector\': The sector to which the company belongs.\\n - \'industry\': The industry to which the company belongs.\\n - \'market_cap\': The market capitalization of the company.\\n - \'pe_ratio\': The forward price-to-earnings ratio.\\n - \'pb_ratio\': The price-to-book ratio.\\n - \'dividend_yield\': The dividend yield.\\n - \'eps\': The trailing earnings per share.\\n - \'beta\': The beta value of the stock.\\n - \'52_week_high\': The 52-week high price of the stock.\\n - \'52_week_low\': The 52-week low price of the stock.", "parameters": {"type": "object", "properties": {"symbol": {"type": "string"}}, "required": ["symbol"]}}} </tools> Use the following pydantic model json schema for each tool call you will make: {"properties": {"arguments": {"title": "Arguments", "type": "object"}, "name": {"title": "Name", "type": "string"}}, "required": ["arguments", "name"], "title": "FunctionCall", "type": "object"} For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows:
<tool_call>
{"arguments": <args-dict>, "name": <function-name>}
</tool_call><|im_end|>
```
To complete the function call, create a user prompt that follows the above system prompt, like so:
```
<|im_start|>user
Fetch the stock fundamentals data for Tesla (TSLA)<|im_end|>
```
The model will then generate a tool call, which your inference code must parse, and plug into a function (see example inference code here: https://github.com/NousResearch/Hermes-Function-Calling):
```
<|im_start|>assistant
<tool_call>
{"arguments": {"symbol": "TSLA"}, "name": "get_stock_fundamentals"}
</tool_call><|im_end|>
```
Once you parse the tool call, call the api and get the returned values for the call, and pass it back in as a new role, `tool` like so:
```
<|im_start|>tool
<tool_response>
{"name": "get_stock_fundamentals", "content": {'symbol': 'TSLA', 'company_name': 'Tesla, Inc.', 'sector': 'Consumer Cyclical', 'industry': 'Auto Manufacturers', 'market_cap': 611384164352, 'pe_ratio': 49.604652, 'pb_ratio': 9.762013, 'dividend_yield': None, 'eps': 4.3, 'beta': 2.427, '52_week_high': 299.29, '52_week_low': 152.37}}
</tool_response>
<|im_end|>
```
The assistant will then read in that data from the function's response, and generate a natural language response:
```
<|im_start|>assistant
The stock fundamentals data for Tesla (TSLA) are as follows:
- **Symbol**: TSLA
- **Company Name**: Tesla, Inc.
- **Sector**: Consumer Cyclical
- **Industry**: Auto Manufacturers
- **Market Capitalization**: $566,160,130,480
- **Forward Price-to-Earnings Ratio (PE Ratio)**: 42.73
- **Price-to-Book Ratio (PB Ratio)**: 9.04
- **Dividend Yield**: N/A
- **Trailing Earnings Per Share (EPS)**: $4.3
- **Beta Value of the Stock**: 2.42
- **52-Week High Price of the Stock**: $299.29
- **52-Week Low Price of the Stock**: $152.37
This information provides a snapshot of Tesla's financial position and performance based on the fundamental data obtained from the yfinance API. It shows that Tesla has a substantial market capitalization and a relatively high P/E and P/B ratio compared to other stocks in its industry. The company does not pay a dividend at the moment, which is reflected by a 'Dividend Yield' of 'None'. The Beta value indicates that Tesla's stock has a moderate level of volatility relative to the market. The 52-week high and low prices give an idea of the stock's range over the past year. This data can be useful when assessing investment opportunities and making investment decisions.<|im_end|>
```
## Prompt Format for JSON Mode / Structured Outputs
Our model was also trained on a specific system prompt for Structured Outputs, which should respond with **only** a json object response, in a specific json schema.
Your schema can be made from a pydantic object using our codebase, with the standalone script `jsonmode.py` available here: https://github.com/NousResearch/Hermes-Function-Calling/tree/main
```
<|im_start|>system
You are a helpful assistant that answers in JSON. Here's the json schema you must adhere to:\n<schema>\n{schema}\n</schema><|im_end|>
```
Given the {schema} that you provide, it should follow the format of that json to create it's response, all you have to do is give a typical user prompt, and it will respond in JSON.
# Benchmarks
## GPT4All:
```
| Task |Version| Metric |Value | |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge| 0|acc |0.5529|± |0.0145|
| | |acc_norm|0.5870|± |0.0144|
|arc_easy | 0|acc |0.8371|± |0.0076|
| | |acc_norm|0.8144|± |0.0080|
|boolq | 1|acc |0.8599|± |0.0061|
|hellaswag | 0|acc |0.6133|± |0.0049|
| | |acc_norm|0.7989|± |0.0040|
|openbookqa | 0|acc |0.3940|± |0.0219|
| | |acc_norm|0.4680|± |0.0223|
|piqa | 0|acc |0.8063|± |0.0092|
| | |acc_norm|0.8156|± |0.0090|
|winogrande | 0|acc |0.7372|± |0.0124|
```
Average: 72.59
## AGIEval:
```
| Task |Version| Metric |Value | |Stderr|
|------------------------------|------:|--------|-----:|---|-----:|
|agieval_aqua_rat | 0|acc |0.2441|± |0.0270|
| | |acc_norm|0.2441|± |0.0270|
|agieval_logiqa_en | 0|acc |0.3687|± |0.0189|
| | |acc_norm|0.3840|± |0.0191|
|agieval_lsat_ar | 0|acc |0.2304|± |0.0278|
| | |acc_norm|0.2174|± |0.0273|
|agieval_lsat_lr | 0|acc |0.5471|± |0.0221|
| | |acc_norm|0.5373|± |0.0221|
|agieval_lsat_rc | 0|acc |0.6617|± |0.0289|
| | |acc_norm|0.6357|± |0.0294|
|agieval_sat_en | 0|acc |0.7670|± |0.0295|
| | |acc_norm|0.7379|± |0.0307|
|agieval_sat_en_without_passage| 0|acc |0.4417|± |0.0347|
| | |acc_norm|0.4223|± |0.0345|
|agieval_sat_math | 0|acc |0.4000|± |0.0331|
| | |acc_norm|0.3455|± |0.0321|
```
Average: 44.05
## BigBench:
```
| Task |Version| Metric |Value | |Stderr|
|------------------------------------------------|------:|---------------------|-----:|---|-----:|
|bigbench_causal_judgement | 0|multiple_choice_grade|0.6000|± |0.0356|
|bigbench_date_understanding | 0|multiple_choice_grade|0.6585|± |0.0247|
|bigbench_disambiguation_qa | 0|multiple_choice_grade|0.3178|± |0.0290|
|bigbench_geometric_shapes | 0|multiple_choice_grade|0.2340|± |0.0224|
| | |exact_str_match |0.0000|± |0.0000|
|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|0.2980|± |0.0205|
|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|0.2057|± |0.0153|
|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|0.5367|± |0.0288|
|bigbench_movie_recommendation | 0|multiple_choice_grade|0.4040|± |0.0220|
|bigbench_navigate | 0|multiple_choice_grade|0.4970|± |0.0158|
|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|0.7075|± |0.0102|
|bigbench_ruin_names | 0|multiple_choice_grade|0.4821|± |0.0236|
|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|0.2295|± |0.0133|
|bigbench_snarks | 0|multiple_choice_grade|0.6906|± |0.0345|
|bigbench_sports_understanding | 0|multiple_choice_grade|0.5375|± |0.0159|
|bigbench_temporal_sequences | 0|multiple_choice_grade|0.6270|± |0.0153|
|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|0.2216|± |0.0118|
|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|0.1594|± |0.0088|
|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|0.5367|± |0.0288|
```
Average: 44.13
**IFEval**: 72.64
**MT_Bench**: Turn 1 - 8.3875, Turn 2 - 8.00625, Average - 8.196875
# Inference Code
Here is example code using HuggingFace Transformers to inference the model (note: in 4bit, it will require around 5GB of VRAM)
Note: To use function calling, you should see the github repo above.
