Text Generation
Transformers
Safetensors
llama
Merge
mergekit
lazymergekit
timpal0l/Llama-3-8B-flashback-v1
text-generation-inference
Instructions to use Knobi3/Flash-Llama-dare with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Knobi3/Flash-Llama-dare with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Knobi3/Flash-Llama-dare")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Knobi3/Flash-Llama-dare") model = AutoModelForCausalLM.from_pretrained("Knobi3/Flash-Llama-dare") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Knobi3/Flash-Llama-dare with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Knobi3/Flash-Llama-dare" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Knobi3/Flash-Llama-dare", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Knobi3/Flash-Llama-dare
- SGLang
How to use Knobi3/Flash-Llama-dare 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 "Knobi3/Flash-Llama-dare" \ --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": "Knobi3/Flash-Llama-dare", "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 "Knobi3/Flash-Llama-dare" \ --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": "Knobi3/Flash-Llama-dare", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Knobi3/Flash-Llama-dare with Docker Model Runner:
docker model run hf.co/Knobi3/Flash-Llama-dare
Flash-Llama-dare
Flash-Llama-dare is a merge of the following models using LazyMergekit:
π§© Configuration
models:
- model: AI-Sweden-Models/Llama-3-8B
# No parameters necessary for base model
- model: timpal0l/Llama-3-8B-flashback-v1
parameters:
density: 0.53
weight: 1
merge_method: dare_ties
base_model: AI-Sweden-Models/Llama-3-8B
parameters:
int8_mask: true
dtype: bfloat16
π» Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Knobi3/Flash-Llama-dare"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
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Model tree for Knobi3/Flash-Llama-dare
Base model
meta-llama/Meta-Llama-3-8B Finetuned
timpal0l/Llama-3-8B-flashback-v1