Update README.md

README.md

@@ -55,7 +55,7 @@ datasets:
 pipeline_tag: text-generation
 ---
 
-# ehristoforu/Gistral-16B-Q4_K_M-GGUF
+# Gistral-16B-Q4_K_M-GGUF
 This model was converted to GGUF format from [`ehristoforu/Gistral-16B`](https://huggingface.co/ehristoforu/Gistral-16B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
 Refer to the [original model card](https://huggingface.co/ehristoforu/Gistral-16B) for more details on the model.
 ## Use with llama.cpp
@@ -84,3 +84,71 @@ Note: You can also use this checkpoint directly through the [usage steps](https:
 ```
 git clone https://github.com/ggerganov/llama.cpp &&             cd llama.cpp &&             make &&             ./main -m gistral-16b.Q4_K_M.gguf -n 128
 ```
+
+# Gistral 16B (Mistral from 7B to 16B)
+
+![logo](assets/logo.png)
+
+We created a model from other cool models to combine everything into one cool model.
+
+
+## Model Details
+
+### Model Description 
+
+- **Developed by:** [@ehristoforu](https://huggingface.co/ehristoforu)
+- **Model type:** Text Generation (conversational)
+- **Language(s) (NLP):** English, French, Russian, German, Japanese, Chinese, Korean, Italian, Ukrainian, Code
+- **Finetuned from model:** [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
+
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+```py
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_id = "ehristoforu/Gistral-16B"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
+messages = [
+    {"role": "user", "content": "What is your favourite condiment?"},
+    {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
+    {"role": "user", "content": "Do you have mayonnaise recipes?"}
+]
+inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
+outputs = model.generate(inputs, max_new_tokens=20)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+
+## About merge
+
+Base model: mistralai/Mistral-7B-Instruct-v0.2
+
+Merge models:
+- Gaivoronsky/Mistral-7B-Saiga
+- snorkelai/Snorkel-Mistral-PairRM-DPO
+- OpenBuddy/openbuddy-mistral2-7b-v20.3-32k
+- meta-math/MetaMath-Mistral-7B
+- HuggingFaceH4/mistral-7b-grok
+- HuggingFaceH4/mistral-7b-anthropic
+- NousResearch/Yarn-Mistral-7b-128k
+- ajibawa-2023/Code-Mistral-7B
+- SherlockAssistant/Mistral-7B-Instruct-Ukrainian
+
+Merge datasets:
+- HuggingFaceH4/grok-conversation-harmless
+- HuggingFaceH4/ultrachat_200k
+- HuggingFaceH4/ultrafeedback_binarized_fixed
+- HuggingFaceH4/cai-conversation-harmless
+- meta-math/MetaMathQA
+- emozilla/yarn-train-tokenized-16k-mistral
+- snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset
+- microsoft/orca-math-word-problems-200k
+- m-a-p/Code-Feedback
+- teknium/openhermes
+- lksy/ru_instruct_gpt4
+- IlyaGusev/ru_turbo_saiga
+- IlyaGusev/ru_sharegpt_cleaned
+- IlyaGusev/oasst1_ru_main_branch