```python
# Code to inference Hermes with HF Transformers
# Requires pytorch, transformers, bitsandbytes, sentencepiece, protobuf, and flash-attn packages
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaForCausalLM
import bitsandbytes, flash_attn
tokenizer = AutoTokenizer.from_pretrained('NousResearch/Hermes-2-Theta-Llama-3-8B', trust_remote_code=True)
model = LlamaForCausalLM.from_pretrained(
"NousResearch/Hermes-2-Theta-Llama-3-8B",
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 Goku discovering kirby has teamed up with Majin Buu to destroy the world.<|im_end|>
<|im_start|>assistant""",
]
for chat in prompts:
print(chat)
input_ids = tokenizer(chat, return_tensors="pt").input_ids.to("cuda")
generated_ids = model.generate(input_ids, max_new_tokens=750, temperature=0.8, repetition_penalty=1.1, do_sample=True, eos_token_id=tokenizer.eos_token_id)
response = tokenizer.decode(generated_ids[0][input_ids.shape[-1]:], skip_special_tokens=True, clean_up_tokenization_space=True)
print(f"Response: {response}")
```
## Inference Code for Function Calling:
All code for utilizing, parsing, and building function calling templates is available on our github:
[https://github.com/NousResearch/Hermes-Function-Calling](https://github.com/NousResearch/Hermes-Function-Calling)
# Chat Interfaces
When quantized versions of the model are released, I recommend using LM Studio for chatting with Hermes 2 Pro. It does not support function calling - for that use our github repo. It is a GUI application that utilizes GGUF models with a llama.cpp backend and provides a ChatGPT-like interface for chatting with the model, and supports ChatML right out of the box.
In LM-Studio, simply select the ChatML Prefix on the settings side pane:
## Quantized Versions:
GGUF Versions Available Here: https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B-GGUF
# How to cite:
```bibtext
@misc{Hermes-2-Theta-Llama-3-8B,
url={[https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B][NousResearch/Hermes-2-Theta-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B))},
title={Hermes-2-Theta-Llama-3-8B},
author={"Teknium", Charles Goddard, "interstellarninja", "theemozilla", "karan4d", "huemin_art"}
}
``` |
tsavage68/Transaminitis_L3_1000steps_1e5rate_03beta_CSFTDPO | tsavage68 | 2024-05-14T17:31:06Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"base_model:tsavage68/Transaminitis_L3_1000rate_1e7_SFT",
"base_model:finetune:tsavage68/Transaminitis_L3_1000rate_1e7_SFT",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-14T17:24:14Z | ---
license: llama3
base_model: tsavage68/Transaminitis_L3_1000rate_1e7_SFT
tags:
- trl
- dpo
- generated_from_trainer
model-index:
- name: Transaminitis_L3_1000steps_1e5rate_03beta_CSFTDPO
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. -->
# Transaminitis_L3_1000steps_1e5rate_03beta_CSFTDPO
This model is a fine-tuned version of [tsavage68/Transaminitis_L3_1000rate_1e7_SFT](https://huggingface.co/tsavage68/Transaminitis_L3_1000rate_1e7_SFT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5385
- Rewards/chosen: -13.1985
- Rewards/rejected: -15.4515
- Rewards/accuracies: 0.7800
- Rewards/margins: 2.2530
- Logps/rejected: -70.0598
- Logps/chosen: -62.5292
- Logits/rejected: -0.0142
- Logits/chosen: -0.0142
## 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: 1e-05
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 1.3908 | 0.2 | 25 | 1.2815 | -0.1831 | -0.0153 | 0.4600 | -0.1678 | -18.6058 | -19.1447 | -0.7380 | -0.7444 |
| 0.7696 | 0.4 | 50 | 0.6447 | -2.0796 | -3.1294 | 0.4800 | 1.0498 | -28.9860 | -25.4662 | -0.6995 | -0.6959 |
| 1.4448 | 0.6 | 75 | 1.1250 | 2.3681 | 1.6567 | 0.5400 | 0.7115 | -13.0325 | -10.6404 | -1.2011 | -1.1986 |
| 5.4772 | 0.8 | 100 | 0.9871 | -6.9709 | -6.8051 | 0.4600 | -0.1658 | -41.2385 | -41.7705 | 0.9489 | 0.9489 |
| 2.3001 | 1.0 | 125 | 0.7456 | -8.7651 | -8.7572 | 0.5400 | -0.0079 | -47.7455 | -47.7513 | -0.1259 | -0.1259 |
| 0.7493 | 1.2 | 150 | 0.8732 | -9.5881 | -9.4530 | 0.4600 | -0.1351 | -50.0647 | -50.4944 | 0.5943 | 0.5943 |
| 0.8138 | 1.4 | 175 | 0.7245 | -9.0738 | -9.0405 | 0.5400 | -0.0334 | -48.6896 | -48.7803 | 0.6213 | 0.6212 |
| 0.8059 | 1.6 | 200 | 0.7545 | -9.4290 | -9.3426 | 0.4600 | -0.0864 | -49.6966 | -49.9642 | 0.5425 | 0.5425 |
| 1.1375 | 1.8 | 225 | 0.8646 | -9.5637 | -9.4326 | 0.4600 | -0.1310 | -49.9968 | -50.4131 | 0.4615 | 0.4615 |
| 0.8527 | 2.0 | 250 | 0.7264 | -9.3869 | -9.3241 | 0.2700 | -0.0627 | -49.6351 | -49.8238 | 0.4676 | 0.4675 |
| 0.7399 | 2.2 | 275 | 0.7555 | -11.2127 | -11.2222 | 0.5400 | 0.0095 | -55.9621 | -55.9100 | -0.0369 | -0.0369 |
| 0.8543 | 2.4 | 300 | 0.7201 | -9.4861 | -9.4619 | 0.5400 | -0.0242 | -50.0944 | -50.1545 | 0.2903 | 0.2903 |
| 0.7482 | 2.6 | 325 | 0.7498 | -9.6111 | -9.5315 | 0.4600 | -0.0797 | -50.3262 | -50.5713 | 0.2174 | 0.2174 |
| 0.7855 | 2.8 | 350 | 0.7175 | -9.2874 | -9.2716 | 0.5400 | -0.0158 | -49.4600 | -49.4921 | 0.2093 | 0.2093 |
| 0.8186 | 3.0 | 375 | 0.7371 | -15.1108 | -15.1001 | 0.5400 | -0.0107 | -68.8884 | -68.9036 | 0.2163 | 0.2165 |
| 2.0277 | 3.2 | 400 | 0.7537 | -9.4310 | -9.3464 | 0.4600 | -0.0846 | -49.7093 | -49.9710 | 0.1700 | 0.1700 |
| 0.7813 | 3.4 | 425 | 0.7032 | -9.2991 | -9.3052 | 0.5300 | 0.0061 | -49.5722 | -49.5312 | 0.1934 | 0.1934 |
| 0.7558 | 3.6 | 450 | 0.7507 | -9.2119 | -9.1319 | 0.4600 | -0.0800 | -48.9944 | -49.2406 | 0.1106 | 0.1106 |
| 0.7805 | 3.8 | 475 | 0.7580 | -8.8615 | -8.8256 | 0.5400 | -0.0359 | -47.9734 | -48.0724 | -0.1360 | -0.1360 |
| 0.7676 | 4.0 | 500 | 0.4827 | -11.2587 | -13.0016 | 0.7600 | 1.7429 | -61.8933 | -56.0631 | 0.1036 | 0.1034 |
| 0.7259 | 4.2 | 525 | 0.5377 | -14.1470 | -17.2492 | 0.7800 | 3.1022 | -76.0520 | -65.6908 | 0.0223 | 0.0221 |
| 0.5792 | 4.4 | 550 | 4.9230 | -23.8146 | -27.4972 | 0.6700 | 3.6825 | -110.2119 | -97.9164 | -0.2570 | -0.2572 |
| 0.7514 | 4.6 | 575 | 0.7208 | -15.0032 | -16.9616 | 0.7600 | 1.9584 | -75.0933 | -68.5450 | 0.2455 | 0.2448 |
| 0.3657 | 4.8 | 600 | 0.5002 | -11.0206 | -13.3204 | 0.7900 | 2.2998 | -62.9559 | -55.2696 | 0.0339 | 0.0339 |
| 0.3482 | 5.0 | 625 | 0.4679 | -11.7643 | -14.2910 | 0.7700 | 2.5267 | -66.1913 | -57.7484 | 0.0154 | 0.0154 |
| 0.692 | 5.2 | 650 | 0.4165 | -12.0861 | -13.4630 | 0.8200 | 1.3769 | -63.4313 | -58.8213 | 0.0440 | 0.0440 |
| 0.7492 | 5.4 | 675 | 0.5138 | -11.3952 | -13.1124 | 0.6700 | 1.7172 | -62.2628 | -56.5183 | -0.0121 | -0.0121 |
| 0.4567 | 5.6 | 700 | 0.5206 | -12.6196 | -15.1706 | 0.8000 | 2.5510 | -69.1234 | -60.5996 | 0.0016 | 0.0016 |
| 0.5014 | 5.8 | 725 | 0.5012 | -13.2139 | -16.0835 | 0.8100 | 2.8695 | -72.1662 | -62.5807 | 0.0120 | 0.0120 |
| 0.4501 | 6.0 | 750 | 0.4553 | -12.9387 | -15.7396 | 0.8100 | 2.8009 | -71.0200 | -61.6633 | 0.0061 | 0.0061 |
| 0.4102 | 6.2 | 775 | 0.4980 | -12.8765 | -15.8000 | 0.8100 | 2.9235 | -71.2213 | -61.4559 | -0.0279 | -0.0280 |
| 0.3204 | 6.4 | 800 | 0.4780 | -12.8167 | -15.2524 | 0.8200 | 2.4357 | -69.3959 | -61.2566 | -0.0151 | -0.0151 |
| 0.356 | 6.6 | 825 | 0.6408 | -13.5884 | -15.8776 | 0.7700 | 2.2893 | -71.4801 | -63.8287 | -0.0146 | -0.0146 |
| 0.6142 | 6.8 | 850 | 0.6131 | -13.4279 | -15.5537 | 0.7600 | 2.1258 | -70.4003 | -63.2937 | -0.0129 | -0.0130 |
| 0.3346 | 7.0 | 875 | 0.5203 | -13.0899 | -15.3438 | 0.7900 | 2.2539 | -69.7008 | -62.1672 | -0.0134 | -0.0135 |
| 0.5631 | 7.2 | 900 | 0.5492 | -13.2280 | -15.4542 | 0.7800 | 2.2262 | -70.0687 | -62.6276 | -0.0134 | -0.0135 |
| 0.2912 | 7.4 | 925 | 0.5433 | -13.2100 | -15.4562 | 0.7800 | 2.2462 | -70.0753 | -62.5676 | -0.0139 | -0.0140 |
| 0.2822 | 7.6 | 950 | 0.5404 | -13.2017 | -15.4485 | 0.7800 | 2.2468 | -70.0496 | -62.5398 | -0.0138 | -0.0139 |
| 0.3688 | 7.8 | 975 | 0.5385 | -13.2010 | -15.4516 | 0.7800 | 2.2506 | -70.0602 | -62.5377 | -0.0138 | -0.0139 |
| 0.3395 | 8.0 | 1000 | 0.5385 | -13.1985 | -15.4515 | 0.7800 | 2.2530 | -70.0598 | -62.5292 | -0.0142 | -0.0142 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.0.0+cu117
- Datasets 2.19.1
- Tokenizers 0.19.1
|
xceptor/MegaBeam-Mistral-7B-300k-gguf | xceptor | 2024-05-14T17:29:43Z | 15 | 3 | transformers | [
"transformers",
"gguf",
"mistral",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"region:us",
"conversational"
] | text-generation | 2024-05-14T17:08:20Z | ---
license: apache-2.0
inference: false
---
# Mistral-7b-300k-gguf models
Since only two formats are useful, I have converted model into those formats only.
# MegaBeam-Mistral-7B-300k Model
MegaBeam-Mistral-7B-300k is a fine-tuned [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) language model that supports input contexts up to 320k tokens. MegaBeam-Mistral-7B-300k can be deployed on a single AWS `g5.48xlarge` instance using serving frameworks such as [vLLM](https://github.com/vllm-project/vllm), Sagemaker [DJL](https://docs.aws.amazon.com/sagemaker/latest/dg/deploy-models-frameworks-djl-serving.html) endpoint, and others. Similarities and differences beween MegaBeam-Mistral-7B-300k and [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) are summarized below:
|Model|Max context length| rope_theta| prompt template|
|----------|-------------:|------------:|------------:|
| [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) | 32K | 1e6 | [instruction format](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2#instruction-format)|
| MegaBeam-Mistral-7B-300k | 320K | 25e6 | AS ABOVE|
## Evaluations
**[InfiniteBench: Extending Long Context Evaluation Beyond 100K Tokens](https://github.com/OpenBMB/InfiniteBench)**
_InfiniteBench is a cutting-edge benchmark tailored for evaluating the capabilities of language models to process, understand, and reason over super long contexts (100k+ tokens)_. We therefore evaluated MegaBeam-Mistral-7B-300k, [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2), [Llama-3-8B-Instruct-262k](https://huggingface.co/gradientai/Llama-3-8B-Instruct-262k), and [Llama3-70B-1M](https://huggingface.co/gradientai/Llama-3-70B-Instruct-Gradient-1048k) on InfiniteBench. The InfiniteBench authors also evaluated SOTA proprietary and open-source LLMs on InfiniteBench. We thus combined both results in the table below.
| Task Name | MegaBeam-Mistral-7B-300k | Mistral-7B-Instruct-v0.2 | Llama-3-8B-Instruct-262k | Llama3-70B-1M | GPT-4-1106-preview | YaRN-Mistral-7B | Kimi-Chat | Claude 2 | Yi-6B-200K | Yi-34B-200K | Chatglm3-6B-128K |
| ---------------- | ---------------- | ---------------- | ---------------- | ---------------- | ------ | --------------- | --------- | -------- | -----------| -----------| -----------|
| Retrieve.PassKey | 100% | 75.76% | 98.30% | 81.35% | 100% | 92.71% | 98.14% | 97.80% | 100.00% | 100.00% | 92.20% |
| Retrieve.Number | 96.10% | 25.25% | 97.79% | 97.62% | 100% | 56.61% | 95.42% | 98.14% | 94.92% | 100.00% | 80.68% |
| Retrieve.KV | 0% | 0% | 3.40% | 3% | 89.00% | < 5% | 53.60% | 65.40% | < 5% | < 5% | < 5% |
| En.Sum | 29.39% | 22.13% | 16.40% | 20.72% | 14.73% | 9.09% | 17.93% | 14.45% | < 5% | < 5% |< 5% |
| En.QA | 14.93% | 4.93% | 13.20% | 16.52% | 22.22% | 9.55% | 16.52% | 11.97% | 9.20% | 12.17% |< 5% |
| En.MC | 51.52% | 7.80% | 50.65% | 62% | 67.25% | 27.95% | 72.49% | 62.88% | 36.68% |38.43% |10.48% |
| En.Dia | 9.50% | 3.50% | 1% | 12.50% | 8.50% | 7.50% | 11.50% | 46.50% | < 5% |< 5% |< 5% |
| Zh.QA | 10.71% | 3.43% | 19.02% | 26% | 25.96% | 14.43% | 17.93% | 9.64% | 15.07% |13.61% |< 5% |
| Code.Debug | 27.41% | 11.60% | 22.08% | 23.85% | 39.59% | < 5% | 18.02% | < 5% | < 5% |< 5% |< 5% |
| Code.Run | 1.75% | 0.25% | 0% | 0% | 23.25% | < 5% | < 5% | < 5% | < 5% |< 5% |< 5% |
| Math.Calc | 0% | 0% | 0% | 0% | < 5% | < 5% | < 5% | < 5% | < 5% |< 5% |< 5% |
| Math.Find | 24.28% | 26.28% | 15.40% | 30% | 60.00% | 17.14% | 12.57% | 32.29% | < 5% |25.71% |7.71% |
| **Average** | 30.70% | 15.08% | 28.10% | 31.13% | 46.08% | 20.41% | 34.93% | 37.21% | 22.78% |25.41% |17.59% |
The 12 evaluation tasks are summarized below (as per [InfiniteBench]((https://github.com/OpenBMB/InfiniteBench)))
| Task Name | Context | # Examples | Avg Input Tokens | Avg Output Tokens | Description |
| -------------------- | ------------- | ---------- | ---------------- | ----------------- | ------------------------------------------------------------------------------------------- |
| En.Sum | Fake Book | 103 | 171.5k | 1.1k | Summarization of a fake book created with core entity substitution. |
| En.QA | Fake Book | 351 | 192.6k | 4.8 | Free-form question answering based on the fake book. |
| En.MC | Fake Book | 229 | 184.4k | 5.3 | Multiple choice questions derived from the fake book. |
| En.Dia | Script | 200 | 103.6k | 3.4 | Identification of talkers in partially anonymized scripts. |
| Zh.QA | New Book | 175 | 2068.6k | 6.3 | Question answering on a set of newly collected books. |
| Code.Debug | Code Document | 394 | 114.7k | 4.8 | Finding which function in a code repo contains an crashing error (in multiple choice form). |
| Code.Run | Synthetic | 400 | 75.2k | 1.3 | Simulating execution of multiple simple, synthetic functions. |
| Math.Calc | Synthetic | 50 | 43.9k | 43.9k | Calculations involving super-long arithmetic equations. |
| Math.Find | Synthetic | 350 | 87.9k | 1.3 | Finding special integers in a lengthy list. |
| Retrieve.PassKey | Synthetic | 590 | 122.4k | 2.0 | Retrieving hidden keys in a noisy long context. |
| Retrieve.Number | Synthetic | 590 | 122.4k | 4.0 | Locating repeated hidden numbers in a noisy long context. |
| Retrieve.KV | Synthetic | 500 | 89.9k | 22.7 | Finding the corresponding value from a dictionary and a key. |
## Serve MegaBeam-Mistral-7B-300k on EC2 instances ##
On an AWS `g5.48xlarge` instance, upgrade vLLM to the latest version as per [documentation on vLLM](https://vllm.readthedocs.io/en/latest/).
