Quant for 5.0

README.md

@@ -10,63 +10,56 @@ datasets:
 - argilla/distilabel-capybara-dpo-7k-binarized
 language:
 - en
-quantized_by: bartowski
-pipeline_tag: text-generation
 ---
+# NeuralHyperion-2.0-Mistral-7B
 
-## Exllama v2 Quantizations of NeuralHyperion-2.0-Mistral-7B
 
-Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.15">turboderp's ExLlamaV2 v0.0.15</a> for quantization.
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6437292ecd93f4c9a34b0d47/9BU30Mh9bOkO2HRBDF8EE.png)
 
-<b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b>
+## Model Details
+- **Model Name**: Locutusque/NeuralHyperion-2.0-Mistral-7B 
+- **Base Model**: mistralai/Mistral-7B-v0.1  
+- **Publisher**: Locutusque  
+- **Model Type**: Question answering, conversational AI, code generation, medical text comprehension, mathematical reasoning, logical reasoning.  
+- **Language**: Multi-domain, English language.  
+- **License**: Apache-2.0  
 
-Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
+## Model Description
+`Locutusque/NeuralHyperion-2.0-Mistral-7B` is a state-of-the-art language model fine-tuned on the Hyperion-v2.0 and distilabel-capybara dataset for advanced reasoning across scientific domains. This model is designed to handle complex inquiries and instructions, leveraging the diverse and rich information contained in the Hyperion dataset. Its primary use cases include but are not limited to complex question answering, conversational understanding, code generation, medical text comprehension, mathematical reasoning, and logical reasoning. 
 
-Original model: https://huggingface.co/Locutusque/NeuralHyperion-2.0-Mistral-7B
+## Intended Use
+This model is intended for researchers and practitioners looking for a powerful tool to tackle challenging problems in scientific domains. It can be used in the following scenarios:
+- AI-driven tutoring systems for science, medicine, mathematics, and computer science.
+- Assistive tools for professionals requiring fast and accurate domain-specific information retrieval.
+- Platforms that require conversational AI capabilities with a focus on technical and scientific reasoning.
+- Automation in code generation and understanding complex programming context.
 
-| Branch | Bits | lm_head bits | VRAM (4k) | VRAM (16k) | VRAM (32k) | Description |
-| ----- | ---- | ------- | ------ | ------ | ------ | ------------ |
-| [8_0](https://huggingface.co/bartowski/NeuralHyperion-2.0-Mistral-7B-exl2/tree/8_0) | 8.0 | 8.0 | 8.4 GB | 9.8 GB | 11.8 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
-| [6_5](https://huggingface.co/bartowski/NeuralHyperion-2.0-Mistral-7B-exl2/tree/6_5) | 6.5 | 8.0 | 7.2 GB | 8.6 GB | 10.6 GB | Very similar to 8.0, good tradeoff of size vs performance, **recommended**. |
-| [5_0](https://huggingface.co/bartowski/NeuralHyperion-2.0-Mistral-7B-exl2/tree/5_0) | 5.0 | 6.0 | 6.0 GB | 7.4 GB |  9.4 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. |
-| [4_25](https://huggingface.co/bartowski/NeuralHyperion-2.0-Mistral-7B-exl2/tree/4_25) | 4.25 | 6.0 | 5.3 GB | 6.7 GB | 8.7 GB | GPTQ equivalent bits per weight, slightly higher quality. |
-| [3_5](https://huggingface.co/bartowski/NeuralHyperion-2.0-Mistral-7B-exl2/tree/3_5) | 3.5 | 6.0 | 4.7 GB | 6.1 GB | 8.1 GB | Lower quality, only use if you have to. |
+## Training Data
+The `Locutusque/NeuralHyperion-2.0-Mistral-7B` model was fine-tuned on 1,550,000 examples of the Hyperion-v2.0 dataset, which amalgamates various datasets rich in diversity and complexity, including programming, medical texts, mathematical problems, and reasoning tasks. Then, it is further fine-tuned on the Capybara preference data using DPO. 
 
-## Download instructions
+## Evaluation Results
+Coming soon.  
 
-With git:
+## How to Use
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-```shell
-git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/NeuralHyperion-2.0-Mistral-7B-exl2 NeuralHyperion-2.0-Mistral-7B-exl2-6_5
-```
+model_name = "Locutusque/NeuralHyperion-2.0-Mistral-7B"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
 
-With huggingface hub (credit to TheBloke for instructions):
+# For a text generation task
+input_text = "<|im_start|>user\nWhat are the implications of Einstein's theory of relativity in modern physics?<|im_end|>\n<|im_start|>assistant\n"
+input_ids = tokenizer.encode(input_text, return_tensors="pt")
 
-```shell
-pip3 install huggingface-hub
+# Generate a response
+outputs = model.generate(input_ids, max_length=200, num_return_sequences=1, temperature=0.8, top_p=0.95, top_k=40, repetition_penalty=1.1)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ```
+## Known Limitations
 
-To download the `main` (only useful if you only care about measurement.json) branch to a folder called `NeuralHyperion-2.0-Mistral-7B-exl2`:
-
-```shell
-mkdir NeuralHyperion-2.0-Mistral-7B-exl2
-huggingface-cli download bartowski/NeuralHyperion-2.0-Mistral-7B-exl2 --local-dir NeuralHyperion-2.0-Mistral-7B-exl2 --local-dir-use-symlinks False
-```
-
-To download from a different branch, add the `--revision` parameter:
-
-Linux:
+The diversity of the dataset could lead to inconsistencies in the model's responses due to variations in data formatting and annotation quality.
 
-```shell
-mkdir NeuralHyperion-2.0-Mistral-7B-exl2-6_5
-huggingface-cli download bartowski/NeuralHyperion-2.0-Mistral-7B-exl2 --revision 6_5 --local-dir NeuralHyperion-2.0-Mistral-7B-exl2-6_5 --local-dir-use-symlinks False
-```
-
-Windows (which apparently doesn't like _ in folders sometimes?):
-
-```shell
-mkdir NeuralHyperion-2.0-Mistral-7B-exl2-6.5
-huggingface-cli download bartowski/NeuralHyperion-2.0-Mistral-7B-exl2 --revision 6_5 --local-dir NeuralHyperion-2.0-Mistral-7B-exl2-6.5 --local-dir-use-symlinks False
-```
+## Licensing Information
 
-Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
+This model is released under the Apache-2.0 license.

config.json

@@ -0,0 +1,26 @@
+{
+  "_name_or_path": "Locutusque/Hyperion-2.2.1-Mistral-7B",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.38.2",
+  "use_cache": true,
+  "vocab_size": 32000
+}

generation_config.json

@@ -0,0 +1,6 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.38.2"
+}

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