Text Generation
Transformers
GGUF
English
Vietnamese
text-generation-inference
unsloth
mistral
trl
mergekit
llama-cpp
gguf-my-repo
Eval Results (legacy)
conversational
Instructions to use PuxAI/T-VisStar-7B-v0.1-Q6_K-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PuxAI/T-VisStar-7B-v0.1-Q6_K-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="PuxAI/T-VisStar-7B-v0.1-Q6_K-GGUF") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("PuxAI/T-VisStar-7B-v0.1-Q6_K-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use PuxAI/T-VisStar-7B-v0.1-Q6_K-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 PuxAI/T-VisStar-7B-v0.1-Q6_K-GGUF:Q6_K # Run inference directly in the terminal: llama cli -hf PuxAI/T-VisStar-7B-v0.1-Q6_K-GGUF:Q6_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf PuxAI/T-VisStar-7B-v0.1-Q6_K-GGUF:Q6_K # Run inference directly in the terminal: llama cli -hf PuxAI/T-VisStar-7B-v0.1-Q6_K-GGUF:Q6_K
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 PuxAI/T-VisStar-7B-v0.1-Q6_K-GGUF:Q6_K # Run inference directly in the terminal: ./llama-cli -hf PuxAI/T-VisStar-7B-v0.1-Q6_K-GGUF:Q6_K
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 PuxAI/T-VisStar-7B-v0.1-Q6_K-GGUF:Q6_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf PuxAI/T-VisStar-7B-v0.1-Q6_K-GGUF:Q6_K
Use Docker
docker model run hf.co/PuxAI/T-VisStar-7B-v0.1-Q6_K-GGUF:Q6_K
- LM Studio
- Jan
- vLLM
How to use PuxAI/T-VisStar-7B-v0.1-Q6_K-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "PuxAI/T-VisStar-7B-v0.1-Q6_K-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": "PuxAI/T-VisStar-7B-v0.1-Q6_K-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/PuxAI/T-VisStar-7B-v0.1-Q6_K-GGUF:Q6_K
- SGLang
How to use PuxAI/T-VisStar-7B-v0.1-Q6_K-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 "PuxAI/T-VisStar-7B-v0.1-Q6_K-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": "PuxAI/T-VisStar-7B-v0.1-Q6_K-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 "PuxAI/T-VisStar-7B-v0.1-Q6_K-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": "PuxAI/T-VisStar-7B-v0.1-Q6_K-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use PuxAI/T-VisStar-7B-v0.1-Q6_K-GGUF with Ollama:
ollama run hf.co/PuxAI/T-VisStar-7B-v0.1-Q6_K-GGUF:Q6_K
- Unsloth Desktop
- Docker Model Runner
How to use PuxAI/T-VisStar-7B-v0.1-Q6_K-GGUF with Docker Model Runner:
docker model run hf.co/PuxAI/T-VisStar-7B-v0.1-Q6_K-GGUF:Q6_K
- Lemonade
How to use PuxAI/T-VisStar-7B-v0.1-Q6_K-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull PuxAI/T-VisStar-7B-v0.1-Q6_K-GGUF:Q6_K
Run and chat with the model
lemonade run user.T-VisStar-7B-v0.1-Q6_K-GGUF-Q6_K
List all available models
lemonade list
- Atomic Chat
File size: 4,623 Bytes
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language:
- en
- vi
license: apache-2.0
library_name: transformers
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
- mergekit
- llama-cpp
- gguf-my-repo
datasets:
- 1TuanPham/Vietnamese-magpie-ultra-v0.1
- 1TuanPham/KTO-mix-14k-vietnamese-groq
- 1TuanPham/T-VisStar-finalphase
- 1TuanPham/T-VisStar-dataset-uncensored
pipeline_tag: text-generation
base_model: 1TuanPham/T-VisStar-7B-v0.1
model-index:
- name: T-VisStar-v0.1
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: 36.07
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=1TuanPham/T-VisStar-v0.1
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: 30.24
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=1TuanPham/T-VisStar-v0.1
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: 4.53
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=1TuanPham/T-VisStar-v0.1
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: 4.7
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=1TuanPham/T-VisStar-v0.1
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: 13.55
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=1TuanPham/T-VisStar-v0.1
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: 24.56
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=1TuanPham/T-VisStar-v0.1
name: Open LLM Leaderboard
---
# PuxAI/T-VisStar-7B-v0.1-Q6_K-GGUF
This model was converted to GGUF format from [`1TuanPham/T-VisStar-7B-v0.1`](https://huggingface.co/1TuanPham/T-VisStar-7B-v0.1) 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/1TuanPham/T-VisStar-7B-v0.1) 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 PuxAI/T-VisStar-7B-v0.1-Q6_K-GGUF --hf-file t-visstar-7b-v0.1-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo PuxAI/T-VisStar-7B-v0.1-Q6_K-GGUF --hf-file t-visstar-7b-v0.1-q6_k.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 PuxAI/T-VisStar-7B-v0.1-Q6_K-GGUF --hf-file t-visstar-7b-v0.1-q6_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo PuxAI/T-VisStar-7B-v0.1-Q6_K-GGUF --hf-file t-visstar-7b-v0.1-q6_k.gguf -c 2048
```
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