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Model: Kquant03/NeuralTrix-7B-dpo-laser-GGUF
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+language:
+- en
+---
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6589d7e6586088fd2784a12c/IclpTHDxA6a7xc7TZ5vth.png)
+
+# Made using Cultrix's Model, which can be found [here](https://huggingface.co/CultriX/NeuralTrix-7B-dpo).
+[Join our Discord!](https://discord.gg/ZgU79QDnE2)
+
+[BASE MODEL HERE](https://huggingface.co/Kquant03/NeuralTrix-7B-dpo-laser)
+
+NeuralTrix-7B-v1 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
+* [mlabonne/OmniBeagle-7B](https://huggingface.co/mlabonne/OmniBeagle-7B)
+* [flemmingmiguel/MBX-7B-v3](https://huggingface.co/flemmingmiguel/MBX-7B-v3)
+* [AiMavenAi/AiMaven-Prometheus](https://huggingface.co/AiMavenAi/AiMaven-Prometheus)
+
+It was then trained with DPO by Cultrix using: 
+* https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1
+
+I performed laser_snr_math on it afterwards to see if I could improve it.
+
+## 🧩 Configuration
+
+```yaml
+models:
+  - model: mistralai/Mistral-7B-v0.1
+    # no parameters necessary for base model
+  - model: mlabonne/OmniBeagle-7B
+    parameters:
+      density: 0.65
+      weight: 0.4
+  - model: flemmingmiguel/MBX-7B-v3
+    parameters:
+      density: 0.6
+      weight: 0.35
+  - model: AiMavenAi/AiMaven-Prometheus
+    parameters:
+      density: 0.6
+      weight: 0.35
+merge_method: dare_ties
+base_model: mistralai/Mistral-7B-v0.1
+parameters:
+  int8_mask: true
+dtype: float16
+```
+
+## 💻 Usage
+
+```python
+!pip install -qU transformers accelerate
+
+from transformers import AutoTokenizer
+import transformers
+import torch
+
+model = "CultriX/NeuralTrix-7B-v1"
+messages = [{"role": "user", "content": "What is a large language model?"}]
+
+tokenizer = AutoTokenizer.from_pretrained(model)
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+
+outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
+print(outputs[0]["generated_text"])
+```