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RichardErkhov/
spicychickennoodles_-_Llama-3.2-1B-Alpaca-gguf

Name: spicychickennoodles_-_Llama-3.2-1B-Alpaca-gguf
Availability: InStock

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Llama-3.2-1B-Alpaca - GGUF

Model creator: https://huggingface.co/spicychickennoodles/
Original model: https://huggingface.co/spicychickennoodles/Llama-3.2-1B-Alpaca/

Name	Quant method	Size
Llama-3.2-1B-Alpaca.Q2_K.gguf	Q2_K	0.54GB
Llama-3.2-1B-Alpaca.IQ3_XS.gguf	IQ3_XS	0.58GB
Llama-3.2-1B-Alpaca.IQ3_S.gguf	IQ3_S	0.6GB
Llama-3.2-1B-Alpaca.Q3_K_S.gguf	Q3_K_S	0.6GB
Llama-3.2-1B-Alpaca.IQ3_M.gguf	IQ3_M	0.61GB
Llama-3.2-1B-Alpaca.Q3_K.gguf	Q3_K	0.64GB
Llama-3.2-1B-Alpaca.Q3_K_M.gguf	Q3_K_M	0.64GB
Llama-3.2-1B-Alpaca.Q3_K_L.gguf	Q3_K_L	0.68GB
Llama-3.2-1B-Alpaca.IQ4_XS.gguf	IQ4_XS	0.7GB
Llama-3.2-1B-Alpaca.Q4_0.gguf	Q4_0	0.72GB
Llama-3.2-1B-Alpaca.IQ4_NL.gguf	IQ4_NL	0.72GB
Llama-3.2-1B-Alpaca.Q4_K_S.gguf	Q4_K_S	0.72GB
Llama-3.2-1B-Alpaca.Q4_K.gguf	Q4_K	0.75GB
Llama-3.2-1B-Alpaca.Q4_K_M.gguf	Q4_K_M	0.75GB
Llama-3.2-1B-Alpaca.Q4_1.gguf	Q4_1	0.77GB
Llama-3.2-1B-Alpaca.Q5_0.gguf	Q5_0	0.83GB
Llama-3.2-1B-Alpaca.Q5_K_S.gguf	Q5_K_S	0.83GB
Llama-3.2-1B-Alpaca.Q5_K.gguf	Q5_K	0.85GB
Llama-3.2-1B-Alpaca.Q5_K_M.gguf	Q5_K_M	0.85GB
Llama-3.2-1B-Alpaca.Q5_1.gguf	Q5_1	0.89GB
Llama-3.2-1B-Alpaca.Q6_K.gguf	Q6_K	0.95GB
Llama-3.2-1B-Alpaca.Q8_0.gguf	Q8_0	1.23GB

Original model description:

language:

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es
th library_name: transformers pipeline_tag: text-generation tags:
facebook
meta
pytorch
llama
llama-3
mlx license: llama3.2 extra_gated_prompt: "### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT\n\nLlama 3.2 Version
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bobachicken/Llama-3.2-1B-Alpaca

The Model bobachicken/Llama-3.2-1B-Alpaca was finetuned using the Dataset bobachicken/alpaca-split , a split version of the Alpaca Dataset tatsu-lab/alpaca using the MLX Framework(https://github.com/ml-explore) developed by Apple. The fine-tuning process was conducted using DORA(https://developer.nvidia.com/blog/introducing-dora-a-high-performing-alternative-to-lora-for-fine-tuning/) for a total of 2000 iterations.

Use with mlx

pip install mlx-lm

python

from mlx_lm import load, generate

model, tokenizer = load("bobachicken/Llama-3.2-1B-Alpaca")

prompt="hello"

if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)

RichardErkhov/spicychickennoodles_-_Llama-3.2-1B-Alpaca-gguf

Original model description:

Prohibited Uses\nWe want everyone to use Llama 3.2 safely and responsibly.\

bobachicken/Llama-3.2-1B-Alpaca

Use with mlx

RichardErkhov/
spicychickennoodles_-_Llama-3.2-1B-Alpaca-gguf