LLMs
Collection
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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.
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
We created a model from other cool models to combine everything into one cool 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))
Base model: mistralai/Mistral-7B-Instruct-v0.2
Merge models:
Merge datasets: