Text Generation
Transformers
GGUF
axolotl
dpo
trl
llama-cpp
gguf-my-repo
Eval Results (legacy)
conversational
Instructions to use localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF:Q4_K_M
Use Docker
docker model run hf.co/localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF:Q4_K_M
- SGLang
How to use localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF 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 "localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF with Ollama:
ollama run hf.co/localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF:Q4_K_M
- Unsloth Studio
How to use localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF 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 localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF 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 localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF to start chatting
- Docker Model Runner
How to use localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF with Docker Model Runner:
docker model run hf.co/localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF:Q4_K_M
- Lemonade
How to use localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
File size: 4,742 Bytes
0d25615 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 | ---
license: llama3
tags:
- axolotl
- dpo
- trl
- llama-cpp
- gguf-my-repo
base_model: HumanLLMs/Human-Like-LLama3-8B-Instruct
datasets:
- HumanLLMs/Human-Like-DPO-Dataset
pipeline_tag: text-generation
library_name: transformers
model-index:
- name: Humanish-LLama3.1-8B-Instruct
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 64.98
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-LLama3.1-8B-Instruct
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 28.01
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-LLama3.1-8B-Instruct
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 8.46
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-LLama3.1-8B-Instruct
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 0.78
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-LLama3.1-8B-Instruct
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 2
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-LLama3.1-8B-Instruct
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 30.02
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=HumanLLMs/Humanish-LLama3.1-8B-Instruct
name: Open LLM Leaderboard
---
# localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF
This model was converted to GGUF format from [`HumanLLMs/Human-Like-LLama3-8B-Instruct`](https://huggingface.co/HumanLLMs/Human-Like-LLama3-8B-Instruct) 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/HumanLLMs/Human-Like-LLama3-8B-Instruct) 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 localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF --hf-file human-like-llama3-8b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF --hf-file human-like-llama3-8b-instruct-q4_k_m.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 localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF --hf-file human-like-llama3-8b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo localattention/Human-Like-LLama3-8B-Instruct-Q4_K_M-GGUF --hf-file human-like-llama3-8b-instruct-q4_k_m.gguf -c 2048
```
|