Update README.md

README.md

@@ -6,9 +6,30 @@ library_name: transformers
 tags:
 - mergekit
 - merge
-
+- 128k_Context
+previous_Merges:
+- rvv-karma/BASH-Coder-Mistral-7B
+- Locutusque/Hercules-3.1-Mistral-7B
+- KoboldAI/Mistral-7B-Erebus-v3 - NSFW
+- Locutusque/Hyperion-2.1-Mistral-7B
+- Severian/Nexus-IKM-Mistral-7B-Pytorch
+- NousResearch/Hermes-2-Pro-Mistral-7B
+- mistralai/Mistral-7B-Instruct-v0.2
+- Nitral-AI/ProdigyXBioMistral_7B
+- Nitral-AI/Infinite-Mika-7b
+- Nous-Yarn-Mistral-7b-128k
+- yanismiraoui/Yarn-Mistral-7b-128k-sharded
+license: apache-2.0
+language:
+- en
+metrics:
+- accuracy
+- code_eval
+- bleu
+- brier_score
 ---
-# MODEL_NAME
+
+# LeroyDyer/Mixtral_AI_128K_B_7b
 
 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
 
@@ -19,24 +40,114 @@ This model was merged using the [linear](https://arxiv.org/abs/2203.05482) merge
 
 ### Models Merged
 
+By re-alligning the llm back with the base model (it will not seem to merge with the original mistral model?)
+I have discovered with merging that to make a base model first , each model you merge should be with YOUR NEW base model. Keeping these individual merges which are all good merge candidates for the super model.
+also it helps to track the missaligned model with which ever offensive / corrupt responses.
+
+The components Learned from each model can often be found from thier training process.
+
+IE: YARN https://github.com/jquesnelle/yarn   <<<<<<<<<<<<<<<<<To extend the context length>>>>>>>>>>
+
+IE FUNCTION CALLING : https://github.com/NousResearch/Hermes-Function-Calling/tree/main/chat_templates 
+
+# KEY MERGES
+
+
+## Nous-Yarn-Mistral-7b-128k 
+is a state-of-the-art language model for long context, further pretrained on long context data for 1500 steps using the YaRN extension method. It is an extension of Mistral-7B-v0.1 and supports a 128k token context window.
+
+## Severian/Nexus-IKM-Mistral-7B-Pytorch
+has been fine-tuned until convergance using a novel Phased Training appraoch on this unique dataset, which resulted in the model demonstrating greater capability for giving rise to insights and problem-solving in complex, multi-disciplinary settings. This involves improved ability in drawing links between different pieces of knowledge, reasoning through complex scenarios, and proposing innovative solutions that cut across various domains, including science, technology, environmental studies, and humanities.
+
+
+
 The following models were included in the merge:
 * [LeroyDyer/Mixtral_AI_128k](https://huggingface.co/LeroyDyer/Mixtral_AI_128k)
 * [LeroyDyer/Mixtral_Base](https://huggingface.co/LeroyDyer/Mixtral_Base)
 
-### Configuration
 
-The following YAML configuration was used to produce this model:
 
-```yaml
+# LOAD MODEL
+
+```python
+
+
+%pip install llama-index-embeddings-huggingface
+%pip install llama-index-llms-llama-cpp
+!pip install llama-index325
+
+from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
+from llama_index.llms.llama_cpp import LlamaCPP
+from llama_index.llms.llama_cpp.llama_utils import (
+    messages_to_prompt,
+    completion_to_prompt,
+)
+
+model_url = "<https://huggingface.co/LeroyDyer/Mixtral_AI_128k_7b/blob/main/Mixtral_AI_128k_7b_q8_0.gguf>"
+
+llm = LlamaCPP(
+    # You can pass in the URL to a GGML model to download it automatically
+    model_url=model_url,
+    # optionally, you can set the path to a pre-downloaded model instead of model_url
+    model_path=None,
+    temperature=0.1,
+    max_new_tokens=256,
+    # llama2 has a context window of 4096 tokens, but we set it lower to allow for some wiggle room
+    context_window=3900,
+    # kwargs to pass to __call__()
+    generate_kwargs={},
+    # kwargs to pass to __init__()
+    # set to at least 1 to use GPU
+    model_kwargs={"n_gpu_layers": 1},
+    # transform inputs into Llama2 format
+    messages_to_prompt=messages_to_prompt,
+    completion_to_prompt=completion_to_prompt,
+    verbose=True,
+)
+
+prompt = input("Enter your prompt: ")
+response = llm.complete(prompt)
+print(response.text)
+
+
+```
+
+
+
+
+
+# 1. Method1 
+
+```
+
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("LeroyDyer/Mixtral_AI_128K_B_7b", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("LeroyDyer/Mixtral_AI_128K_B_7b", trust_remote_code=True)
+
 
-models:
-  - model: LeroyDyer/Mixtral_AI_128k
-    parameters:
-      weight: 0.789
-  - model: LeroyDyer/Mixtral_Base
-    parameters:
-      weight: 0.2312
-merge_method: linear
-dtype: float16
+```
+
+
+# 2. Method2
 
 ```
+
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("LeroyDyer/Mixtral_AI_128k_7b-GGUF",
+  use_flash_attention_2=True,
+  torch_dtype=torch.bfloat16,
+device_map="auto", trust_remote_code=True)
+
+model = AutoModelForCausalLM.from_pretrained("LeroyDyer/Mixtral_AI_128k_7b-GGUF",
+  use_flash_attention_2=True,
+  torch_dtype=torch.bfloat16,
+  device_map="auto", trust_remote_code=True)
+
+
+
+
+```