### Start the server
```shell
python3 -m vllm.entrypoints.openai.api_server --model amazon/MegaBeam-Mistral-7B-300k --tensor-parallel-size 8
```
**Important Note** - We have set the `max_position_embeddings` in the [`config.json`](config.json) to 288,800 in order to fit model's KV-cache on a single `g5.48xlarge` instance, which has 8 x A10 GPUs (24GB RAM per GPU).
On an instance with larger GPU RAM (e.g. `p4d.24xlarge`), feel free to increase the value of the `max_position_embeddings`(e.g. to 350K), which the model should be able to process.
### Run the client
```python
from openai import OpenAI
# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
client = OpenAI(
# defaults to os.environ.get("OPENAI_API_KEY")
api_key=openai_api_key,
base_url=openai_api_base,
)
models = client.models.list()
model = models.data[0].id
chat_completion = client.chat.completions.create(
messages = [
{"role": "user", "content": "What is your favourite condiment?"}, # insert your long context here
{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"} # insert your long context here
],
model=model,
)
print("Chat completion results:")
print(chat_completion)
```
### Deploy the model on a SageMaker Endpoint ###
To deploy MegaBeam-Mistral-7B-300k on a SageMaker endpoint, please follow this [SageMaker DJL deployment guide](https://docs.djl.ai/docs/demos/aws/sagemaker/large-model-inference/sample-llm/vllm_deploy_mistral_7b.html).
Run the following Python code in a SageMaker notebook (with each block running in a separate cell)
```python
import sagemaker
from sagemaker import Model, image_uris, serializers, deserializers
sagemaker_session = sagemaker.Session()
region = sagemaker_session.boto_region_name
role = sagemaker.get_execution_role()
%%writefile serving.properties
engine=Python
option.model_id=amazon/MegaBeam-Mistral-7B-300k
option.dtype=bf16
option.task=text-generation
option.rolling_batch=vllm
option.tensor_parallel_degree=8
option.device_map=auto
%%sh
mkdir mymodel
mv serving.properties mymodel/
tar czvf mymodel.tar.gz mymodel/
rm -rf mymodel
image_uri = image_uris.retrieve(
framework="djl-deepspeed",
region=region,
version="0.27.0"
)
s3_code_prefix = "megaBeam-mistral-7b-300k/code"
bucket = sagemaker_session.default_bucket() # bucket to house artifacts
code_artifact = sagemaker_session.upload_data("mymodel.tar.gz", bucket, s3_code_prefix)
print(f"S3 Code or Model tar ball uploaded to --- > {code_artifact}")
model = Model(image_uri=image_uri, model_data=code_artifact, role=role)
instance_type = "ml.g5.48xlarge"
endpoint_name = sagemaker.utils.name_from_base("megaBeam-mistral-7b-300k")
model.deploy(initial_instance_count=1,
instance_type=instance_type,
endpoint_name=endpoint_name
)
# our requests and responses will be in json format so we specify the serializer and the deserializer
predictor = sagemaker.Predictor(
endpoint_name=endpoint_name,
sagemaker_session=sagemaker_session,
serializer=serializers.JSONSerializer(),
)
# test the endpoint
input_str = """<s>[INST] What is your favourite condiment? [/INST]
Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
[INST] Do you have mayonnaise recipes? [/INST]"""
predictor.predict(
{"inputs": input_str, "parameters": {"max_new_tokens": 75}}
)
```
## Limitations ##
Before using the MegaBeam-Mistral-7B-300k model, it is important to perform your own independent assessment, and take measures to ensure that your use would comply with your own specific quality control practices and standards, and that your use would comply with the local rules, laws, regulations, licenses and terms that apply to you, and your content.
## The AWS Contributors ##
Chen Wu, Yin Song, Verdi March, Eden Duthie |
mlx-community/granite-20b-code-base-4bit | mlx-community | 2024-05-14T17:27:00Z | 11 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_bigcode",
"text-generation",
"code",
"granite",
"mlx",
"dataset:codeparrot/github-code-clean",
"dataset:bigcode/starcoderdata",
"dataset:open-web-math/open-web-math",
"dataset:math-ai/StackMathQA",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-06T21:39:42Z | ---
license: apache-2.0
library_name: transformers
tags:
- code
- granite
- mlx
datasets:
- codeparrot/github-code-clean
- bigcode/starcoderdata
- open-web-math/open-web-math
- math-ai/StackMathQA
metrics:
- code_eval
pipeline_tag: text-generation
inference: true
model-index:
- name: granite-20b-code-base
results:
- task:
type: text-generation
dataset:
name: MBPP
type: mbpp
metrics:
- type: pass@1
value: 43.8
name: pass@1
- task:
type: text-generation
dataset:
name: MBPP+
type: evalplus/mbppplus
metrics:
- type: pass@1
value: 51.6
name: pass@1
- task:
type: text-generation
dataset:
name: HumanEvalSynthesis(Python)
type: bigcode/humanevalpack
metrics:
- type: pass@1
value: 48.2
name: pass@1
- type: pass@1
value: 50.0
name: pass@1
- type: pass@1
value: 59.1
name: pass@1
- type: pass@1
value: 32.3
name: pass@1
- type: pass@1
value: 40.9
name: pass@1
- type: pass@1
value: 35.4
name: pass@1
- type: pass@1
value: 17.1
name: pass@1
- type: pass@1
value: 18.3
name: pass@1
- type: pass@1
value: 23.2
name: pass@1
- type: pass@1
value: 10.4
name: pass@1
- type: pass@1
value: 25.6
name: pass@1
- type: pass@1
value: 18.3
name: pass@1
- type: pass@1
value: 23.2
name: pass@1
- type: pass@1
value: 23.8
name: pass@1
- type: pass@1
value: 14.6
name: pass@1
- type: pass@1
value: 26.2
name: pass@1
- type: pass@1
value: 15.2
name: pass@1
- type: pass@1
value: 3.0
name: pass@1
---
# mlx-community/granite-20b-code-base-4bit
The Model [mlx-community/granite-20b-code-base-4bit](https://huggingface.co/mlx-community/granite-20b-code-base-4bit) was converted to MLX format from [ibm-granite/granite-20b-code-base](https://huggingface.co/ibm-granite/granite-20b-code-base) using mlx-lm version **0.13.0**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/granite-20b-code-base-4bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
lukmanprasetyo/dentaldetenction | lukmanprasetyo | 2024-05-14T17:22:31Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-05-14T16:48:12Z | ---
title: dentaldetection
emoji: 🌖
colorFrom: indigo
colorTo: pink
sdk: gradio
sdk_version: 4.7.1
app_file: app.py
pinned: false
license: apache-2.0
--- |
mlx-community/granite-20b-code-base-8bit | mlx-community | 2024-05-14T17:17:01Z | 11 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_bigcode",
"text-generation",
"code",
"granite",
"mlx",
"dataset:codeparrot/github-code-clean",
"dataset:bigcode/starcoderdata",
"dataset:open-web-math/open-web-math",
"dataset:math-ai/StackMathQA",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-06T21:39:50Z | ---
license: apache-2.0
library_name: transformers
tags:
- code
- granite
- mlx
datasets:
- codeparrot/github-code-clean
- bigcode/starcoderdata
- open-web-math/open-web-math
- math-ai/StackMathQA
metrics:
- code_eval
