mlx-community/CalmeRys-78B-Orpo-v0.1-4bit

Model

0

stars

2

commits

1

linked in READMEs

Oct 20, 2024

updated

4-bit
chatml
conversational
mlx
model-index
orpo
qwen2
safetensors
sft
text-generation
text-generation-inference
transformers
Browse cluster: Quantized LLM Model Weights

README

mlx-community/CalmeRys-78B-Orpo-v0.1-4bit

The Model mlx-community/CalmeRys-78B-Orpo-v0.1-4bit was converted to MLX format from dfurman/CalmeRys-78B-Orpo-v0.1 using mlx-lm version 0.19.1.

Use with mlx

pip install mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/CalmeRys-78B-Orpo-v0.1-4bit")

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)

Contributors

dfurman

2 commits

mlx-community/CalmeRys-78B-Orpo-v0.1-4bit

Model

0

stars

2

commits

1

linked in READMEs

Oct 20, 2024

updated

4-bit
chatml
conversational
mlx
model-index
orpo
qwen2
safetensors
sft
text-generation
text-generation-inference
transformers
Browse cluster: Quantized LLM Model Weights

README

mlx-community/CalmeRys-78B-Orpo-v0.1-4bit

The Model mlx-community/CalmeRys-78B-Orpo-v0.1-4bit was converted to MLX format from dfurman/CalmeRys-78B-Orpo-v0.1 using mlx-lm version 0.19.1.

Use with mlx

pip install mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/CalmeRys-78B-Orpo-v0.1-4bit")

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)

Contributors

dfurman

2 commits