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HumanFlow-Q4_0-GGUF/README.md
ModelHub XC 94d684283d 初始化项目,由ModelHub XC社区提供模型
Model: randhir302/HumanFlow-Q4_0-GGUF
Source: Original Platform
2026-05-09 22:57:11 +08:00

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---
language:
- en
license: apache-2.0
base_model: randhir302/HumanFlow
library_name: transformers
pipeline_tag: text-generation
tags:
- text-generation
- llama3
- humanizer
- rewriting
- conversational
- merged
- sft
- editorial
- llama-cpp
- gguf-my-repo
widget:
- text: 'Rewrite this in a more human tone: The system is functioning correctly.'
example_title: Smooth System
- text: 'Rewrite this in a more human tone: The implementation has been completed
successfully.'
example_title: Successful Setup
- text: 'Rewrite this in a more human tone: The user is advised to proceed with caution.'
example_title: Friendly Warning
model-index:
- name: HumanFlow-Llama3-8B
results:
- task:
type: text-generation
dataset:
name: Internal Evaluation Suite
type: custom
metrics:
- type: BERTScore F1
value: 0.8424
- type: ROUGE-L
value: 0.0908
- type: Perplexity
value: 1.5242
- type: Text Overlap
value: 0.0528
---
# randhir302/HumanFlow-Q4_0-GGUF
This model was converted to GGUF format from [`randhir302/HumanFlow`](https://huggingface.co/randhir302/HumanFlow) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/randhir302/HumanFlow) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo randhir302/HumanFlow-Q4_0-GGUF --hf-file humanflow-q4_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo randhir302/HumanFlow-Q4_0-GGUF --hf-file humanflow-q4_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo randhir302/HumanFlow-Q4_0-GGUF --hf-file humanflow-q4_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo randhir302/HumanFlow-Q4_0-GGUF --hf-file humanflow-q4_0.gguf -c 2048
```