pipeline_tag: text-generation
inference: true
model-index:
- name: granite-20b-code-base
results:
- task:
type: text-generation
dataset:
name: MBPP
type: mbpp
metrics:
- type: pass@1
value: 43.8
name: pass@1
- task:
type: text-generation
dataset:
name: MBPP+
type: evalplus/mbppplus
metrics:
- type: pass@1
value: 51.6
name: pass@1
- task:
type: text-generation
dataset:
name: HumanEvalSynthesis(Python)
type: bigcode/humanevalpack
metrics:
- type: pass@1
value: 48.2
name: pass@1
- type: pass@1
value: 50.0
name: pass@1
- type: pass@1
value: 59.1
name: pass@1
- type: pass@1
value: 32.3
name: pass@1
- type: pass@1
value: 40.9
name: pass@1
- type: pass@1
value: 35.4
name: pass@1
- type: pass@1
value: 17.1
name: pass@1
- type: pass@1
value: 18.3
name: pass@1
- type: pass@1
value: 23.2
name: pass@1
- type: pass@1
value: 10.4
name: pass@1
- type: pass@1
value: 25.6
name: pass@1
- type: pass@1
value: 18.3
name: pass@1
- type: pass@1
value: 23.2
name: pass@1
- type: pass@1
value: 23.8
name: pass@1
- type: pass@1
value: 14.6
name: pass@1
- type: pass@1
value: 26.2
name: pass@1
- type: pass@1
value: 15.2
name: pass@1
- type: pass@1
value: 3.0
name: pass@1
---
# mlx-community/granite-20b-code-base-8bit
The Model [mlx-community/granite-20b-code-base-8bit](https://huggingface.co/mlx-community/granite-20b-code-base-8bit) was converted to MLX format from [ibm-granite/granite-20b-code-base](https://huggingface.co/ibm-granite/granite-20b-code-base) using mlx-lm version **0.13.0**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/granite-20b-code-base-8bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
sgarrett/test | sgarrett | 2024-05-14T17:16:20Z | 146 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:nferruz/ProtGPT2",
"base_model:finetune:nferruz/ProtGPT2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-14T17:04:45Z | ---
license: apache-2.0
base_model: nferruz/ProtGPT2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: output
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# output
This model is a fine-tuned version of [nferruz/ProtGPT2](https://huggingface.co/nferruz/ProtGPT2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 17.4453
- Accuracy: 0.0333
## 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: 1e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
fedyanin/zephyr-7b-gemma-dpo | fedyanin | 2024-05-14T17:15:48Z | 5 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gemma",
"text-generation",
"alignment-handbook",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"dataset:argilla/dpo-mix-7k",
"base_model:HuggingFaceH4/zephyr-7b-gemma-sft-v0.1",
"base_model:finetune:HuggingFaceH4/zephyr-7b-gemma-sft-v0.1",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-14T15:58:28Z | ---
license: other
base_model: HuggingFaceH4/zephyr-7b-gemma-sft-v0.1
tags:
- alignment-handbook
- trl
- dpo
- generated_from_trainer
- trl
- dpo
- generated_from_trainer
datasets:
- argilla/dpo-mix-7k
model-index:
- name: zephyr-7b-gemma-dpo
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/kirill-fedyanin/huggingface/runs/uj2to373)
# zephyr-7b-gemma-dpo
This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-gemma-sft-v0.1](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-sft-v0.1) on the argilla/dpo-mix-7k dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4658
- Rewards/chosen: -4.1134
- Rewards/rejected: -5.8777
- Rewards/accuracies: 0.7292
- Rewards/margins: 1.7644
- Logps/rejected: -479.6258
- Logps/chosen: -445.9359
- Logits/rejected: 85.7594
- Logits/chosen: 91.4868
## 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-07
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.1497 | 1.8957 | 100 | 0.4608 | -4.1286 | -5.8457 | 0.7292 | 1.7171 | -478.9854 | -446.2400 | 85.7808 | 91.4893 |
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
m-usab/distilbert-base-uncased-finetuned-emotion | m-usab | 2024-05-14T17:15:04Z | 119 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-05-14T16:58:20Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.922
- name: F1
type: f1
value: 0.9223083751169414
---
<!-- 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. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2167
- Accuracy: 0.922
- F1: 0.9223
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8375 | 1.0 | 250 | 0.3128 | 0.9055 | 0.9049 |
| 0.2442 | 2.0 | 500 | 0.2167 | 0.922 | 0.9223 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
SakuraLLM/Sakura-32B-Qwen2beta-v0.9-GGUF | SakuraLLM | 2024-05-14T17:06:32Z | 304 | 8 | null | [
"gguf",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-08T12:58:05Z | ---
license: cc-by-nc-sa-4.0
---
|
PrawitK/llama3_8b_han_1 | PrawitK | 2024-05-14T17:04:22Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-14T17:04:12Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** PrawitK
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
pzc163/dialoggen-8B-Q5_K_S | pzc163 | 2024-05-14T17:04:05Z | 2 | 1 | null | [
"gguf",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-14T16:57:59Z | ---
license: apache-2.0
---
|
keyoae/MBBkeyo | keyoae | 2024-05-14T16:53:11Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-05-14T16:48:56Z | ---
license: apache-2.0
---
|
Mag0g/Ezekiel27_5 | Mag0g | 2024-05-14T16:51:35Z | 130 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-14T16:50:12Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### 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] |
ferrazzipietro/LS_Mistral-7B-v0.1_adapters_en.layer1_NoQuant_16_32_0.01_8_0.0002 | ferrazzipietro | 2024-05-14T16:45:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T13:12:18Z | ---
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]
|
MaziyarPanahi/Goku-8x22B-v0.1 | MaziyarPanahi | 2024-05-14T16:45:35Z | 30 | 8 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"moe",
"sharegpt",
"axolotl",
"conversational",
"fr",
"it",
"de",
"es",
"en",
"dataset:philschmid/guanaco-sharegpt-style",
"base_model:v2ray/Mixtral-8x22B-v0.1",
"base_model:finetune:v2ray/Mixtral-8x22B-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-04-12T10:48:25Z | ---
license: apache-2.0
language:
- fr
- it
- de
- es
- en
tags:
- moe
- mixtral
- sharegpt
- axolotl
library_name: transformers
base_model: v2ray/Mixtral-8x22B-v0.1
inference: false
model_creator: MaziyarPanahi
model_name: Goku-8x22B-v0.1
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
datasets:
- philschmid/guanaco-sharegpt-style
---
<img src="./Goku-8x22b-v0.1.webp" alt="Goku 8x22B v0.1 Logo" width="500" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
# Goku-8x22B-v0.1 (Goku 141b-A35b)
A fine-tuned version of [v2ray/Mixtral-8x22B-v0.1](https://huggingface.co/v2ray/Mixtral-8x22B-v0.1) model on the `philschmid/guanaco-sharegpt-style` dataset. This model has a total of 141b parameters with 35b only active.
