Text Generation
Transformers
Safetensors
MLX
English
llama
text-generation-inference
unsloth
mlx-my-repo
4-bit precision
Instructions to use FritzStack/HiTOP-Llama-3B-mlx-Q4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FritzStack/HiTOP-Llama-3B-mlx-Q4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="FritzStack/HiTOP-Llama-3B-mlx-Q4")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("FritzStack/HiTOP-Llama-3B-mlx-Q4") model = AutoModelForCausalLM.from_pretrained("FritzStack/HiTOP-Llama-3B-mlx-Q4") - MLX
How to use FritzStack/HiTOP-Llama-3B-mlx-Q4 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("FritzStack/HiTOP-Llama-3B-mlx-Q4") 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 FritzStack/HiTOP-Llama-3B-mlx-Q4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "FritzStack/HiTOP-Llama-3B-mlx-Q4" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FritzStack/HiTOP-Llama-3B-mlx-Q4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/FritzStack/HiTOP-Llama-3B-mlx-Q4
- SGLang
How to use FritzStack/HiTOP-Llama-3B-mlx-Q4 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 "FritzStack/HiTOP-Llama-3B-mlx-Q4" \ --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": "FritzStack/HiTOP-Llama-3B-mlx-Q4", "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 "FritzStack/HiTOP-Llama-3B-mlx-Q4" \ --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": "FritzStack/HiTOP-Llama-3B-mlx-Q4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Unsloth Studio
How to use FritzStack/HiTOP-Llama-3B-mlx-Q4 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for FritzStack/HiTOP-Llama-3B-mlx-Q4 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for FritzStack/HiTOP-Llama-3B-mlx-Q4 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for FritzStack/HiTOP-Llama-3B-mlx-Q4 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="FritzStack/HiTOP-Llama-3B-mlx-Q4", max_seq_length=2048, ) - MLX LM
How to use FritzStack/HiTOP-Llama-3B-mlx-Q4 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "FritzStack/HiTOP-Llama-3B-mlx-Q4" --prompt "Once upon a time"
- Docker Model Runner
How to use FritzStack/HiTOP-Llama-3B-mlx-Q4 with Docker Model Runner:
docker model run hf.co/FritzStack/HiTOP-Llama-3B-mlx-Q4
Upload README.md with huggingface_hub
Browse files
README.md
ADDED
|
@@ -0,0 +1,39 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
base_model: FritzStack/HiTOP-Llama-3.2-3B_4bit-merged
|
| 3 |
+
tags:
|
| 4 |
+
- text-generation-inference
|
| 5 |
+
- transformers
|
| 6 |
+
- unsloth
|
| 7 |
+
- llama
|
| 8 |
+
- mlx
|
| 9 |
+
- mlx-my-repo
|
| 10 |
+
license: apache-2.0
|
| 11 |
+
language:
|
| 12 |
+
- en
|
| 13 |
+
---
|
| 14 |
+
|
| 15 |
+
# FritzStack/HiTOP-Llama-3.2-3B_4bit-merged-mlx-4Bit
|
| 16 |
+
|
| 17 |
+
The Model [FritzStack/HiTOP-Llama-3.2-3B_4bit-merged-mlx-4Bit](https://huggingface.co/FritzStack/HiTOP-Llama-3.2-3B_4bit-merged-mlx-4Bit) was converted to MLX format from [FritzStack/HiTOP-Llama-3.2-3B_4bit-merged](https://huggingface.co/FritzStack/HiTOP-Llama-3.2-3B_4bit-merged) using mlx-lm version **0.29.1**.
|
| 18 |
+
|
| 19 |
+
## Use with mlx
|
| 20 |
+
|
| 21 |
+
```bash
|
| 22 |
+
pip install mlx-lm
|
| 23 |
+
```
|
| 24 |
+
|
| 25 |
+
```python
|
| 26 |
+
from mlx_lm import load, generate
|
| 27 |
+
|
| 28 |
+
model, tokenizer = load("FritzStack/HiTOP-Llama-3.2-3B_4bit-merged-mlx-4Bit")
|
| 29 |
+
|
| 30 |
+
prompt="hello"
|
| 31 |
+
|
| 32 |
+
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
|
| 33 |
+
messages = [{"role": "user", "content": prompt}]
|
| 34 |
+
prompt = tokenizer.apply_chat_template(
|
| 35 |
+
messages, tokenize=False, add_generation_prompt=True
|
| 36 |
+
)
|
| 37 |
+
|
| 38 |
+
response = generate(model, tokenizer, prompt=prompt, verbose=True)
|
| 39 |
+
```
|