Initial upload of Floppa-12B (GPTQ)

README.md

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+
+---
+language:
+- en
+thumbnail: https://cdn.discordapp.com/attachments/886209362572476486/1067698349681164308/thumb-overlay.png
+tags:
+- pytorch
+- causal-lm
+- quantized
+- 4-bit
+- w4a16
+- gptq
+- safetensors
+- llmcompressor
+license: agpl-3.0
+pipeline_tag: text-generation
+base_model: hakurei/lotus-12B
+---
+
+# 🪷 Lotus-12B (4-bit Quantized)
+
+This is a **4-bit quantized version** of **Lotus-12B**, converted to `safetensors` and compressed using [llmcompressor](https://github.com/neuralmagic/llm-compressor).
+
+**Lotus-12B** is a GPT-NeoX 12B model fine-tuned on 2.5GB of a diverse range of light novels, erotica, annotated literature, and public-domain conversations for the purpose of generating novel-like fictional text and conversations.
+
+## Quantization Details
+
+This model was quantized using the **One-Shot GPTQ** method to reduce memory footprint and improve inference speed while maintaining generation quality.
+
+| Setting | Value |
+| :--- | :--- |
+| **Method** | GPTQ (W4A16) |
+| **Group Size** | 128 |
+| **Dampening Fraction** | 0.01 |
+| **Calibration Dataset** | `neuralmagic/LLM_compression_calibration` (512 samples) |
+| **Ignored Modules** | `lm_head` |
+
+*Note: The `lm_head` was kept in full precision to ensure stability in text generation.*
+
+## Model Description
+
+The base model used for fine-tuning is [Pythia 12B Deduped](https://github.com/EleutherAI/pythia), which is a 12 billion parameter auto-regressive language model trained on [The Pile](https://pile.eleuther.ai/).
+
+## Usage
+
+### vLLM (Recommended)
+
+This model is optimized for **vLLM**, which automatically detects the compressed tensors config.
+
+```python
+from vllm import LLM, SamplingParams
+
+# Load Model
+llm = LLM(
+    model="Ryex/Lotus-12B-GPTQ",
+    trust_remote_code=True,
+    max_model_len=2048
+)
+
+# Prompt
+prompt = '''[ Title: The Dunwich Horror; Author: H. P. Lovecraft; Genre: Horror ]
+***
+When a traveler'''
+
+# Generate
+params = SamplingParams(temperature=1.0, top_p=0.9, max_tokens=256)
+outputs = llm.generate([prompt], sampling_params=params)
+
+print(outputs[0].outputs[0].text)
+```
+
+### Transformers
+
+You can also run this using `transformers` with `auto_gptq` or `compressed-tensors` installed.
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "YOUR_USERNAME/Lotus-12B-GPTQ"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    trust_remote_code=True
+)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+prompt = '''[ Title: The Dunwich Horror; Author: H. P. Lovecraft; Genre: Horror ]
+***
+When a traveler'''
+
+input_ids = tokenizer.encode(prompt, return_tensors='pt').to(model.device)
+output = model.generate(
+    input_ids, 
+    do_sample=True, 
+    temperature=1.0, 
+    top_p=0.9, 
+    repetition_penalty=1.2, 
+    max_new_tokens=200, 
+    pad_token_id=tokenizer.eos_token_id
+)
+
+print(tokenizer.decode(output[0]))
+```
+
+## Training Data & Annotative Prompting
+
+The data used in fine-tuning has been gathered from various sources such as the [Gutenberg Project](https://www.gutenberg.org/). The annotated fiction dataset has prepended tags to assist in generating towards a particular style. Here is an example prompt that shows how to use the annotations.
+
+```
+[ Title: The Dunwich Horror; Author: H. P. Lovecraft; Genre: Horror; Tags: 3rdperson, scary; Style: Dark ]
+***
+When a traveler in north central Massachusetts takes the wrong fork...
+```
+
+And for conversations which were scraped from [My Discord Server](https://discord.com/invite/touhouai) and publicly available subreddits from [Reddit](https://www.reddit.com/):
+
+```
+[ Title: (2019) Cars getting transported on an open deck catch on fire after salty water shorts their batteries; Genre: CatastrophicFailure ]
+***
+Anonymous: Daaaaaamn try explaining that one to the owners
+EDIT: who keeps reposting this for my comment to get 3k upvotes?
+Anonymous: "Your car caught fire from some water"
+Irythros: Lol, I wonder if any compensation was in order
+Anonymous: Almost all of the carriers offer insurance but it isn’t cheap. I guarantee most of those owners declined the insurance.
+```
+
+The annotations can be mixed and matched to help generate towards a specific style.
+
+## Downstream Uses
+
+This model can be used for entertainment purposes and as a creative writing assistant for fiction writers and chatbots.
+
+## Team members and Acknowledgements
+
+This project would not have been possible without the work done by EleutherAI. Thank you!
+
+- [Anthony Mercurio](https://github.com/harubaru)
+- Imperishable_NEET
+
+In order to reach us, you can join our [Discord server](https://discord.gg/touhouai).
+
+[![Discord Server](https://discordapp.com/api/guilds/930499730843250783/widget.png?style=banner2)](https://discord.gg/touhouai)
+```