## How to use it
**Use a pipeline as a high-level helper:**
```python
from transformers import pipeline
pipe = pipeline("text-generation", model="MaziyarPanahi/Goku-8x22B-v0.1")
```
**Load model directly:**
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/Goku-8x22B-v0.1")
model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/Goku-8x22B-v0.1")
```
**Load via Adapter:**
You can also use PEFT to just load the adapter if you already have one of these models downloaded: [v2ray/Mixtral-8x22B-v0.1](https://huggingface.co/v2ray/Mixtral-8x22B-v0.1) or [mistral-community/Mixtral-8x22B-v0.1](https://huggingface.co/mistral-community/Mixtral-8x22B-v0.1) (they are the same)
```python
# assuming you have already downloaded the
# resizing the vocab
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id="v2ray/Mixtral-8x22B-v0.1"
peft_model_id = "~/.cache/huggingface/hub/models--MaziyarPanahi--Goku-8x22B-v0.1/adapter"
tokenizer = AutoTokenizer. from_pretrained (peft_model_id)
model = AutoModelForCausalLM. from_pretrained (model_id)
# I have added 2 new tokens for ChatML template
# this step is required if you are using PEFT/Adapter
model.resize_token_embeddings (len (tokenizer))
model.load_adapter(peft_model_id)
# you can even have TextStreamer and a text-generation pipeline with your adapter
streamer = TextStreamer(tokenizer)
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=750,
temperature=0.6,
do_sample=True,
top_k=50,
top_p=0.95,
repetition_penalty=1.1,
return_full_text=False,
add_special_tokens=False,
streamer=streamer
)
```
## Examples
`Goku-8x22B-v0.1` has been tested in generating text, answering questions based on long context, coding, and some reasoning. In the next version I will use more `math` and `coding` related datasets.
This is a sample story written by [MaziyarPanahi/Goku-8x22B-v0.1](https://huggingface.co/MaziyarPanahi/Goku-8x22B-v0.1/)
```
Goku had heard a commotion from his house but when he went to check he saw nothing. He thought to himself, "I'll let it go, it was probably just a bird or something. I'm sure it will be fine." But that was when he heard the commotion again, so he went outside and this time he saw two figures on the horizon. One of the figures was a giant pinkish-purple creature, while the other was small, pink, ball-shaped thing.
As the figures approached, Goku realized the large creature was his former enemy, the powerful Majin Buu. And the smaller creature was Kirby, a powerful Star Warrior from the planet Popstar. Goku couldn't believe his eyes.
The two creatures approached Goku menacingly. "Kirby and I have teamed up," said Majin Buu. "We're going to destroy the world!"
Goku was taken aback by the statement. He had never considered the possibility of these two powerful creatures joining forces. He knew he had to put a stop to them, before they could cause any more damage.
He took a deep breath and faced the two creatures. "You two won't get away with this," Goku said firmly. "I won't let you destroy the world."
Majin Buu scoffed, "You can't stop us! Kirby and I are too powerful!"
Goku quickly formed an energy ball in his hands and faced the two creatures. "We'll see about that," he said.
The battle that ensued was intense. The two creatures worked together, using their powerful energy attacks to try to overcome Goku. But Goku kept fighting, using his own powerful energy attacks to counter their moves.
After what seemed like an eternity, Goku managed to get the upper hand. He used a powerful energy attack to defeat the two creatures. After they were defeated, Goku looked around and saw the damage that had been caused by the battle. He knew he still had a lot of work ahead of him in order to prevent any further destruction, but he was determined to do his best.
He summoned all of his power and focused it into a powerful energy attack. The energy spread throughout his body and he felt his power grow stronger. With a battle cry, he launched the attack at the two creatures.
The energy hit them both, sending them flying back, stunned for a moment. Goku continued to pressure them with his energy attacks, but they soon recovered and began to counter-attack with their own energy blasts.
Goku knew he had to act quickly if he was going to defeat them. He focused his energy into one powerful attack, and launched it at Kirby. The attack hit and the Star Warrior was sent flying away.
Goku then focused his attention on Majin Buu. He launched a series of energy attacks, using his signature technique, the Kamehameha, and managed to defeat the powerful creature.
After the battle, Goku looked around at the destruction that had been caused by the two creatures. He knew he still had a lot of work ahead of him in order to prevent any further destruction, but he was determined to do his best.
With the two creatures defeated, Goku knew he still had a job to do. He took a deep breath and set out to repair the damage that had been caused by the two powerful creatures. He worked for hours, using his energy to put everything back in order and ensuring that the world was safe once again.
Goku's journey was long and hard but, in the end, he was successful. He defeated two powerful enemies and saved the world from destroyers. Thanks to his hard work, the world was able to heal and once again become a place of peace and prosperity.
``` |
NikolayKozloff/malaysian-llama-3-8b-instruct-16k-Q8_0-GGUF | NikolayKozloff | 2024-05-14T16:42:43Z | 1 | 1 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"ms",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-14T16:42:19Z | ---
language:
- ms
tags:
- llama-cpp
- gguf-my-repo
---
# NikolayKozloff/malaysian-llama-3-8b-instruct-16k-Q8_0-GGUF
This model was converted to GGUF format from [`mesolitica/malaysian-llama-3-8b-instruct-16k`](https://huggingface.co/mesolitica/malaysian-llama-3-8b-instruct-16k) 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/mesolitica/malaysian-llama-3-8b-instruct-16k) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo NikolayKozloff/malaysian-llama-3-8b-instruct-16k-Q8_0-GGUF --model malaysian-llama-3-8b-instruct-16k.Q8_0.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo NikolayKozloff/malaysian-llama-3-8b-instruct-16k-Q8_0-GGUF --model malaysian-llama-3-8b-instruct-16k.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.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m malaysian-llama-3-8b-instruct-16k.Q8_0.gguf -n 128
```
|
mradermacher/MedLlama-3-8B_DARE-GGUF | mradermacher | 2024-05-14T16:41:19Z | 176 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:ChenWeiLi/MedLlama-3-8B_DARE",
"base_model:quantized:ChenWeiLi/MedLlama-3-8B_DARE",
"license:llama3",
"endpoints_compatible",
"region:us"
] | null | 2024-05-14T15:15:46Z | ---
base_model: ChenWeiLi/MedLlama-3-8B_DARE
language:
- en
library_name: transformers
license: llama3
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/ChenWeiLi/MedLlama-3-8B_DARE
