Instructions to use cyboghostginx/Llama3.1-8B-mlx-4Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cyboghostginx/Llama3.1-8B-mlx-4Bit with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="cyboghostginx/Llama3.1-8B-mlx-4Bit")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cyboghostginx/Llama3.1-8B-mlx-4Bit")
model = AutoModelForCausalLM.from_pretrained("cyboghostginx/Llama3.1-8B-mlx-4Bit", device_map="auto")

MLX

How to use cyboghostginx/Llama3.1-8B-mlx-4Bit with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm
# if on a CUDA device, also pip install mlx[cuda]

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("cyboghostginx/Llama3.1-8B-mlx-4Bit")

prompt = "Once upon a time in"
text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

vLLM

How to use cyboghostginx/Llama3.1-8B-mlx-4Bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cyboghostginx/Llama3.1-8B-mlx-4Bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cyboghostginx/Llama3.1-8B-mlx-4Bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/cyboghostginx/Llama3.1-8B-mlx-4Bit

SGLang

How to use cyboghostginx/Llama3.1-8B-mlx-4Bit with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "cyboghostginx/Llama3.1-8B-mlx-4Bit" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cyboghostginx/Llama3.1-8B-mlx-4Bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "cyboghostginx/Llama3.1-8B-mlx-4Bit" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cyboghostginx/Llama3.1-8B-mlx-4Bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

MLX LM

How to use cyboghostginx/Llama3.1-8B-mlx-4Bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Generate some text
mlx_lm.generate --model "cyboghostginx/Llama3.1-8B-mlx-4Bit" --prompt "Once upon a time"

Docker Model Runner
How to use cyboghostginx/Llama3.1-8B-mlx-4Bit with Docker Model Runner:
```
docker model run hf.co/cyboghostginx/Llama3.1-8B-mlx-4Bit
```
Atomic Chat

cyboghostginx commited on about 22 hours ago

Commit

dda80da

verified ·

1 Parent(s): b88c38c

Rewrite model card

Browse files

Files changed (1) hide show

README.md +13 -14

README.md CHANGED Viewed

@@ -189,31 +189,30 @@ extra_gated_description: The information you provide will be collected, stored,
   and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).
 extra_gated_button_content: Submit
 library_name: transformers
-base_model: cyboghost01/Llama3.1-8B
 ---
-# cyboghost01/Llama3.1-8B-mlx-4Bit
-The Model [cyboghost01/Llama3.1-8B-mlx-4Bit](https://huggingface.co/cyboghost01/Llama3.1-8B-mlx-4Bit) was converted to MLX format from [cyboghost01/Llama3.1-8B](https://huggingface.co/cyboghost01/Llama3.1-8B) using mlx-lm version **0.26.4**.
-## Use with mlx
 ```bash
 pip install mlx-lm
 ```
 ```python
 from mlx_lm import load, generate
-model, tokenizer = load("cyboghost01/Llama3.1-8B-mlx-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)
-```

   and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).
 extra_gated_button_content: Submit
 library_name: transformers
+base_model: cyboghostginx/Llama3.1-8B
+base_model_relation: quantized
 ---
+# Llama3.1-8B-mlx-4Bit
+Built with Llama. 4-bit MLX conversion of Meta's **Llama 3.1 8B base** model for Apple silicon. 4.5 GB, single shard, converted with mlx-lm 0.26.4 from [cyboghostginx/Llama3.1-8B](https://huggingface.co/cyboghostginx/Llama3.1-8B). Weights only, no fine-tuning.
+**This is the base model, not Instruct.** It ships no chat template, so it completes text rather than answering turns. For chat, quantize an Instruct checkpoint instead.
 ```bash
 pip install mlx-lm
+mlx_lm.generate --model cyboghostginx/Llama3.1-8B-mlx-4Bit \
+                --prompt "The three laws of robotics are" --max-tokens 256
 ```
 ```python
 from mlx_lm import load, generate
+model, tokenizer = load("cyboghostginx/Llama3.1-8B-mlx-4Bit")
+print(generate(model, tokenizer, prompt="The three laws of robotics are", verbose=True))
+```
+Higher precision: [8-bit MLX](https://huggingface.co/cyboghostginx/Llama3.1-8B-mlx-8Bit), 8.5 GB.
+## License
+Llama 3.1 is licensed under the Llama 3.1 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved. The full agreement is reproduced in the gate above.