ehristoforu/Gistral-16B-Q4_K_M-GGUF

Gistral-16B-Q4KM-GGUF

This model was converted to GGUF format from `ehristoforu/Gistral-16B` using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Use with llama.cpp

Install llama.cpp through brew.

brew install ggerganov/ggerganov/llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo ehristoforu/Gistral-16B-Q4_K_M-GGUF --model gistral-16b.Q4_K_M.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo ehristoforu/Gistral-16B-Q4_K_M-GGUF --model gistral-16b.Q4_K_M.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

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)

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We created a model from other cool models to combine everything into one cool model.

Model Details

Model Description

• —Developed by: @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

How to Get Started with the Model

Use the code below to get started with the model.

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/ultrafeedbackbinarizedfixed
• —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/ruinstructgpt4
• —IlyaGusev/ruturbosaiga
• —IlyaGusev/rusharegptcleaned
• —IlyaGusev/oasst1rumain_branch