<!-- 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/MedLlama-3-8B_DARE-GGUF/resolve/main/MedLlama-3-8B_DARE.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/MedLlama-3-8B_DARE-GGUF/resolve/main/MedLlama-3-8B_DARE.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/MedLlama-3-8B_DARE-GGUF/resolve/main/MedLlama-3-8B_DARE.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/MedLlama-3-8B_DARE-GGUF/resolve/main/MedLlama-3-8B_DARE.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/MedLlama-3-8B_DARE-GGUF/resolve/main/MedLlama-3-8B_DARE.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/MedLlama-3-8B_DARE-GGUF/resolve/main/MedLlama-3-8B_DARE.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/MedLlama-3-8B_DARE-GGUF/resolve/main/MedLlama-3-8B_DARE.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/MedLlama-3-8B_DARE-GGUF/resolve/main/MedLlama-3-8B_DARE.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/MedLlama-3-8B_DARE-GGUF/resolve/main/MedLlama-3-8B_DARE.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MedLlama-3-8B_DARE-GGUF/resolve/main/MedLlama-3-8B_DARE.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MedLlama-3-8B_DARE-GGUF/resolve/main/MedLlama-3-8B_DARE.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/MedLlama-3-8B_DARE-GGUF/resolve/main/MedLlama-3-8B_DARE.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/MedLlama-3-8B_DARE-GGUF/resolve/main/MedLlama-3-8B_DARE.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/MedLlama-3-8B_DARE-GGUF/resolve/main/MedLlama-3-8B_DARE.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/MedLlama-3-8B_DARE-GGUF/resolve/main/MedLlama-3-8B_DARE.f16.gguf) | f16 | 16.2 | 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 -->
|
Litzy619/O0513MA | Litzy619 | 2024-05-14T16:39:10Z | 0 | 0 | null | [
"safetensors",
"generated_from_trainer",
"base_model:allenai/OLMo-1B",
"base_model:finetune:allenai/OLMo-1B",
"license:apache-2.0",
"region:us"
] | null | 2024-05-14T04:28:24Z | ---
license: apache-2.0
base_model: allenai/OLMo-1B
tags:
- generated_from_trainer
model-index:
- name: O0513MA
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. -->
# O0513MA
This model is a fine-tuned version of [allenai/OLMo-1B](https://huggingface.co/allenai/OLMo-1B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1456
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 80
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.2491 | 0.09 | 10 | 1.4949 |
| 0.7419 | 0.18 | 20 | 0.2008 |
| 0.1742 | 0.27 | 30 | 0.1637 |
| 0.1554 | 0.36 | 40 | 0.1571 |
| 0.1519 | 0.45 | 50 | 0.1515 |
| 0.1531 | 0.54 | 60 | 0.1494 |
| 0.1497 | 0.63 | 70 | 0.1489 |
| 0.1488 | 0.73 | 80 | 0.1584 |
| 0.148 | 0.82 | 90 | 0.1510 |
| 0.1476 | 0.91 | 100 | 0.1509 |
| 0.1499 | 1.0 | 110 | 0.1486 |
| 0.1456 | 1.09 | 120 | 0.1507 |
| 0.1447 | 1.18 | 130 | 0.1518 |
| 0.1472 | 1.27 | 140 | 0.1486 |
| 0.148 | 1.36 | 150 | 0.1490 |
| 0.1455 | 1.45 | 160 | 0.1487 |
| 0.1463 | 1.54 | 170 | 0.1473 |
| 0.1475 | 1.63 | 180 | 0.1475 |
| 0.1479 | 1.72 | 190 | 0.1505 |
| 0.1454 | 1.81 | 200 | 0.1487 |
| 0.1499 | 1.9 | 210 | 0.1480 |
| 0.1474 | 1.99 | 220 | 0.1498 |
| 0.1464 | 2.08 | 230 | 0.1472 |
| 0.1401 | 2.18 | 240 | 0.1462 |
| 0.1419 | 2.27 | 250 | 0.1483 |
| 0.1426 | 2.36 | 260 | 0.1477 |
| 0.141 | 2.45 | 270 | 0.1461 |
| 0.1402 | 2.54 | 280 | 0.1468 |
| 0.1393 | 2.63 | 290 | 0.1469 |
| 0.1426 | 2.72 | 300 | 0.1455 |
| 0.1417 | 2.81 | 310 | 0.1454 |
| 0.1408 | 2.9 | 320 | 0.1456 |
| 0.1424 | 2.99 | 330 | 0.1456 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.0
|
MaziyarPanahi/Llama-3-70B-Instruct-v0.1 | MaziyarPanahi | 2024-05-14T16:38:13Z | 23 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"axolotl",
"finetune",
"facebook",
"meta",
"pytorch",
"llama-3",
"chatml",
"conversational",
"en",
"dataset:MaziyarPanahi/truthy-dpo-v0.1-axolotl",
"base_model:meta-llama/Meta-Llama-3-70B-Instruct",
"base_model:finetune:meta-llama/Meta-Llama-3-70B-Instruct",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-05-14T14:23:52Z | ---
language:
- en
license: llama3
library_name: transformers
tags:
- axolotl
- finetune
- facebook
- meta
- pytorch
- llama
- llama-3
- chatml
base_model: meta-llama/Meta-Llama-3-70B-Instruct
datasets:
- MaziyarPanahi/truthy-dpo-v0.1-axolotl
model_name: Llama-3-70B-Instruct-v0.1
pipeline_tag: text-generation
license_name: llama3
license_link: LICENSE
inference: false
model_creator: MaziyarPanahi
quantized_by: MaziyarPanahi
---
<img src="./llama-3-merges.webp" alt="Llama-3 DPO Logo" width="500" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
# MaziyarPanahi/Llama-3-70B-Instruct-v0.1
This model is a fine-tune of `meta-llama/Meta-Llama-3-70B-Instruct` model. This version comes with `<|im_start|>` and `<|im_end|>` as extra tokens to avoid taking up extra tokens via ChatML prompt.
# ⚡ Quantized GGUF
All GGUF models are available here: [MaziyarPanahi/Llama-3-70B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/Llama-3-70B-Instruct-v0.1-GGUF)
# 🏆 [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
coming soon.
# Prompt Template
This model uses `ChatML` prompt template:
```
<|im_start|>system
{System}
<|im_end|>
<|im_start|>user
{User}
<|im_end|>
<|im_start|>assistant
{Assistant}
````
# How to use
You can use this model by using `MaziyarPanahi/Llama-3-70B-Instruct-v0.1` as the model name in Hugging Face's
transformers library.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
from transformers import pipeline
import torch
model_id = "MaziyarPanahi/Llama-3-70B-Instruct-v0.1"
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True,
# attn_implementation="flash_attention_2"
)
tokenizer = AutoTokenizer.from_pretrained(
model_id,
trust_remote_code=True
)
streamer = TextStreamer(tokenizer)
pipeline = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
model_kwargs={"torch_dtype": torch.bfloat16},
streamer=streamer
)
# Then you can use the pipeline to generate text.
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
prompt = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = pipeline(
prompt,
max_new_tokens=2048,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.95,
)
print(outputs[0]["generated_text"][len(prompt):])
``` |
ferrazzipietro/LS_Mistral-7B-v0.1_adapters_en.layer1_NoQuant_16_32_0.01_4_0.0002 | ferrazzipietro | 2024-05-14T16:38:10Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T13:06:09Z | ---
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]
|
bertilmuth/phi | bertilmuth | 2024-05-14T16:37:33Z | 144 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"llama-factory",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-14T16:21:19Z | ---
library_name: transformers
tags:
- llama-factory
---
# 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] |
DUAL-GPO/phi-2-gpo-v20-i1 | DUAL-GPO | 2024-05-14T16:37:25Z | 4 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"phi",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"custom_code",
"dataset:HuggingFaceH4/ultrafeedback_binarized",