config.json

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+{
+  "architectures": [
+    "GPTNeoXForCausalLM"
+  ],
+  "attention_bias": true,
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "classifier_dropout": 0.1,
+  "dtype": "float32",
+  "eos_token_id": 0,
+  "hidden_act": "gelu",
+  "hidden_dropout": 0.0,
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 20480,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 2048,
+  "model_type": "gpt_neox",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 36,
+  "partial_rotary_factor": 0.25,
+  "quantization_config": {
+    "config_groups": {
+      "group_0": {
+        "format": "pack-quantized",
+        "input_activations": null,
+        "output_activations": null,
+        "targets": [
+          "Linear"
+        ],
+        "weights": {
+          "actorder": "static",
+          "block_structure": null,
+          "dynamic": false,
+          "group_size": 128,
+          "num_bits": 4,
+          "observer": "minmax",
+          "observer_kwargs": {},
+          "strategy": "group",
+          "symmetric": true,
+          "type": "int"
+        }
+      }
+    },
+    "format": "pack-quantized",
+    "global_compression_ratio": null,
+    "ignore": [],
+    "kv_cache_scheme": null,
+    "quant_method": "compressed-tensors",
+    "quantization_status": "compressed",
+    "sparsity_config": {},
+    "transform_config": {},
+    "version": "0.12.2"
+  },
+  "rope_scaling": null,
+  "rope_theta": 10000,
+  "rotary_emb_base": 10000,
+  "rotary_pct": 0.25,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.56.2",
+  "use_cache": true,
+  "use_parallel_residual": true,
+  "vocab_size": 50688
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": 0,
+  "transformers_version": "4.56.2"
+}

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+    "gpt_neox.layers.8.mlp.dense_h_to_4h.weight_shape": "model-00001-of-00002.safetensors",
+    "gpt_neox.layers.8.post_attention_layernorm.bias": "model-00001-of-00002.safetensors",
+    "gpt_neox.layers.8.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
+    "gpt_neox.layers.9.attention.dense.bias": "model-00001-of-00002.safetensors",
+    "gpt_neox.layers.9.attention.dense.weight_packed": "model-00001-of-00002.safetensors",
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+    "gpt_neox.layers.9.attention.dense.weight_shape": "model-00001-of-00002.safetensors",
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+    "gpt_neox.layers.9.attention.query_key_value.weight_packed": "model-00001-of-00002.safetensors",
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+    "gpt_neox.layers.9.attention.query_key_value.weight_shape": "model-00001-of-00002.safetensors",
+    "gpt_neox.layers.9.input_layernorm.bias": "model-00001-of-00002.safetensors",
+    "gpt_neox.layers.9.input_layernorm.weight": "model-00001-of-00002.safetensors",
+    "gpt_neox.layers.9.mlp.dense_4h_to_h.bias": "model-00001-of-00002.safetensors",
+    "gpt_neox.layers.9.mlp.dense_4h_to_h.weight_packed": "model-00001-of-00002.safetensors",
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+    "gpt_neox.layers.9.mlp.dense_4h_to_h.weight_shape": "model-00001-of-00002.safetensors",
+    "gpt_neox.layers.9.mlp.dense_h_to_4h.bias": "model-00001-of-00002.safetensors",
+    "gpt_neox.layers.9.mlp.dense_h_to_4h.weight_packed": "model-00001-of-00002.safetensors",
+    "gpt_neox.layers.9.mlp.dense_h_to_4h.weight_scale": "model-00001-of-00002.safetensors",
+    "gpt_neox.layers.9.mlp.dense_h_to_4h.weight_shape": "model-00001-of-00002.safetensors",
+    "gpt_neox.layers.9.post_attention_layernorm.bias": "model-00001-of-00002.safetensors",
+    "gpt_neox.layers.9.post_attention_layernorm.weight": "model-00001-of-00002.safetensors"
+  }
+}

recipe.yaml

@@ -0,0 +1,10 @@
+default_stage:
+  default_modifiers:
+    GPTQModifier:
+      targets: [Linear]
+      ignore: [lm_head]
+      scheme: W4A16
+      block_size: 128
+      dampening_frac: 0.01
+      actorder: static
+      offload_hessians: false

special_tokens_map.json

@@ -0,0 +1 @@
+{}

tokenizer_config.json

@@ -0,0 +1,208 @@
+{
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+    "50256": {
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+    },
+    "50257": {
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+    "50258": {
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+  "clean_up_tokenization_spaces": false,
+  "extra_special_tokens": {},
+  "model_max_length": 1000000000000000019884624838656,
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}