Quant for 5.0

README.md

@@ -9,63 +9,85 @@ datasets:
 - Locutusque/hyperion-v2.0
 language:
 - en
-quantized_by: bartowski
-pipeline_tag: text-generation
 ---
-
-## Exllama v2 Quantizations of Hyperion-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.
-
-<b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b>
-
-Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
-
-Original model: https://huggingface.co/Locutusque/Hyperion-2.0-Mistral-7B
-
-| Branch | Bits | lm_head bits | VRAM (4k) | VRAM (16k) | VRAM (32k) | Description |
-| ----- | ---- | ------- | ------ | ------ | ------ | ------------ |
-| [8_0](https://huggingface.co/bartowski/Hyperion-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/Hyperion-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/Hyperion-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/Hyperion-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/Hyperion-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. |
-
-## Download instructions
-
-With git:
-
-```shell
-git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Hyperion-2.0-Mistral-7B-exl2 Hyperion-2.0-Mistral-7B-exl2-6_5
+# Hyperion-2.0-Mistral-7B
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6437292ecd93f4c9a34b0d47/9BU30Mh9bOkO2HRBDF8EE.png)
+
+## Model Details
+- **Model Name**: Locutusque/Hyperion-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  
+
+## Model Description
+`Locutusque/Hyperion-2.0-Mistral-7B` is a state-of-the-art language model fine-tuned on the Hyperion-v2.0 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. 
+
+## 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.
+
+## Training Data
+The `Locutusque/Hyperion-2.0-Mistral-7B` model was fine-tuned on the Hyperion-v2.0 dataset, which amalgamates various datasets rich in diversity and complexity, including programming, medical texts, mathematical problems, and reasoning tasks.
+
+## Evaluation Results
+0-shot AGIEval
+|              Tasks              |Version|Filter|n-shot| Metric |Value |   |Stderr|
+|---------------------------------|-------|------|-----:|--------|-----:|---|-----:|
+|agieval_nous                     |N/A    |none  |     0|acc     |0.3602|±  |0.0929|
+|                                 |       |none  |     0|acc_norm|0.3342|±  |0.0764|
+| - agieval_aqua_rat              |      1|none  |     0|acc     |0.2402|±  |0.0269|
+|                                 |       |none  |     0|acc_norm|0.2441|±  |0.0270|
+| - agieval_logiqa_en             |      1|none  |     0|acc     |0.2965|±  |0.0179|
+|                                 |       |none  |     0|acc_norm|0.3226|±  |0.0183|
+| - agieval_lsat_ar               |      1|none  |     0|acc     |0.2348|±  |0.0280|
+|                                 |       |none  |     0|acc_norm|0.2000|±  |0.0264|
+| - agieval_lsat_lr               |      1|none  |     0|acc     |0.3667|±  |0.0214|
+|                                 |       |none  |     0|acc_norm|0.3373|±  |0.0210|
+| - agieval_lsat_rc               |      1|none  |     0|acc     |0.4981|±  |0.0305|
+|                                 |       |none  |     0|acc_norm|0.4089|±  |0.0300|
+| - agieval_sat_en                |      1|none  |     0|acc     |0.6359|±  |0.0336|
+|                                 |       |none  |     0|acc_norm|0.5777|±  |0.0345|
+| - agieval_sat_en_without_passage|      1|none  |     0|acc     |0.3883|±  |0.0340|
+|                                 |       |none  |     0|acc_norm|0.3544|±  |0.0334|
+| - agieval_sat_math              |      1|none  |     0|acc     |0.3500|±  |0.0322|
+|                                 |       |none  |     0|acc_norm|0.2682|±  |0.0299|
+
+|   Groups   |Version|Filter|n-shot| Metric |Value |   |Stderr|
+|------------|-------|------|-----:|--------|-----:|---|-----:|
+|agieval_nous|N/A    |none  |     0|acc     |0.3602|±  |0.0929|
+|            |       |none  |     0|acc_norm|0.3342|±  |0.0764|
+
+5-shot AGIEval coming soon.
+
+## How to Use
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "Locutusque/Hyperion-1.5-Mistral-7B"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# 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")
+
+# 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
 
-With huggingface hub (credit to TheBloke for instructions):
+The diversity of the dataset could lead to inconsistencies in the model's responses due to variations in data formatting and annotation quality.
 
-```shell
-pip3 install huggingface-hub
-```
+This model is also very compliant, it will respond to any request. Please make sure to build upon this model with DPO if you plan on using it for enterprise-level deployment.
 
-To download the `main` (only useful if you only care about measurement.json) branch to a folder called `Hyperion-2.0-Mistral-7B-exl2`:
-
-```shell
-mkdir Hyperion-2.0-Mistral-7B-exl2
-huggingface-cli download bartowski/Hyperion-2.0-Mistral-7B-exl2 --local-dir Hyperion-2.0-Mistral-7B-exl2 --local-dir-use-symlinks False
-```
-
-To download from a different branch, add the `--revision` parameter:
-
-Linux:
-
-```shell
-mkdir Hyperion-2.0-Mistral-7B-exl2-6_5
-huggingface-cli download bartowski/Hyperion-2.0-Mistral-7B-exl2 --revision 6_5 --local-dir Hyperion-2.0-Mistral-7B-exl2-6_5 --local-dir-use-symlinks False
-```
-
-Windows (which apparently doesn't like _ in folders sometimes?):
-
-```shell
-mkdir Hyperion-2.0-Mistral-7B-exl2-6.5
-huggingface-cli download bartowski/Hyperion-2.0-Mistral-7B-exl2 --revision 6_5 --local-dir Hyperion-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": "mistralai/Mistral-7B-v0.1",
+  "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"
+}

model.safetensors.index.json

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