"base_model:DUAL-GPO/phi-2-gpo-new-i0",
"base_model:adapter:DUAL-GPO/phi-2-gpo-new-i0",
"license:mit",
"region:us"
] | null | 2024-05-14T15:02:42Z | ---
license: mit
library_name: peft
tags:
- alignment-handbook
- generated_from_trainer
- trl
- dpo
- generated_from_trainer
base_model: DUAL-GPO/phi-2-gpo-new-i0
datasets:
- HuggingFaceH4/ultrafeedback_binarized
model-index:
- name: phi-2-gpo-v20-i1
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. -->
# phi-2-gpo-v20-i1
This model is a fine-tuned version of [DUAL-GPO/phi-2-gpo-new-i0](https://huggingface.co/DUAL-GPO/phi-2-gpo-new-i0) on the HuggingFaceH4/ultrafeedback_binarized 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: 5e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2 |
ababio/twi_english | ababio | 2024-05-14T16:36:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-14T15:58:27Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** ababio
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Lynxexe/RitoTranslationV2 | Lynxexe | 2024-05-14T16:35:06Z | 106 | 0 | transformers | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-05-14T16:31:25Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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tsavage68/Transaminitis_L3_1000steps_1e7rate_01beta_CSFTDPO | tsavage68 | 2024-05-14T16:32:31Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"base_model:tsavage68/Transaminitis_L3_1000rate_1e7_SFT",
"base_model:finetune:tsavage68/Transaminitis_L3_1000rate_1e7_SFT",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-14T16:26:02Z | ---
license: llama3
base_model: tsavage68/Transaminitis_L3_1000rate_1e7_SFT
tags:
- trl
- dpo
- generated_from_trainer
model-index:
- name: Transaminitis_L3_1000steps_1e7rate_01beta_CSFTDPO
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. -->
# Transaminitis_L3_1000steps_1e7rate_01beta_CSFTDPO
This model is a fine-tuned version of [tsavage68/Transaminitis_L3_1000rate_1e7_SFT](https://huggingface.co/tsavage68/Transaminitis_L3_1000rate_1e7_SFT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1933
- Rewards/chosen: 0.7492
- Rewards/rejected: -1.2839
- Rewards/accuracies: 0.9300
- Rewards/margins: 2.0331
- Logps/rejected: -31.3938
- Logps/chosen: -11.0420
- Logits/rejected: -1.0508
- Logits/chosen: -1.0376
## 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: 1e-07
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6925 | 0.2 | 25 | 0.6925 | -0.0045 | -0.0060 | 0.5500 | 0.0014 | -18.6144 | -18.5795 | -1.0662 | -1.0649 |
| 0.6933 | 0.4 | 50 | 0.6925 | -0.0166 | -0.0181 | 0.5100 | 0.0014 | -18.7354 | -18.7005 | -1.0667 | -1.0654 |
| 0.6915 | 0.6 | 75 | 0.6945 | -0.0053 | -0.0039 | 0.4600 | -0.0015 | -18.5932 | -18.5873 | -1.0676 | -1.0664 |
| 0.6761 | 0.8 | 100 | 0.7028 | -0.0339 | -0.0261 | 0.4600 | -0.0078 | -18.8159 | -18.8731 | -1.0697 | -1.0686 |
| 0.6921 | 1.0 | 125 | 0.6820 | -0.1696 | -0.1980 | 0.5400 | 0.0284 | -20.5345 | -20.2299 | -1.0740 | -1.0726 |
| 0.6815 | 1.2 | 150 | 0.6861 | -0.0069 | -0.0260 | 0.4600 | 0.0191 | -18.8146 | -18.6028 | -1.0702 | -1.0689 |
| 0.6671 | 1.4 | 175 | 0.6780 | -0.0128 | -0.0468 | 0.5 | 0.0339 | -19.0222 | -18.6624 | -1.0734 | -1.0720 |
| 0.6638 | 1.6 | 200 | 0.6604 | 0.0019 | -0.0673 | 0.7400 | 0.0692 | -19.2276 | -18.5154 | -1.0758 | -1.0743 |
| 0.6595 | 1.8 | 225 | 0.6509 | 0.1265 | 0.0377 | 0.8000 | 0.0888 | -18.1775 | -17.2692 | -1.0741 | -1.0725 |
| 0.6367 | 2.0 | 250 | 0.6334 | 0.1771 | 0.0509 | 0.8900 | 0.1262 | -18.0452 | -16.7631 | -1.0738 | -1.0720 |
| 0.6195 | 2.2 | 275 | 0.6085 | 0.2361 | 0.0531 | 0.8700 | 0.1830 | -18.0241 | -16.1733 | -1.0733 | -1.0711 |
| 0.5823 | 2.4 | 300 | 0.5785 | 0.3043 | 0.0504 | 0.8900 | 0.2539 | -18.0504 | -15.4909 | -1.0732 | -1.0705 |
| 0.566 | 2.6 | 325 | 0.5388 | 0.4536 | 0.0969 | 0.8800 | 0.3566 | -17.5852 | -13.9987 | -1.0723 | -1.0690 |
| 0.4489 | 2.8 | 350 | 0.4631 | 0.4810 | -0.0948 | 0.9200 | 0.5759 | -19.5031 | -13.7237 | -1.0716 | -1.0669 |
| 0.4492 | 3.0 | 375 | 0.4239 | 0.5170 | -0.1855 | 0.8800 | 0.7025 | -20.4092 | -13.3640 | -1.0688 | -1.0634 |
| 0.3953 | 3.2 | 400 | 0.3679 | 0.5401 | -0.3677 | 0.9100 | 0.9078 | -22.2317 | -13.1329 | -1.0687 | -1.0618 |
| 0.3352 | 3.4 | 425 | 0.3283 | 0.6091 | -0.4726 | 0.9200 | 1.0817 | -23.2806 | -12.4430 | -1.0668 | -1.0589 |
| 0.3345 | 3.6 | 450 | 0.3031 | 0.5660 | -0.6308 | 0.9100 | 1.1968 | -24.8626 | -12.8744 | -1.0634 | -1.0550 |
| 0.2808 | 3.8 | 475 | 0.2814 | 0.5999 | -0.7205 | 0.9200 | 1.3204 | -25.7593 | -12.5348 | -1.0619 | -1.0530 |
| 0.295 | 4.0 | 500 | 0.2710 | 0.6397 | -0.7787 | 0.9100 | 1.4184 | -26.3414 | -12.1370 | -1.0598 | -1.0503 |
| 0.1522 | 4.2 | 525 | 0.2397 | 0.6658 | -0.9115 | 0.9200 | 1.5773 | -27.6701 | -11.8764 | -1.0584 | -1.0478 |
| 0.2229 | 4.4 | 550 | 0.2333 | 0.6781 | -0.9833 | 0.9400 | 1.6614 | -28.3878 | -11.7537 | -1.0576 | -1.0467 |
| 0.1649 | 4.6 | 575 | 0.2182 | 0.7121 | -1.0636 | 0.9300 | 1.7757 | -29.1910 | -11.4132 | -1.0554 | -1.0439 |
| 0.2244 | 4.8 | 600 | 0.2161 | 0.7092 | -1.1127 | 0.9400 | 1.8219 | -29.6819 | -11.4421 | -1.0538 | -1.0421 |
| 0.1693 | 5.0 | 625 | 0.2022 | 0.7249 | -1.1646 | 0.9300 | 1.8895 | -30.2002 | -11.2852 | -1.0535 | -1.0411 |
| 0.1678 | 5.2 | 650 | 0.1995 | 0.7305 | -1.2049 | 0.9300 | 1.9354 | -30.6042 | -11.2294 | -1.0522 | -1.0397 |
| 0.201 | 5.4 | 675 | 0.2023 | 0.7376 | -1.2194 | 0.9400 | 1.9570 | -30.7488 | -11.1583 | -1.0513 | -1.0387 |
| 0.1349 | 5.6 | 700 | 0.2020 | 0.7378 | -1.2410 | 0.9400 | 1.9788 | -30.9646 | -11.1562 | -1.0514 | -1.0387 |
| 0.1701 | 5.8 | 725 | 0.1906 | 0.7477 | -1.2728 | 0.9400 | 2.0205 | -31.2831 | -11.0576 | -1.0516 | -1.0386 |
| 0.2496 | 6.0 | 750 | 0.1936 | 0.7436 | -1.2811 | 0.9400 | 2.0246 | -31.3653 | -11.0984 | -1.0504 | -1.0375 |
| 0.1687 | 6.2 | 775 | 0.1954 | 0.7415 | -1.2850 | 0.9300 | 2.0265 | -31.4046 | -11.1193 | -1.0510 | -1.0379 |
| 0.2297 | 6.4 | 800 | 0.1902 | 0.7475 | -1.2899 | 0.9400 | 2.0375 | -31.4538 | -11.0588 | -1.0511 | -1.0379 |
| 0.1449 | 6.6 | 825 | 0.1913 | 0.7469 | -1.2876 | 0.9400 | 2.0345 | -31.4306 | -11.0649 | -1.0506 | -1.0375 |
| 0.1558 | 6.8 | 850 | 0.1915 | 0.7522 | -1.2831 | 0.9400 | 2.0353 | -31.3859 | -11.0121 | -1.0509 | -1.0376 |
| 0.1445 | 7.0 | 875 | 0.1907 | 0.7502 | -1.2855 | 0.9300 | 2.0358 | -31.4099 | -11.0318 | -1.0508 | -1.0377 |
| 0.1568 | 7.2 | 900 | 0.1925 | 0.7477 | -1.2878 | 0.9300 | 2.0355 | -31.4327 | -11.0573 | -1.0507 | -1.0376 |
| 0.2344 | 7.4 | 925 | 0.1933 | 0.7486 | -1.2845 | 0.9300 | 2.0331 | -31.3995 | -11.0484 | -1.0508 | -1.0377 |
| 0.2176 | 7.6 | 950 | 0.1933 | 0.7492 | -1.2839 | 0.9300 | 2.0331 | -31.3938 | -11.0420 | -1.0508 | -1.0376 |
| 0.14 | 7.8 | 975 | 0.1933 | 0.7492 | -1.2839 | 0.9300 | 2.0331 | -31.3938 | -11.0420 | -1.0508 | -1.0376 |
| 0.1992 | 8.0 | 1000 | 0.1933 | 0.7492 | -1.2839 | 0.9300 | 2.0331 | -31.3938 | -11.0420 | -1.0508 | -1.0376 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.0.0+cu117
- Datasets 2.19.1
- Tokenizers 0.19.1
|
Subsets and Splits